@article{rhodes_davidian_lu_2024, title={Estimation of optimal treatment regimes with electronic medical record data using the residual life value estimator}, volume={2}, ISSN={["1468-4357"]}, DOI={10.1093/biostatistics/kxae002}, abstractNote={Summary}, journal={BIOSTATISTICS}, author={Rhodes, Grace and Davidian, Marie and Lu, Wenbin}, year={2024}, month={Feb} }
 @article{somers_winger_fisher_hyland_davidian_laber_miller_kelleher_vilardaga_majestic_et al._2023, title={Behavioral cancer pain intervention dosing: results of a Sequential Multiple Assignment Randomized Trial}, volume={164}, ISSN={["1872-6623"]}, DOI={10.1097/j.pain.0000000000002915}, abstractNote={Abstract}, number={9}, journal={PAIN}, author={Somers, Tamara J. J. and Winger, Joseph G. G. and Fisher, Hannah M. M. and Hyland, Kelly A. A. and Davidian, Marie and Laber, Eric B. B. and Miller, Shannon N. N. and Kelleher, Sarah A. A. and Vilardaga, Jennifer C. Plumb C. and Majestic, Catherine and et al.}, year={2023}, month={Sep}, pages={1935–1941} }
 @article{li_reed_winger_hyland_fisher_kelleher_miller_davidian_laber_keefe_et al._2023, title={Cost-Effectiveness Analysis Evaluating Delivery Strategies for Pain Coping Skills Training in Women With Breast Cancer}, volume={24}, ISSN={["1528-8447"]}, DOI={10.1016/j.jpain.2023.05.004}, abstractNote={Pain coping skills training (PCST) is efficacious in patients with cancer, but clinical access is limited. To inform implementation, as a secondary outcome, we estimated the cost-effectiveness of 8 dosing strategies of PCST evaluated in a sequential multiple assignment randomized trial among women with breast cancer and pain (N = 327). Women were randomized to initial doses and re-randomized to subsequent doses based on their initial response (ie, ≥30% pain reduction). A decision-analytic model was designed to incorporate costs and benefits associated with 8 different PCST dosing strategies. In the primary analysis, costs were limited to resources required to deliver PCST. Quality-adjusted life-years (QALYs) were modeled based on utility weights measured with the EuroQol-5 dimension 5-level at 4 assessments over 10 months. A probabilistic sensitivity analysis was performed to account for parameter uncertainty. Implementation of PCST initiated with the 5-session protocol was more costly ($693-853) than strategies initiated with the 1-session protocol ($288-496). QALYs for strategies beginning with the 5-session protocol were greater than for strategies beginning with the 1-session protocol. With the goal of implementing PCST as part of comprehensive cancer treatment and with willingness-to-pay thresholds ranging beyond $20,000 per QALY, the strategy most likely to provide the greatest number of QALYs at an acceptable cost was a 1-session PCST protocol followed by either 5 maintenance telephone calls for responders or 5 sessions of PCST for nonresponders. A PCST program with 1 initial session and subsequent dosing based on response provides good value and improved outcomes. PERSPECTIVE: This article presents the results of a cost analysis of the delivery of PCST, a nonpharmacological intervention, to women with breast cancer and pain. Results could potentially provide important cost-related information to health care providers and systems on the use of an efficacious and accessible nonmedication strategy for pain management. TRIALS REGISTRATION: ClinicalTrials.gov: NCT02791646, registered 6/2/2016.}, number={9}, journal={JOURNAL OF PAIN}, author={Li, Yanhong and Reed, Shelby D. and Winger, Joseph G. and Hyland, Kelly A. and Fisher, Hannah M. and Kelleher, Sarah A. and Miller, Shannon N. and Davidian, Marie and Laber, Eric B. and Keefe, Francis J. and et al.}, year={2023}, month={Sep}, pages={1712–1720} }
 @article{rhodes_davidian_lu_2023, title={DYNAMIC PREDICTION OF RESIDUAL LIFE WITH LONGITUDINAL COVARIATES USING LONG SHORT-TERM MEMORY NETWORKS}, volume={17}, ISSN={["1941-7330"]}, DOI={10.1214/22-AOAS1706}, abstractNote={Sepsis, a complex medical condition that involves severe infections with life-threatening organ dysfunction, is a leading cause of death worldwide. Treatment of sepsis is highly challenging. When making treatment decisions, clinicians and patients desire accurate predictions of mean residual life (MRL) that leverage all available patient information, including longitudinal biomarker data. Biomarkers are biological, clinical, and other variables reflecting disease progression that are often measured repeatedly on patients in the clinical setting. Dynamic prediction methods leverage accruing biomarker measurements to improve performance, providing updated predictions as new measurements become available. We introduce two methods for dynamic prediction of MRL using longitudinal biomarkers. in both methods, we begin by using long short-term memory networks (LSTMs) to construct encoded representations of the biomarker trajectories, referred to as "context vectors." In our first method, the LSTM-GLM, we dynamically predict MRL via a transformed MRL model that includes the context vectors as covariates. In our second method, the LSTM-NN, we dynamically predict MRL from the context vectors using a feed-forward neural network. We demonstrate the improved performance of both proposed methods relative to competing methods in simulation studies. We apply the proposed methods to dynamically predict the restricted mean residual life (RMRL) of septic patients in the intensive care unit using electronic medical record data. We demonstrate that the LSTM-GLM and the LSTM-NN are useful tools for producing individualized, real-time predictions of RMRL that can help inform the treatment decisions of septic patients.}, number={3}, journal={ANNALS OF APPLIED STATISTICS}, author={Rhodes, Grace and Davidian, Marie and Lu, Wenbin}, year={2023}, month={Sep}, pages={2039–2058} }
 @article{manschot_laber_davidian_2023, title={Interim monitoring of sequential multiple assignment randomized trials using partial information}, volume={3}, ISSN={["1541-0420"]}, DOI={10.1111/biom.13854}, abstractNote={Abstract}, journal={BIOMETRICS}, author={Manschot, Cole and Laber, Eric and Davidian, Marie}, year={2023}, month={Mar} }
 @article{johnson_lu_davidian_2022, title={A general framework for subgroup detection via one-step value difference estimation}, volume={8}, ISSN={["1541-0420"]}, DOI={10.1111/biom.13711}, abstractNote={Abstract}, journal={BIOMETRICS}, author={Johnson, Dana and Lu, Wenbin and Davidian, Marie}, year={2022}, month={Aug} }
 @article{tsiatis_davidian_2022, title={Group sequential methods for interim monitoring of randomized clinical trials with time-lagged outcome}, volume={9}, ISSN={["1097-0258"]}, DOI={10.1002/sim.9580}, abstractNote={The primary analysis in two‐arm clinical trials usually involves inference on a scalar treatment effect parameter; for example, depending on the outcome, the difference of treatment‐specific means, risk difference, risk ratio, or odds ratio. Most clinical trials are monitored for the possibility of early stopping. Because ordinarily the outcome on any given subject can be ascertained only after some time lag, at the time of an interim analysis, among the subjects already enrolled, the outcome is known for only a subset and is effectively censored for those who have not been enrolled sufficiently long for it to be observed. Typically, the interim analysis is based only on the data from subjects for whom the outcome has been ascertained. A goal of an interim analysis is to stop the trial as soon as the evidence is strong enough to do so, suggesting that the analysis ideally should make the most efficient use of all available data, thus including information on censoring as well as other baseline and time‐dependent covariates in a principled way. A general group sequential framework is proposed for clinical trials with a time‐lagged outcome. Treatment effect estimators that take account of censoring and incorporate covariate information at an interim analysis are derived using semiparametric theory and are demonstrated to lead to stronger evidence for early stopping than standard approaches. The associated test statistics are shown to have the independent increments structure, so that standard software can be used to obtain stopping boundaries.}, journal={STATISTICS IN MEDICINE}, author={Tsiatis, Anastasios A. and Davidian, Marie}, year={2022}, month={Sep} }
 @article{davidian_2022, title={Methods Based on Semiparametric Theory for Analysis in the Presence of Missing Data}, volume={9}, ISSN={["2326-831X"]}, DOI={10.1146/annurev-statistics-040120-025906}, abstractNote={ A statistical model is a class of probability distributions assumed to contain the true distribution generating the data. In parametric models, the distributions are indexed by a finite-dimensional parameter characterizing the scientific question of interest. Semiparametric models describe the distributions in terms of a finite-dimensional parameter and an infinite-dimensional component, offering more flexibility. Ordinarily, the statistical model represents distributions for the full data intended to be collected. When elements of these full data are missing, the goal is to make valid inference on the full-data-model parameter using the observed data. In a series of fundamental works, Robins, Rotnitzky, and colleagues derived the class of observed-data estimators under a semiparametric model assuming that the missingness mechanism is at random, which leads to practical, robust methodology for many familiar data-analytic challenges. This article reviews semiparametric theory and the key steps in this derivation. }, journal={ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION}, author={Davidian, Marie}, year={2022}, pages={167–196} }
 @article{cooks_duke_neil_vilaro_wilson-howard_modave_george_odedina_lok_carek_et al._2022, title={Telehealth and racial disparities in colorectal cancer screening: A pilot study of how virtual clinician characteristics influence screening intentions}, volume={6}, ISSN={["2059-8661"]}, DOI={10.1017/cts.2022.386}, abstractNote={Abstract}, number={1}, journal={JOURNAL OF CLINICAL AND TRANSLATIONAL SCIENCE}, author={Cooks, Eric J. and Duke, Kyle A. and Neil, Jordan M. and Vilaro, Melissa J. and Wilson-Howard, Danyell and Modave, Francois and George, Thomas J. and Odedina, Folakemi T. and Lok, Benjamin C. and Carek, Peter and et al.}, year={2022}, month={Apr} }
 @article{krieger_neil_duke_zalake_tavassoli_vilaro_wilson-howard_chavez_laber_davidian_et al._2021, title={A Pilot Study Examining the Efficacy of Delivering Colorectal Cancer Screening Messages via Virtual Health Assistants}, volume={61}, url={http://dx.doi.org/10.1016/j.amepre.2021.01.014}, DOI={10.1016/j.amepre.2021.01.014}, abstractNote={Patients are more likely to complete colorectal cancer screening when recommended by a race-concordant healthcare provider. Leveraging virtual healthcare assistants to deliver tailored screening interventions may promote adherence to colorectal cancer screening guidelines among diverse patient populations. The purpose of this pilot study is to determine the efficacy of the Agent Leveraging Empathy for eXams virtual healthcare assistant intervention to increase patient intentions to talk to their doctor about colorectal cancer screening. It also examines the influence of animation and race concordance on intentions to complete colorectal cancer screening.White and Black adults (N=1,363) aged 50-73 years and not adherent to colorectal cancer screening guidelines were recruited from Qualtrics Panels in 2018 to participate in a 3-arm (animated virtual healthcare assistant, static virtual healthcare assistant, attention control) message design experiment. In 2020, a probit regression model was used to identify the intervention effects.Participants assigned to the animated virtual healthcare assistant (p<0.01) reported higher intentions to talk to their doctor about colorectal cancer screening than participants assigned to the other conditions. There was a significant effect of race concordance on colorectal cancer screening intentions but only in the static virtual healthcare assistant condition (p=0.04). Participant race, age, trust in healthcare providers, health literacy, and cancer information overload were also significant predictors of colorectal cancer screening intentions.Animated virtual healthcare assistants were efficacious compared with the static virtual healthcare assistant and attention control conditions. The influence of race concordance between source and participant was inconsistent across conditions. This warrants additional investigation in future studies given the potential for virtual healthcare assistant‒assisted interventions to promote colorectal cancer screening within guidelines.}, number={2}, journal={American Journal of Preventive Medicine}, publisher={Elsevier BV}, author={Krieger, Janice L. and Neil, Jordan M. and Duke, Kyle A. and Zalake, Mohan S. and Tavassoli, Fatemeh and Vilaro, Melissa J. and Wilson-Howard, Danyell S. and Chavez, Sarah Y. and Laber, Eric B. and Davidian, Marie and et al.}, year={2021}, month={Aug}, pages={251–255} }
 @article{tsiatis_davidian_2021, title={Estimating vaccine efficacy over time after a randomized study is unblinded}, volume={8}, ISSN={["1541-0420"]}, DOI={10.1111/biom.13509}, abstractNote={Abstract}, journal={BIOMETRICS}, author={Tsiatis, Anastasios A. and Davidian, Marie}, year={2021}, month={Aug} }
 @article{tsiatis_davidian_holloway_2021, title={Estimation of the odds ratio in a proportional odds model with censored time-lagged outcome in a randomized clinical trial}, volume={12}, ISSN={["1541-0420"]}, url={https://doi.org/10.1111/biom.13603}, DOI={10.1111/biom.13603}, abstractNote={Abstract}, journal={BIOMETRICS}, author={Tsiatis, Anastasios A. and Davidian, Marie and Holloway, Shannon T.}, year={2021}, month={Dec} }
 @article{tsiatis_davidian_2021, title={Rejoinder: Estimating vaccine efficacy over time after a randomized study is unblinded}, volume={8}, ISSN={["1541-0420"]}, DOI={10.1111/biom.13539}, abstractNote={We are honored to have our work critiqued by such distinguished, internationally recognized authorities on vaccine efficacy and vaccine trials. When the first author (AAT) was appointed to the Data and SafetyMonitoring Board for the U.S. government-sponsored COVID-19 vaccine trials, we were embarrassingly unacquainted with even the basic concepts in this area, startingwith the definition of vaccine efficacy (VE), and it was to the fundamental work of these researcherswe turned to get up to speed. Responding to the points they raise has enhanced our understanding of the area and the role of ourworkwithin it.We comment on the issues raised in each discussion in turn; because all note challenges posed by viral variants, we address this point separately at the end.}, journal={BIOMETRICS}, author={Tsiatis, Anastasios A. and Davidian, Marie}, year={2021}, month={Aug} }
 @book{tsiatis_davidian_laber_holloway_2020, place={Boca Raton}, title={Dynamic Treatment Regimes: Statistical Methods for Precision Medicine}, ISBN={9781498769778}, url={https://www.taylorfrancis.com/books/mono/10.1201/9780429192692/dynamic-treatment-regimes-anastasios-tsiatis-marie-davidian-shannon-holloway-eric-labe}, DOI={10.1201/9780429192692/dynamic-treatment-regimes-anastasios-tsiatis-marie-davidian-shannon-holloway-eric-labe}, journal={CRC Press}, publisher={Chapman & Hall/CRC Press}, author={Tsiatis, A.A. and Davidian, M. and Laber, E.B. and Holloway, S.T.}, year={2020} }
 @misc{ruppert_yin_davidian_tsiatis_byrd_woyach_mandrekar_2019, title={Application of a sequential multiple assignment randomized trial (SMART) design in older patients with chronic lymphocytic leukemia}, volume={30}, ISSN={["1569-8041"]}, DOI={10.1093/annonc/mdz053}, abstractNote={BACKGROUND
Ibrutinib therapy is safe and effective in patients with chronic lymphocytic leukemia (CLL). Currently, ibrutinib is administered continuously until disease progression. Combination regimens with ibrutinib are being developed to deepen response which could allow for ibrutinib maintenance (IM) discontinuation. Among untreated older patients with CLL, clinical investigators had the following questions: (i) does ibrutinib + venetoclax + obinutuzumab (IVO) with IM have superior progression-free survival (PFS) compared with ibrutinib + obinutuzumab (IO) with IM, and (ii) does the treatment strategy of IVO + IM for patients without minimal residual disease complete response (MRD- CR) or IVO + IM discontinuation for patients with MRD- CR have superior PFS compared with IO + IM.


DESIGN
Conventional designs randomize patients to IO with IM or IVO with IM to address the first objective, or randomize patients to each treatment strategy to address the second objective. A sequential multiple assignment randomized trial (SMART) design and analysis is proposed to address both objectives.


RESULTS
A SMART design strategy is appropriate when comparing adaptive interventions, which are defined by an individual's sequence of treatment decisions and guided by intermediate outcomes, such as response to therapy. A review of common applications of SMART design strategies is provided. Specific to the SMART design previously considered for Alliance study A041702, the general structure of the SMART is presented, an approach to sample size and power calculations when comparing adaptive interventions embedded in the SMART with a time-to-event end point is fully described, and analyses plans are outlined.


CONCLUSION
SMART design strategies can be used in cancer clinical trials with adaptive interventions to identify optimal treatment strategies. Further, standard software exists to provide sample size, power calculations, and data analysis for a SMART design.}, number={4}, journal={ANNALS OF ONCOLOGY}, author={Ruppert, A. S. and Yin, J. and Davidian, M. and Tsiatis, A. A. and Byrd, J. C. and Woyach, J. A. and Mandrekar, S. J.}, year={2019}, month={Apr}, pages={542–550} }
 @article{zhang_laber_davidian_tsiatis_2018, title={Interpretable Dynamic Treatment Regimes}, volume={113}, ISSN={["1537-274X"]}, DOI={10.1080/01621459.2017.1345743}, abstractNote={ABSTRACT Precision medicine is currently a topic of great interest in clinical and intervention science.  A key component of precision medicine is that it is evidence-based, that is, data-driven, and consequently there has been tremendous interest in estimation of precision medicine strategies using observational or randomized study data. One way to formalize precision medicine is through a treatment regime, which is a sequence of decision rules, one per stage of clinical intervention, that map up-to-date patient information to a recommended treatment. An optimal treatment regime is defined as maximizing the mean of some cumulative clinical outcome if applied to a population of interest. It is well-known that even under simple generative models an optimal treatment regime can be a highly nonlinear function of patient information. Consequently, a focal point of recent methodological research has been the development of flexible models for estimating optimal treatment regimes. However, in many settings, estimation of an optimal treatment regime is an exploratory analysis intended to generate new hypotheses for subsequent research and not to directly dictate treatment to new patients. In such settings, an estimated treatment regime that is interpretable in a domain context may be of greater value than an unintelligible treatment regime built using “black-box” estimation methods. We propose an estimator of an optimal treatment regime composed of a sequence of decision rules, each expressible as a list of “if-then” statements that can be presented as either a paragraph or as a simple flowchart that is immediately interpretable to domain experts. The discreteness of these lists precludes smooth, that is, gradient-based, methods of estimation and leads to nonstandard asymptotics. Nevertheless, we provide a computationally efficient estimation algorithm, prove consistency of the proposed estimator, and derive rates of convergence. We illustrate the proposed methods using a series of simulation examples and application to data from a sequential clinical trial on bipolar disorder. Supplementary materials for this article are available online.}, number={524}, journal={JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION}, author={Zhang, Yichi and Laber, Eric B. and Davidian, Marie and Tsiatis, Anastasios A.}, year={2018}, pages={1541–1549} }
 @article{hager_tsiatis_davidian_2018, title={Optimal two-stage dynamic treatment regimes from a classification perspective with censored survival data}, volume={74}, ISSN={["1541-0420"]}, DOI={10.1111/biom.12894}, abstractNote={Summary}, number={4}, journal={BIOMETRICS}, author={Hager, Rebecca and Tsiatis, Anastasios A. and Davidian, Marie}, year={2018}, month={Dec}, pages={1180–1192} }
 @misc{laber_rose_davidian_tsiatis_2018, title={Q-Learning}, ISBN={9781118445112}, url={http://dx.doi.org/10.1002/9781118445112.stat07998}, DOI={10.1002/9781118445112.stat07998}, abstractNote={Abstract}, journal={Wiley StatsRef: Statistics Reference Online}, publisher={John Wiley & Sons, Ltd}, author={Laber, Eric B. and Rose, Eric J. and Davidian, Marie and Tsiatis, Anastasios A.}, year={2018}, month={Feb}, pages={1–10} }
 @article{thompson_davidian_buckland_2017, title={Biometrics, JABES and the International Biometric Society}, volume={22}, ISSN={1085-7117 1537-2693}, url={http://dx.doi.org/10.1007/S13253-017-0302-9}, DOI={10.1007/S13253-017-0302-9}, number={3}, journal={Journal of Agricultural, Biological and Environmental Statistics}, publisher={Springer Nature}, author={Thompson, Elizabeth and Davidian, Marie and Buckland, Stephen}, year={2017}, month={Aug}, pages={221–223} }
 @article{laber_davidian_2017, title={Dynamic treatment regimes, past, present, and future: A conversation with experts}, volume={26}, ISSN={["1477-0334"]}, DOI={10.1177/0962280217708661}, abstractNote={ We asked three leading researchers in the area of dynamic treatment regimes to share their stories on how they became interested in this topic and their perspectives on the most important opportunities and challenges for the future. }, number={4}, journal={STATISTICAL METHODS IN MEDICAL RESEARCH}, author={Laber, Eric B. and Davidian, Marie}, year={2017}, month={Aug}, pages={1605–1610} }
 @article{kelleher_dorfman_plumb vilardaga_majestic_winger_gandhi_nunez_van denburg_shelby_reed_et al._2017, title={Optimizing delivery of a behavioral pain intervention in cancer patients using a sequential multiple assignment randomized trial SMART}, volume={57}, ISSN={1551-7144}, url={http://dx.doi.org/10.1016/J.CCT.2017.04.001}, DOI={10.1016/J.CCT.2017.04.001}, abstractNote={Pain is common in cancer patients and results in lower quality of life, depression, poor physical functioning, financial difficulty, and decreased survival time. Behavioral pain interventions are effective and nonpharmacologic. Traditional randomized controlled trials (RCT) test interventions of fixed time and dose, which poorly represent successive treatment decisions in clinical practice. We utilize a novel approach to conduct a RCT, the sequential multiple assignment randomized trial (SMART) design, to provide comparative evidence of: 1) response to differing initial doses of a pain coping skills training (PCST) intervention and 2) intervention dose sequences adjusted based on patient response. We also examine: 3) participant characteristics moderating intervention responses and 4) cost-effectiveness and practicality.Breast cancer patients (N=327) having pain (ratings≥5) are recruited and randomly assigned to: 1) PCST-Full or 2) PCST-Brief. PCST-Full consists of 5 PCST sessions. PCST-Brief consists of one 60-min PCST session. Five weeks post-randomization, participants re-rate their pain and are re-randomized, based on intervention response, to receive additional PCST sessions, maintenance calls, or no further intervention. Participants complete measures of pain intensity, interference and catastrophizing.Novel RCT designs may provide information that can be used to optimize behavioral pain interventions to be adaptive, better meet patients' needs, reduce barriers, and match with clinical practice. This is one of the first trials to use a novel design to evaluate symptom management in cancer patients and in chronic illness; if successful, it could serve as a model for future work with a wide range of chronic illnesses.}, journal={Contemporary Clinical Trials}, publisher={Elsevier BV}, author={Kelleher, Sarah A. and Dorfman, Caroline S. and Plumb Vilardaga, Jen C. and Majestic, Catherine and Winger, Joseph and Gandhi, Vicky and Nunez, Christine and Van Denburg, Alyssa and Shelby, Rebecca A. and Reed, Shelby D. and et al.}, year={2017}, month={Jun}, pages={51–57} }
 @article{davidian_ivanova_marchenko_2017, title={Special Issue of Journal of Biopharmaceutical Statistics dedicated to 2016 Trends and Innovations in Clinical Trial Statistics (TICTS) Conference}, volume={27}, ISSN={["1520-5711"]}, DOI={10.1080/10543406.2016.1273038}, abstractNote={Should the Food and Drug Administration (FDA) approve a new weight loss drug if Pr (weight loss of 5kg) > 0.95 instead of using the usual two-sided p-value < 0.05 paradigm? This was just one of the intriguing questions from the 2nd Trends and Innovations in Clinical Trial Statistics (TICTS) conference held in May of 2016 in Durham, North Carolina. Among the highlights were the keynote address on the need of efficient evidence generation system to inform decisions by Robert Califf, the inspirational talk on generating real-world evidence by David Madigan, Steve Ruberg’s talk on making what is novel today routine tomorrow, and thoughts on how to raise the new generation of statisticians by Lisa LaVange. Presenters and panelists discussed a broad range of topics including statistical challenges and opportunities for innovation, novel trial designs and their implementation, Bayesian methods and applications, quantitative evaluation of safety, big data and analytics, clinical trial optimization and visualization, use of modeling and simulations to improve decision making, and clinical trials in small population. This special issue is dedicated to the 2nd TICTS conference, which was co-organized by Quintiles, the Department of Statistics at North Carolina State University, and the North Carolina Chapter of the American Statistical Association. The issue includes articles on missing data, multiple comparisons, sample size re-estimation, Bayesian methods and applications, strategies for estimating a homogeneous treatment effect in observational studies, use of modeling and simulations to improve decision making, and methods in oncology. Oncology is well represented with papers covering methods for phase 1 dose-finding studies up to phase 2/phase 3 program optimization. Some articles are based on the actual talks presented at the conference and some are based on the topics covered at the conference but not necessarily presented. We thank the authors of the original articles for their contributions to this special issue. We believe these contributions, taken together, will provide readers with new insights and perspectives on current trends and innovations in clinical trial statistics. Additionally, we are grateful to the Journal’s Editor-in-Chief, Prof. Shein-Chung Chow, for the opportunity to organize this special issue and Ms. Victoria Chang for her extensive editorial assistance.}, number={3}, journal={JOURNAL OF BIOPHARMACEUTICAL STATISTICS}, author={Davidian, Marie and Ivanova, Anastasia and Marchenko, Olga}, year={2017}, pages={357–357} }
 @article{vock_durheim_tsuang_copeland_tsiatis_davidian_neely_lederer_palmer_2017, title={Survival benefit of lung transplantation in the modern era of lung allocation}, volume={14}, number={2}, journal={Annals of the American Thoracic Society}, author={Vock, D. M. and Durheim, M. T. and Tsuang, W. M. and Copeland, C. A. F. and Tsiatis, A. A. and Davidian, M. and Neely, M. L. and Lederer, D. J. and Palmer, S. M.}, year={2017}, pages={172–181} }
 @misc{verbeke_fieuws_molenberghs_davidian_2017, title={The analysis of multivariate longitudinal data: A review: Response to letter of M. Gebregziabher}, volume={26}, ISSN={["1477-0334"]}, DOI={10.1177/0962280214539862}, abstractNote={[Verbeke, Geert; Fieuws, Steffen; Molenberghs, Geert] Katholieke Univ Leuven, Interuniv Inst Biostat & Stat Bioinformat, B-3000 Leuven, Belgium. [Verbeke, Geert; Molenberghs, Geert] Univ Hasselt, Interuniv Inst Biostat & Stat Bioinformat, Diepenbeek, Belgium. [Davidian, Marie] North Carolina State Univ, Dept Stat, Raleigh, NC USA.}, number={1}, journal={STATISTICAL METHODS IN MEDICAL RESEARCH}, author={Verbeke, Geert and Fieuws, Steffen and Molenberghs, Geert and Davidian, Marie}, year={2017}, month={Feb}, pages={112–112} }
 @article{olby_muguet-chanoit_lim_davidian_mariani_freeman_platt_humphrey_kent_giovanella_et al._2016, title={A Placebo-Controlled, Prospective, Randomized Clinical Trial of Polyethylene Glycol and Methylprednisolone Sodium Succinate in Dogs with Intervertebral Disk Herniation}, volume={30}, ISSN={["1939-1676"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84956678975&partnerID=MN8TOARS}, DOI={10.1111/jvim.13657}, abstractNote={BackgroundAcute intervertebral disk herniation (IVDH) is a common cause of spinal cord injury in dogs and currently there is no proven medical treatment to counter secondary injury effects. Use of methylprednisolone sodium succinate (MPSS) or polyethylene glycol (PEG) as neuroprotectants is advocated but controversial because neither treatment has been tested in placebo‐controlled, randomized, blinded trials in dogs.}, number={1}, journal={JOURNAL OF VETERINARY INTERNAL MEDICINE}, author={Olby, N. J. and Muguet-Chanoit, A. C. and Lim, J. -H. and Davidian, M. and Mariani, C. L. and Freeman, A. C. and Platt, S. R. and Humphrey, J. and Kent, M. and Giovanella, C. and et al.}, year={2016}, pages={206–214} }
 @inbook{davidian_tsiatis_laber_2016, place={Boca Raton}, title={Dynamic treatment regimes}, booktitle={Cancer Clinical Trials: Current and Controversial Issues in Design and Analysis}, publisher={Chapman & Hall/CRC Press}, author={Davidian, M. and Tsiatis, A.A. and Laber, E.B.}, editor={George, S.L. and Wang, X. and Pang, H.Editors}, year={2016}, pages={409–446} }
 @article{zhang_tsiatis_davidian_zhang_laber_2016, title={Estimating optimal treatment regimes from a classification perspective (vol 1, pg 103, 2012)}, volume={5}, ISSN={["2049-1573"]}, DOI={10.1002/sta4.124}, abstractNote={StatVolume 5, Issue 1 p. 278-278 Erratum Estimating optimal treatment regimes from a classification perspective Baqun Zhang, Corresponding Author Baqun Zhang baqun.zhang@northwestern.edu Department of Preventive Medicine, Northwestern University, Chicago, IL, 60611 USAE-mail: baqun.zhang@northwestern.eduSearch for more papers by this authorAnastasios A. Tsiatis, Anastasios A. Tsiatis Department of Statistics, North Carolina State University, Raleigh, NC, 27695-8203 USASearch for more papers by this authorMarie Davidian, Marie Davidian Department of Statistics, North Carolina State University, Raleigh, NC, 27695-8203 USASearch for more papers by this authorMin Zhang, Min Zhang Department of Biotatistics, University of Michigan, Ann Arbor, MI, 48109-2029 USASearch for more papers by this authorEric Laber, Eric Laber Department of Statistics, North Carolina State University, Raleigh, NC, 27695-8203 USASearch for more papers by this author Baqun Zhang, Corresponding Author Baqun Zhang baqun.zhang@northwestern.edu Department of Preventive Medicine, Northwestern University, Chicago, IL, 60611 USAE-mail: baqun.zhang@northwestern.eduSearch for more papers by this authorAnastasios A. Tsiatis, Anastasios A. Tsiatis Department of Statistics, North Carolina State University, Raleigh, NC, 27695-8203 USASearch for more papers by this authorMarie Davidian, Marie Davidian Department of Statistics, North Carolina State University, Raleigh, NC, 27695-8203 USASearch for more papers by this authorMin Zhang, Min Zhang Department of Biotatistics, University of Michigan, Ann Arbor, MI, 48109-2029 USASearch for more papers by this authorEric Laber, Eric Laber Department of Statistics, North Carolina State University, Raleigh, NC, 27695-8203 USASearch for more papers by this author First published: 04 November 2016 https://doi.org/10.1002/sta4.124Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume5, Issue12016Pages 278-278 RelatedInformation}, number={1}, journal={STAT}, author={Zhang, Baqun and Tsiatis, Anastasios A. and Davidian, Marie and Zhang, Min and Laber, Eric}, year={2016}, pages={278–278} }
 @article{jiang_lu_song_davidian_2016, title={On estimation of optimal treatment regimes for maximizing t
-year survival probability}, volume={79}, ISSN={1369-7412}, url={http://dx.doi.org/10.1111/rssb.12201}, DOI={10.1111/rssb.12201}, abstractNote={Summary}, number={4}, journal={Journal of the Royal Statistical Society: Series B (Statistical Methodology)}, publisher={Wiley}, author={Jiang, Runchao and Lu, Wenbin and Song, Rui and Davidian, Marie}, year={2016}, month={Sep}, pages={1165–1185} }
 @misc{davidian_tsiatis_laber_2016, title={Optimal Dynamic Treatment Regimes}, ISBN={9781118445112}, url={http://dx.doi.org/10.1002/9781118445112.stat07895}, DOI={10.1002/9781118445112.stat07895}, abstractNote={Abstract}, journal={Wiley StatsRef: Statistics Reference Online}, publisher={John Wiley & Sons, Ltd}, author={Davidian, Marie and Tsiatis, Anastasios A. and Laber, Eric B.}, year={2016}, month={Nov}, pages={1–7} }
 @article{kass_caffo_davidian_meng_yu_reid_2016, title={Ten Simple Rules for Effective Statistical Practice}, volume={12}, ISSN={1553-7358}, url={http://dx.doi.org/10.1371/journal.pcbi.1004961}, DOI={10.1371/journal.pcbi.1004961}, abstractNote={Several months ago, Phil Bourne, the initiator and frequent author of the wildly successful and incredibly useful “Ten Simple Rules” series, suggested that some statisticians put together a Ten Simple Rules article related to statistics. (One of the rules for writing a PLOS Ten Simple Rules article is to be Phil Bourne [1]. In lieu of that, we hope effusive praise for Phil will suffice.) 
 
Implicit in the guidelines for writing Ten Simple Rules [1] is “know your audience.” We developed our list of rules with researchers in mind: researchers having some knowledge of statistics, possibly with one or more statisticians available in their building, or possibly with a healthy do-it-yourself attitude and a handful of statistical packages on their laptops. We drew on our experience in both collaborative research and teaching, and, it must be said, from our frustration at being asked, more than once, to “take a quick look at my student’s thesis/my grant application/my referee’s report: it needs some input on the stats, but it should be pretty straightforward.” 
 
There are some outstanding resources available that explain many of these concepts clearly and in much more detail than we have been able to do here: among our favorites are Cox and Donnelly [2], Leek [3], Peng [4], Kass et al. [5], Tukey [6], and Yu [7]. 
 
Every article on statistics requires at least one caveat. Here is ours: we refer in this article to “science” as a convenient shorthand for investigations using data to study questions of interest. This includes social science, engineering, digital humanities, finance, and so on. Statisticians are not shy about reminding administrators that statistical science has an impact on nearly every part of almost all organizations.}, number={6}, journal={PLOS Computational Biology}, publisher={Public Library of Science (PLoS)}, author={Kass, Robert E. and Caffo, Brian S. and Davidian, Marie and Meng, Xiao-Li and Yu, Bin and Reid, Nancy}, editor={Lewitter, FranEditor}, year={2016}, month={Jun}, pages={e1004961} }
 @article{zhang_tsiatis_laber_davidian_2015, title={A robust method for estimating optimal treatment regimes}, volume={71}, DOI={10.1111/biom.12229}, abstractNote={A recent article (}, journal={Biometrics}, author={Zhang, B. and Tsiatis, A.A. and Laber, E.B. and Davidian, M.}, year={2015}, pages={271–273} }
 @inbook{davidian_tsiatis_laber_2015, title={Chapter 9: Value search estimators for optimal dynamic treatment regimes}, ISBN={9781611974171 9781611974188}, url={http://dx.doi.org/10.1137/1.9781611974188.ch9}, DOI={10.1137/1.9781611974188.ch9}, abstractNote={9.1 ▪ IntroductionThe area of personalized medicine is based on the premise that a patient's individual characteristics are implicated in which treatments are likely to benefit him/her. The most popular perspective on personalized medicine emphasizes identification of subgroups of patients who share certain characteristics, most often genetic/genomic features, and who are likely to benefit from a specific treatment. This goal understandably is of considerable interest in pharmaceutical research and in a regulatory context and has centered around development of biomarkers that can be used to identify such patients and targeting of new products to biomarker-defined subgroups. This point of view can be summarized as focusing on “the right patient for the treatment.”}, booktitle={Adaptive Treatment Strategies in Practice}, publisher={Society for Industrial and Applied Mathematics}, author={Davidian, Marie and Tsiatis, Anastasios A. and Laber, Eric B.}, year={2015}, month={Dec}, pages={135–155} }
 @article{milanzi_molenberghs_alonso_kenward_tsiatis_davidian_verbeke_2015, title={Estimation After a Group Sequential Trial}, volume={7}, ISSN={["1867-1772"]}, DOI={10.1007/s12561-014-9112-6}, abstractNote={Group sequential trials are one important instance of studies for which the sample size is not fixed a priori but rather takes one of a finite set of pre-specified values, dependent on the observed data. Much work has been devoted to the inferential consequences of this design feature. Molenberghs et al. (Statistical Methods in Medical Research, 2012) and Milanzi et al. (Properties of estimators in exponential family settings with observation-based stopping rules, 2012) reviewed and extended the existing literature, focusing on a collection of seemingly disparate, but related, settings, namely completely random sample sizes, group sequential studies with deterministic and random stopping rules, incomplete data, and random cluster sizes. They showed that the ordinary sample average is a viable option for estimation following a group sequential trial, for a wide class of stopping rules and for random outcomes with a distribution in the exponential family. Their results are somewhat surprising in the sense that the sample average is not optimal, and further, there does not exist an optimal, or even, unbiased linear estimator. However, the sample average is asymptotically unbiased, both conditionally upon the observed sample size as well as marginalized over it. By exploiting ignorability they showed that the sample average is the conventional maximum likelihood estimator. They also showed that a conditional maximum likelihood estimator is finite sample unbiased, but is less efficient than the sample average and has the larger mean squared error. Asymptotically, the sample average and the conditional maximum likelihood estimator are equivalent. This previous work is restricted, however, to the situation in which the the random sample size can take only two values, $$N=n$$ or $$N=2n$$ . In this paper, we consider the more practically useful setting of sample sizes in a the finite set $$\{n_1,n_2,\dots ,n_L\}$$ . It is shown that the sample average is then a justifiable estimator , in the sense that it follows from joint likelihood estimation, and it is consistent and asymptotically unbiased. We also show why simulations can give the false impression of bias in the sample average when considered conditional upon the sample size. The consequence is that no corrections need to be made to estimators following sequential trials. When small-sample bias is of concern, the conditional likelihood estimator (CLE) provides a relatively straightforward modification to the sample average. Finally, it is shown that classical likelihood-based standard errors and confidence intervals can be applied, obviating the need for technical corrections.}, number={2}, journal={STATISTICS IN BIOSCIENCES}, author={Milanzi, Elasma and Molenberghs, Geert and Alonso, Ariel and Kenward, Michael G. and Tsiatis, Anastasios A. and Davidian, Marie and Verbeke, Geert}, year={2015}, month={Oct}, pages={187–205} }
 @article{milanzi_molenberghs_2015, title={Properties of Estimators in Exponential Family Settings with Observationbased Stopping Rules}, volume={07}, ISSN={2155-6180}, url={http://dx.doi.org/10.4172/2155-6180.1000272}, DOI={10.4172/2155-6180.1000272}, abstractNote={Often, sample size is not fixed by design. A key example is a sequential trial with a stopping rule, where stopping is based on what has been observed at an interim look. While such designs are used for time and cost efficiency, and hypothesis testing theory has been well developed, estimation following a sequential trial is a challenging, still controversial problem. Progress has been made in the literature, predominantly for normal outcomes and/or for a deterministic stopping rule. Here, we place these settings in a broader context of outcomes following an exponential family distribution and, with a stochastic stopping rule that includes a deterministic rule and completely random sample size as special cases. It is shown that the estimation problem is usually simpler than often thought. In particular, it is established that the ordinary sample average is a very sensible choice, contrary to commonly encountered statements. We study (1) The so-called incompleteness property of the sufficient statistics, (2) A general class of linear estimators, and (3) Joint and conditional likelihood estimation. Apart from the general exponential family setting, normal and binary outcomes are considered as key examples. While our results hold for a general number of looks, for ease of exposition, we focus on the simple yet generic setting of two possible sample sizes, N=n or N=2n.}, number={01}, journal={Journal of Biometrics & Biostatistics}, publisher={OMICS Publishing Group}, author={Milanzi, Elasma and Molenberghs, Geert}, year={2015} }
 @article{zhang_laber_tsiatis_davidian_2015, title={Using Decision Lists to Construct Interpretable and Parsimonious Treatment Regimes}, volume={71}, ISSN={["1541-0420"]}, DOI={10.1111/biom.12354}, abstractNote={Summary}, number={4}, journal={BIOMETRICS}, author={Zhang, Yichi and Laber, Eric B. and Tsiatis, Anastasios and Davidian, Marie}, year={2015}, month={Dec}, pages={895–904} }
 @article{laber_zhao_regh_davidian_tsiatis_stanford_zeng_song_kosorok_2015, title={Using pilot data to size a two-arm randomized trial to find a nearly optimal personalized treatment strategy}, volume={35}, ISSN={0277-6715}, url={http://dx.doi.org/10.1002/SIM.6783}, DOI={10.1002/SIM.6783}, abstractNote={A personalized treatment strategy formalizes evidence‐based treatment selection by mapping patient information to a recommended treatment. Personalized treatment strategies can produce better patient outcomes while reducing cost and treatment burden. Thus, among clinical and intervention scientists, there is a growing interest in conducting randomized clinical trials when one of the primary aims is estimation of a personalized treatment strategy. However, at present, there are no appropriate sample size formulae to assist in the design of such a trial. Furthermore, because the sampling distribution of the estimated outcome under an estimated optimal treatment strategy can be highly sensitive to small perturbations in the underlying generative model, sample size calculations based on standard (uncorrected) asymptotic approximations or computer simulations may not be reliable. We offer a simple and robust method for powering a single stage, two‐armed randomized clinical trial when the primary aim is estimating the optimal single stage personalized treatment strategy. The proposed method is based on inverting a plugin projection confidence interval and is thereby regular and robust to small perturbations of the underlying generative model. The proposed method requires elicitation of two clinically meaningful parameters from clinical scientists and uses data from a small pilot study to estimate nuisance parameters, which are not easily elicited. The method performs well in simulated experiments and is illustrated using data from a pilot study of time to conception and fertility awareness. Copyright © 2015 John Wiley & Sons, Ltd.}, number={8}, journal={Statistics in Medicine}, publisher={Wiley}, author={Laber, Eric B. and Zhao, Ying-Qi and Regh, Todd and Davidian, Marie and Tsiatis, Anastasios and Stanford, Joseph B. and Zeng, Donglin and Song, Rui and Kosorok, Michael R.}, year={2015}, month={Oct}, pages={1245–1256} }
 @misc{davidian_kutal_2014, title={Collaboration To Meet the Statistical Needs in the Chemistry Curriculum}, volume={91}, ISSN={["1938-1328"]}, DOI={10.1021/ed400516y}, abstractNote={Recent articles have recommended adding statistical topics to the chemistry curriculum. This letter to the editor recommends: (i) collaboration between statisticians and chemists; and (ii) emulating existing successful models to meet the statistical needs in the chemistry curriculum.}, number={1}, journal={JOURNAL OF CHEMICAL EDUCATION}, author={Davidian, Marie and Kutal, Charles}, year={2014}, month={Jan}, pages={12–12} }
 @article{laber_tsiatis_davidian_holloway_2014, title={Combining Biomarkers to Optimize Patient Treatment Recommendations Discussions}, volume={70}, ISSN={["1541-0420"]}, DOI={10.1111/biom.12187}, abstractNote={We congratulate the Kang, Janes, and Huang (hereafter KJH) on an interesting and powerful new method for estimating an optimal treatment rule, also referred to as an optimal treatment regime. Their proposed method relies on having a high-quality estimator for the regression of outcome on biomarkers and treatment, which the authors obtain using a novel boosting algorithm. Methods for constructing treatment rules/regimes that rely on outcome models are sometimes called indirect or regression-based methods because the treatment rule is inferred from the outcome model (Barto and Dieterich, 1988). 
 
Regression-based methods are appealing because they can be used to make prognostic predictions as well as treatment recommendations. While it is common practice to use parametric or semiparametric models in regression-based approaches (Robins, 2004; Chakraborty and Moodie, 2013; Laber et al., 2014; Schulte et al., 2014), there is growing interest in using nonparametric methods to avoid model misspecification (Zhao et al., 2011; Moodie et al., 2013). In contrast, direct estimation methods, also known as policy-search methods, try to weaken or eliminate dependence on correct outcome models and instead attempt to search for the best treatment rule within a pre-specified class of rules (Orellana, Rotnitzky, and Robins, 2010; Zhang et al., 2012a,b; Zhao et al., 2012; Zhang et al., 2013). Direct estimation methods make fewer assumptions about the outcome model, which may make them more robust to model misspecification but potentially more variable. 
 
We derive a direct estimation analog to the method of KJH, which we term value boosting. The method is based on recasting the problem of estimating an optimal treatment rule as a weighted classification problem (Zhang et al., 2012a; Zhao et al., 2012). We show how the method of KJH can be used with existing policy-search methods to construct a treatment rule that is interpretable, logistically feasible, parsimonious, or otherwise appealing.}, number={3}, journal={BIOMETRICS}, author={Laber, Eric B. and Tsiatis, Anastasios A. and Davidian, Marie and Holloway, Shannon T.}, year={2014}, month={Sep}, pages={707–710} }
 @inbook{tsiatis_davidian_2014, place={Boca Raton}, title={Missing data methods: A semiparametric perspective}, booktitle={Handbook of Missing Data}, publisher={Chapman & Hall/CRC Press}, author={Tsiatis, A.A. and Davidian, M.}, editor={Fitzmaurice, G. and Kenward, M. and Molenberghs, G. and Tsiatis, A.A. and Verbeke, G.Editors}, year={2014} }
 @article{molenberghs_kenward_aerts_verbeke_tsiatis_davidian_rizopoulos_2014, title={On random sample size, ignorability, ancillarity, completeness, separability, and degeneracy: Sequential trials, random sample sizes, and missing data}, volume={23}, ISSN={["1477-0334"]}, DOI={10.1177/0962280212445801}, abstractNote={ The vast majority of settings for which frequentist statistical properties are derived assume a fixed, a priori known sample size. Familiar properties then follow, such as, for example, the consistency, asymptotic normality, and efficiency of the sample average for the mean parameter, under a wide range of conditions. We are concerned here with the alternative situation in which the sample size is itself a random variable which may depend on the data being collected. Further, the rule governing this may be deterministic or probabilistic. There are many important practical examples of such settings, including missing data, sequential trials, and informative cluster size. It is well known that special issues can arise when evaluating the properties of statistical procedures under such sampling schemes, and much has been written about specific areas (Grambsch P. Sequential sampling based on the observed Fisher information to guarantee the accuracy of the maximum likelihood estimator. Ann Stat 1983; 11: 68–77; Barndorff-Nielsen O and Cox DR. The effect of sampling rules on likelihood statistics. Int Stat Rev 1984; 52: 309–326). Our aim is to place these various related examples into a single framework derived from the joint modeling of the outcomes and sampling process and so derive generic results that in turn provide insight, and in some cases practical consequences, for different settings. It is shown that, even in the simplest case of estimating a mean, some of the results appear counterintuitive. In many examples, the sample average may exhibit small sample bias and, even when it is unbiased, may not be optimal. Indeed, there may be no minimum variance unbiased estimator for the mean. Such results follow directly from key attributes such as non-ancillarity of the sample size and incompleteness of the minimal sufficient statistic of the sample size and sample sum. Although our results have direct and obvious implications for estimation following group sequential trials, there are also ramifications for a range of other settings, such as random cluster sizes, censored time-to-event data, and the joint modeling of longitudinal and time-to-event data. Here, we use the simplest group sequential setting to develop and explicate the main results. Some implications for random sample sizes and missing data are also considered. Consequences for other related settings will be considered elsewhere. }, number={1}, journal={STATISTICAL METHODS IN MEDICAL RESEARCH}, author={Molenberghs, Geert and Kenward, Michael G. and Aerts, Marc and Verbeke, Geert and Tsiatis, Anastasios A. and Davidian, Marie and Rizopoulos, Dimitris}, year={2014}, month={Feb}, pages={11–41} }
 @article{davidian_2014, title={Publishing without perishing and other career advice}, journal={Past, Present, and Future of Statistical Science}, author={Davidian, M.}, year={2014}, pages={581–591} }
 @article{schulte_tsiatis_laber_davidian_2014, title={Q- and A-Learning Methods for Estimating Optimal Dynamic Treatment Regimes}, volume={29}, ISSN={["0883-4237"]}, DOI={10.1214/13-sts450}, abstractNote={In clinical practice, physicians make a series of treatment decisions over the course of a patient's disease based on his/her baseline and evolving characteristics. A dynamic treatment regime is a set of sequential decision rules that operationalizes this process. Each rule corresponds to a decision point and dictates the next treatment action based on the accrued information. Using existing data, a key goal is estimating the optimal regime, that, if followed by the patient population, would yield the most favorable outcome on average. Q- and A-learning are two main approaches for this purpose. We provide a detailed account of these methods, study their performance, and illustrate them using data from a depression study.}, number={4}, journal={STATISTICAL SCIENCE}, author={Schulte, Phillip J. and Tsiatis, Anastasios A. and Laber, Eric B. and Davidian, Marie}, year={2014}, month={Nov}, pages={640–661} }
 @inbook{ren_davidian_george_goldberg_wright_tsiatis_kosorok_2014, title={Research Methods for Clinical Trials in Personalized Medicine: A Systematic Review}, ISBN={9789814489065 9789814489072}, url={http://dx.doi.org/10.1142/9789814489072_0025}, DOI={10.1142/9789814489072_0025}, abstractNote={Background: Personalized medicine, the notion that an individual’s genetic and other characteristics can be used to individualize the diagnosis, treatment and prevention of disease, is an active and exciting area of research, with tremendous potential to improve the health of society. Methods: Seventy-six studies using personalized medicine analysis techniques published from 2006 to 2010 in six high-impact journals Journal of the American Medical Association, Journal of the National Cancer Institute, Lancet, Nature, Nature Medicine, and the New England Journal of Medicine were reviewed. Selected articles were manually selected based on reporting of the use of genetic information to stratify subjects and on analyses of the association between biomarkers and patient clinical outcomes. Results: We found considerable variability and limited consensus in approaches. Approaches could largely be classified as data-driven, seeking discovery through statistical analysis of data, or knowledge-driven, relying heavily on prior biological information. Some studies took a hybrid approach. Eliminating two articles that were retracted after publication, 56 of the remaining 74 (76%) were cancerrelated. Conclusions: Much work is needed to standardize and improve statistical methods for finding biomarkers, validating results, and efficiently optimizing better individual treatment strategies. Several promising new analytic approaches are available and should be considered in future studies of personalized medicine.}, booktitle={Lost in Translation}, publisher={WORLD SCIENTIFIC}, author={Ren, Zheng and Davidian, Marie and George, Stephen L. and Goldberg, Richard M. and Wright, Fred A. and Tsiatis, Anastasios A. and Kosorok, Michael R.}, year={2014}, month={Mar}, pages={659–684} }
 @article{vock_davidian_tsiatis_2014, title={SNP_NLMM: A SAS Macro to Implement a Flexible Random Effects Density for Generalized Linear and Nonlinear Mixed Models}, volume={56}, ISSN={1548-7660}, url={http://dx.doi.org/10.18637/jss.v056.c02}, DOI={10.18637/jss.v056.c02}, abstractNote={Generalized linear and nonlinear mixed models (GMMMs and NLMMs) are commonly used to represent non-Gaussian or nonlinear longitudinal or clustered data. A common assumption is that the random effects are Gaussian. However, this assumption may be unrealistic in some applications, and misspecification of the random effects density may lead to maximum likelihood parameter estimators that are inconsistent, biased, and inefficient. Because testing if the random effects are Gaussian is difficult, previous research has recommended using a flexible random effects density. However, computational limitations have precluded widespread use of flexible random effects densities for GLMMs and NLMMs. We develop a SAS macro, SNP_NLMM, that overcomes the computational challenges to fit GLMMs and NLMMs where the random effects are assumed to follow a smooth density that can be represented by the seminonparametric formulation proposed by Gallant and Nychka (1987). The macro is flexible enough to allow for any density of the response conditional on the random effects and any nonlinear mean trajectory. We demonstrate the SNP_NLMM macro on a GLMM of the disease progression of toenail infection and on a NLMM of intravenous drug concentration over time.}, number={Code Snippet 2}, journal={Journal of Statistical Software}, publisher={Foundation for Open Access Statistic}, author={Vock, David M. and Davidian, Marie and Tsiatis, Anastasios A.}, year={2014} }
 @book{davidian_lin_morris_stefanski_2014, title={The Work of Raymond J. Carroll}, ISBN={9783319058009 9783319058016}, url={http://dx.doi.org/10.1007/978-3-319-05801-6}, DOI={10.1007/978-3-319-05801-6}, abstractNote={Measurement Error.- Transformation and Weighting.- Epidemiology.- Nonparametric and Semiparametric Regression for Independent Data.- Nonparametric and Semiparametric Regression for Dependent Data.- Robustness.- Other Work Article list for each of these areas is in attachment.}, publisher={Springer International Publishing}, year={2014} }
 @article{verbeke_fieuws_molenberghs_davidian_2014, title={The analysis of multivariate longitudinal data: A review}, volume={23}, ISSN={["1477-0334"]}, DOI={10.1177/0962280212445834}, abstractNote={ Longitudinal experiments often involve multiple outcomes measured repeatedly within a set of study participants. While many questions can be answered by modeling the various outcomes separately, some questions can only be answered in a joint analysis of all of them. In this article, we will present a review of the many approaches proposed in the statistical literature. Four main model families will be presented, discussed and compared. Focus will be on presenting advantages and disadvantages of the different models rather than on the mathematical or computational details. }, number={1}, journal={STATISTICAL METHODS IN MEDICAL RESEARCH}, author={Verbeke, Geert and Fieuws, Steffen and Molenberghs, Geert and Davidian, Marie}, year={2014}, month={Feb}, pages={42–59} }
 @article{vock_tsiatis_davidian_laber_tsuang_copeland_palmer_2013, title={Assessing the Causal Effect of Organ Transplantation on the Distribution of Residual Lifetime}, volume={69}, ISSN={["1541-0420"]}, DOI={10.1111/biom.12084}, abstractNote={Summary}, number={4}, journal={BIOMETRICS}, author={Vock, David M. and Tsiatis, Anastasios A. and Davidian, Marie and Laber, Eric B. and Tsuang, Wayne M. and Copeland, C. Ashley Finlen and Palmer, Scott M.}, year={2013}, month={Dec}, pages={820–829} }
 @inbook{thomas_stefanski_davidian_2013, title={Bias Reduction in Logistic Regression with Estimated Variance Predictors}, ISBN={9781461468707 9781461468714}, ISSN={0930-0325}, url={http://dx.doi.org/10.1007/978-1-4614-6871-4_2}, DOI={10.1007/978-1-4614-6871-4_2}, abstractNote={We study the problem of modeling a response as a function of baseline covariates and a primary predictor of interest that is a noisy measurement of a subject-specific variance. The problem arises naturally in biostatistical joint models wherein the subjects’ primary endpoints are related to the features of subject-specific longitudinal risk processes or profiles. Often the longitudinal process features of interest are parameters of a longitudinal mean function. However, there is a relatively recent and growing interest in relating primary endpoints to longitudinal process variances. In the application motivating our work longitudinal processes consist of 30-day blood pressure trajectories measured between 91 and 120 days post dialysis therapy, with the primary endpoints being short-term mortality. Often the longitudinal risk processes are adequately characterized in terms of trends such as the slopes and intercepts identified with the subject-specific biomarker processes. Modeling of the trend lines results in subject-specific estimated intercepts and slopes, thus inducing a heteroscedastic measurement-error model structure where the estimated trend parameters play the role of measurements of the “true” subject-specific trend parameters that appear as predictors in the primary endpoint model. Our interest lies in models in which the residual variances of the longitudinal processes feed into the model for the primary endpoint. These subject-specific variance parameters are estimated in the course of trend-line fitting creating a measurement error model scenario where variances are predictors and mean squared errors are their noisy measurements. Background literature is reviewed and several methodological approaches for addressing the resulting errors-in-variances problem are studied.}, booktitle={ISS-2012 Proceedings Volume On Longitudinal Data Analysis Subject to Measurement Errors, Missing Values, and/or Outliers}, publisher={Springer New York}, author={Thomas, Laine and Stefanski, Leonard A. and Davidian, Marie}, year={2013}, pages={33–51} }
 @article{sullivan_davidian_destefano_stone_2013, title={Building the Biostatistics Pipeline: Summer Institutes for Training in Biostatistics (SIBS)}, volume={26}, ISSN={0933-2480 1867-2280}, url={http://dx.doi.org/10.1080/09332480.2013.772386}, DOI={10.1080/09332480.2013.772386}, abstractNote={4 Almost every day, another finding related to human health is reported in the news. A new drug shows promise for treating Alzheimer’s disease. A study in a leading medical journal suggests an association between a father’s age and risk of autism, while another finds evidence that certain genetic variants are associated with heart disease. An expert panel recommends that common tests for screening for ovarian cancer should be stopped, as they may do more harm than good. A combined analysis of three major air pollution studies suggests a link between exposure and cognitive decline. Behind all these discoveries is biostatistics—the science of development of statistical theory and methods for application to data-driven challenges in the health sciences. Biostatisticians work on interesting building the biostatistics pipeline: summer institutes for training in biostatistics (sibs)}, number={1}, journal={CHANCE}, publisher={Informa UK Limited}, author={Sullivan, Lisa M. and Davidian, Marie and DeStefano, Anita L. and Stone, Roslyn A.}, year={2013}, month={Feb}, pages={4–9} }
 @article{thomas_stefanski_davidian_2013, title={Moment adjusted imputation for multivariate measurement error data with applications to logistic regression}, volume={67}, ISSN={["1872-7352"]}, DOI={10.1016/j.csda.2013.04.017}, abstractNote={In clinical studies, covariates are often measured with error due to biological fluctuations, device error and other sources. Summary statistics and regression models that are based on mismeasured data will differ from the corresponding analysis based on the "true" covariate. Statistical analysis can be adjusted for measurement error, however various methods exhibit a tradeo between convenience and performance. Moment Adjusted Imputation (MAI) is method for measurement error in a scalar latent variable that is easy to implement and performs well in a variety of settings. In practice, multiple covariates may be similarly influenced by biological fluctuastions, inducing correlated multivariate measurement error. The extension of MAI to the setting of multivariate latent variables involves unique challenges. Alternative strategies are described, including a computationally feasible option that is shown to perform well.}, journal={COMPUTATIONAL STATISTICS & DATA ANALYSIS}, author={Thomas, Laine and Stefanski, Leonard A. and Davidian, Marie}, year={2013}, month={Nov}, pages={15–24} }
 @article{zhang_tsiatis_laber_davidian_2013, title={Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions}, volume={100}, ISSN={["1464-3510"]}, DOI={10.1093/biomet/ast014}, abstractNote={A dynamic treatment regime is a list of sequential decision rules for assigning treatment based on a patient's history. Q- and A-learning are two main approaches for estimating the optimal regime, i.e., that yielding the most beneficial outcome in the patient population, using data from a clinical trial or observational study. Q-learning requires postulated regression models for the outcome, while A-learning involves models for that part of the outcome regression representing treatment contrasts and for treatment assignment. We propose an alternative to Q- and A-learning that maximizes a doubly robust augmented inverse probability weighted estimator for population mean outcome over a restricted class of regimes. Simulations demonstrate the method's performance and robustness to model misspecification, which is a key concern.}, number={3}, journal={BIOMETRIKA}, author={Zhang, Baqun and Tsiatis, Anastasios A. and Laber, Eric B. and Davidian, Marie}, year={2013}, month={Sep}, pages={681–694} }
 @article{davidian_2013, title={The International Year of Statistics: A Celebration and A Call to Action}, volume={108}, ISSN={["1537-274X"]}, DOI={10.1080/01621459.2013.844019}, abstractNote={We are almost two-thirds of the way through 2013, the International Year of Statistics. Statistics2013, as it is also known, is a year-long recognition of the impact of our discipline on science and society. Almost daily, events are taking place across the globe highlighting the contributions of statistics to just about everything. Improving human health. Understanding our environment. Informing decision-making in business and in government. All of us here in this room know the importance of our field. Statistics2013 is devoted to sharing that knowledge – with fellow scientists, industry leaders, government policymakers, students, the media, and the public. Raising awareness of how statistics and statistical thinking affect every one of us will expand opportunities for us to make a difference. And will attract students to the study of statistics, which is essential to meeting the demands posed by this new, data-driven age. And, ultimately, will increase the ability of the public to make sense of what those data mean to them.}, number={504}, journal={JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION}, author={Davidian, Marie}, year={2013}, month={Dec}, pages={1141–1146} }
 @article{zhang_tsiatis_laber_davidian_2012, title={A Robust Method for Estimating Optimal Treatment Regimes}, volume={68}, ISSN={["1541-0420"]}, DOI={10.1111/j.1541-0420.2012.01763.x}, abstractNote={Summary A treatment regime is a rule that assigns a treatment, among a set of possible treatments, to a patient as a function of his/her observed characteristics, hence “personalizing” treatment to the patient. The goal is to identify the optimal treatment regime that, if followed by the entire population of patients, would lead to the best outcome on average. Given data from a clinical trial or observational study, for a single treatment decision, the optimal regime can be found by assuming a regression model for the expected outcome conditional on treatment and covariates, where, for a given set of covariates, the optimal treatment is the one that yields the most favorable expected outcome. However, treatment assignment via such a regime is suspect if the regression model is incorrectly specified. Recognizing that, even if misspecified, such a regression model defines a class of regimes, we instead consider finding the optimal regime within such a class by finding the regime that optimizes an estimator of overall population mean outcome. To take into account possible confounding in an observational study and to increase precision, we use a doubly robust augmented inverse probability weighted estimator for this purpose. Simulations and application to data from a breast cancer clinical trial demonstrate the performance of the method.}, number={4}, journal={BIOMETRICS}, author={Zhang, Baqun and Tsiatis, Anastasios A. and Laber, Eric B. and Davidian, Marie}, year={2012}, month={Dec}, pages={1010–1018} }
 @article{zhang_tsiatis_davidian_zhang_laber_2012, title={Estimating optimal treatment regimes from a classification perspective}, volume={1}, ISSN={2049-1573}, url={http://dx.doi.org/10.1002/sta.411}, DOI={10.1002/sta.411}, abstractNote={A treatment regime maps observed patient characteristics to a recommended treatment. Recent technological advances have increased the quality, accessibility, and volume of patient‐level data; consequently, there is a growing need for powerful and flexible estimators of an optimal treatment regime that can be used with either observational or randomized clinical trial data. We propose a novel and general framework that transforms the problem of estimating an optimal treatment regime into a classification problem wherein the optimal classifier corresponds to the optimal treatment regime. We show that commonly employed parametric and semi‐parametric regression estimators, as well as recently proposed robust estimators of an optimal treatment regime can be represented as special cases within our framework. Furthermore, our approach allows any classification procedure that can accommodate case weights to be used without modification to estimate an optimal treatment regime. This introduces a wealth of new and powerful learning algorithms for use in estimating treatment regimes. We illustrate our approach using data from a breast cancer clinical trial. Copyright © 2012 John Wiley & Sons, Ltd.}, number={1}, journal={Stat}, publisher={Wiley}, author={Zhang, Baqun and Tsiatis, Anastasios A. and Davidian, Marie and Zhang, Min and Laber, Eric}, year={2012}, month={Oct}, pages={103–114} }
 @article{vock_davidian_tsiatis_muir_2012, title={Mixed model analysis of censored longitudinal data with flexible random-effects density}, volume={13}, ISSN={["1468-4357"]}, DOI={10.1093/biostatistics/kxr026}, abstractNote={Mixed models are commonly used to represent longitudinal or repeated measures data. An additional complication arises when the response is censored, for example, due to limits of quantification of the assay used. While Gaussian random effects are routinely assumed, little work has characterized the consequences of misspecifying the random-effects distribution nor has a more flexible distribution been studied for censored longitudinal data. We show that, in general, maximum likelihood estimators will not be consistent when the random-effects density is misspecified, and the effect of misspecification is likely to be greatest when the true random-effects density deviates substantially from normality and the number of noncensored observations on each subject is small. We develop a mixed model framework for censored longitudinal data in which the random effects are represented by the flexible seminonparametric density and show how to obtain estimates in SAS procedure NLMIXED. Simulations show that this approach can lead to reduction in bias and increase in efficiency relative to assuming Gaussian random effects. The methods are demonstrated on data from a study of hepatitis C virus.}, number={1}, journal={BIOSTATISTICS}, author={Vock, David M. and Davidian, Marie and Tsiatis, Anastasios A. and Muir, Andrew J.}, year={2012}, month={Jan}, pages={61–73} }
 @article{yuan_zhang_davidian_2012, title={Variable selection for covariate-adjusted semiparametric inference in randomized clinical trials}, volume={31}, ISSN={["1097-0258"]}, DOI={10.1002/sim.5433}, abstractNote={Extensive baseline covariate information is routinely collected on participants in randomized clinical trials, and it is well recognized that a proper covariate‐adjusted analysis can improve the efficiency of inference on the treatment effect. However, such covariate adjustment has engendered considerable controversy, as post hoc selection of covariates may involve subjectivity and may lead to biased inference, whereas prior specification of the adjustment may exclude important variables from consideration. Accordingly, how to select covariates objectively to gain maximal efficiency is of broad interest. We propose and study the use of modern variable selection methods for this purpose in the context of a semiparametric framework, under which variable selection in modeling the relationship between outcome and covariates is separated from estimation of the treatment effect, circumventing the potential for selection bias associated with standard analysis of covariance methods. We demonstrate that such objective variable selection techniques combined with this framework can identify key variables and lead to unbiased and efficient inference on the treatment effect. A critical issue in finite samples is validity of estimators of uncertainty, such as standard errors and confidence intervals for the treatment effect. We propose an approach to estimation of sampling variation of estimated treatment effect and show its superior performance relative to that of existing methods. Copyright © 2012 John Wiley & Sons, Ltd.}, number={29}, journal={STATISTICS IN MEDICINE}, author={Yuan, Shuai and Zhang, Hao Helen and Davidian, Marie}, year={2012}, month={Dec}, pages={3789–3804} }
 @article{davidian_louis_2012, title={Why Statistics?}, volume={336}, ISSN={["0036-8075"]}, DOI={10.1126/science.1218685}, abstractNote={Popular media and science publications sound the drum: “Big Data” will drive our future, from translating genomic information into new therapies, to harnessing the Web to untangle complex social interactions, to detecting infectious disease outbreaks. Statistics is the science of learning from data, and of measuring, controlling, and communicating uncertainty; and it thereby provides the navigation essential for controlling the course of scientific and societal advances. This field will become ever more critical as academia, businesses, and governments rely increasingly on data-driven decisions, expanding the demand for statistics expertise.}, number={6077}, journal={SCIENCE}, author={Davidian, Marie and Louis, Thomas A.}, year={2012}, month={Apr}, pages={12–12} }
 @article{thomas_stefanski_davidian_2011, title={A Moment-Adjusted Imputation Method for Measurement Error Models}, volume={67}, ISSN={["1541-0420"]}, DOI={10.1111/j.1541-0420.2011.01569.x}, abstractNote={Summary Studies of clinical characteristics frequently measure covariates with a single observation. This may be a mismeasured version of the “true” phenomenon due to sources of variability like biological fluctuations and device error. Descriptive analyses and outcome models that are based on mismeasured data generally will not reflect the corresponding analyses based on the “true” covariate. Many statistical methods are available to adjust for measurement error. Imputation methods like regression calibration and moment reconstruction are easily implemented but are not always adequate. Sophisticated methods have been proposed for specific applications like density estimation, logistic regression, and survival analysis. However, it is frequently infeasible for an analyst to adjust each analysis separately, especially in preliminary studies where resources are limited. We propose an imputation approach called moment‐adjusted imputation that is flexible and relatively automatic. Like other imputation methods, it can be used to adjust a variety of analyses quickly, and it performs well under a broad range of circumstances. We illustrate the method via simulation and apply it to a study of systolic blood pressure and health outcomes in patients hospitalized with acute heart failure.}, number={4}, journal={BIOMETRICS}, author={Thomas, Laine and Stefanski, Leonard and Davidian, Marie}, year={2011}, month={Dec}, pages={1461–1470} }
 @article{tsiatis_davidian_2011, title={Connections between survey calibration estimators and semiparametric models for incomplete data discussion}, volume={79}, number={2}, journal={International Statistical Review}, author={Tsiatis, A. A. and Davidian, M.}, year={2011}, pages={221–223} }
 @article{funk_westreich_wiesen_stuermer_brookhart_davidian_2011, title={Doubly Robust Estimation of Causal Effects}, volume={173}, ISSN={["1476-6256"]}, DOI={10.1093/aje/kwq439}, abstractNote={Doubly robust estimation combines a form of outcome regression with a model for the exposure (i.e., the propensity score) to estimate the causal effect of an exposure on an outcome. When used individually to estimate a causal effect, both outcome regression and propensity score methods are unbiased only if the statistical model is correctly specified. The doubly robust estimator combines these 2 approaches such that only 1 of the 2 models need be correctly specified to obtain an unbiased effect estimator. In this introduction to doubly robust estimators, the authors present a conceptual overview of doubly robust estimation, a simple worked example, results from a simulation study examining performance of estimated and bootstrapped standard errors, and a discussion of the potential advantages and limitations of this method. The supplementary material for this paper, which is posted on the Journal's Web site (http://aje.oupjournals.org/), includes a demonstration of the doubly robust property (Web Appendix 1) and a description of a SAS macro (SAS Institute, Inc., Cary, North Carolina) for doubly robust estimation, available for download at http://www.unc.edu/~mfunk/dr/.}, number={7}, journal={AMERICAN JOURNAL OF EPIDEMIOLOGY}, author={Funk, Michele Jonsson and Westreich, Daniel and Wiesen, Chris and Stuermer, Til and Brookhart, M. Alan and Davidian, Marie}, year={2011}, month={Apr}, pages={761–767} }
 @article{tsiatis_davidian_cao_2011, title={Improved Doubly Robust Estimation When Data Are Monotonely Coarsened, with Application to Longitudinal Studies with Dropout}, volume={67}, ISSN={["1541-0420"]}, DOI={10.1111/j.1541-0420.2010.01476.x}, abstractNote={Summary A routine challenge is that of making inference on parameters in a statistical model of interest from longitudinal data subject to dropout, which are a special case of the more general setting of monotonely coarsened data. Considerable recent attention has focused on doubly robust (DR) estimators, which in this context involve positing models for both the missingness (more generally, coarsening) mechanism and aspects of the distribution of the full data, that have the appealing property of yielding consistent inferences if only one of these models is correctly specified. DR estimators have been criticized for potentially disastrous performance when both of these models are even only mildly misspecified. We propose a DR estimator applicable in general monotone coarsening problems that achieves comparable or improved performance relative to existing DR methods, which we demonstrate via simulation studies and by application to data from an AIDS clinical trial.}, number={2}, journal={BIOMETRICS}, author={Tsiatis, Anastasios A. and Davidian, Marie and Cao, Weihua}, year={2011}, month={Jun}, pages={536–545} }
 @article{zhang_tsiatis_davidian_pieper_mahaffey_2011, title={Inference on treatment effects from a randomized clinical trial in the presence of premature treatment discontinuation: the SYNERGY trial}, volume={12}, ISSN={["1465-4644"]}, DOI={10.1093/biostatistics/kxq054}, abstractNote={The Superior Yield of the New Strategy of Enoxaparin, Revascularization, and GlYcoprotein IIb/IIIa inhibitors (SYNERGY) was a randomized, open-label, multicenter clinical trial comparing 2 anticoagulant drugs on the basis of time-to-event endpoints. In contrast to other studies of these agents, the primary, intent-to-treat analysis did not find evidence of a difference, leading to speculation that premature discontinuation of the study agents by some subjects may have attenuated the apparent treatment effect and thus to interest in inference on the difference in survival distributions were all subjects in the population to follow the assigned regimens, with no discontinuation. Such inference is often attempted via ad hoc analyses that are not based on a formal definition of this treatment effect. We use SYNERGY as a context in which to describe how this effect may be conceptualized and to present a statistical framework in which it may be precisely identified, which leads naturally to inferential methods based on inverse probability weighting.}, number={2}, journal={BIOSTATISTICS}, author={Zhang, Min and Tsiatis, Anastasios A. and Davidian, Marie and Pieper, Karen S. and Mahaffey, Kenneth W.}, year={2011}, month={Apr}, pages={258–269} }
 @article{funk_fusco_cole_thomas_porter_kaufman_davidian_white_hartmann_eron_2011, title={Timing of HAART initiation and clinical outcomes in human immunodeficiency virus type 1 seroconverters}, volume={171}, number={17}, journal={Archives of Internal Medicine}, author={Funk, M. J. and Fusco, J. S. and Cole, S. R. and Thomas, J. C. and Porter, K. and Kaufman, J. S. and Davidian, M. and White, A. D. and Hartmann, K. E. and Eron, J. J.}, year={2011}, pages={1560–1569} }
 @article{hawkins_clay_bradley_davidian_2010, title={Demographic and Historical Findings, Including Exposure to Environmental Tobacco Smoke, in Dogs with Chronic Cough}, volume={24}, ISSN={["1939-1676"]}, DOI={10.1111/j.1939-1676.2010.0530.x}, abstractNote={BACKGROUND
Controlled studies investigating risk factors for the common presenting problem of chronic cough in dogs are lacking.


HYPOTHESIS/OBJECTIVES
To identify demographic and historical factors associated with chronic cough in dogs, and associations between the characteristics of cough and diagnosis.


ANIMALS
Dogs were patients of an academic internal medicine referral service. Coughing dogs had a duration of cough>or=2 months (n=115). Control dogs had presenting problems other than cough (n=104).


METHODS
Owners completed written questionnaires. Demographic information and diagnoses were obtained from medical records. Demographic and historical data were compared between coughing and control dogs. Demographic data and exposure to environmental tobacco smoke (ETS) also were compared with hospital accessions and adult smoking rates, respectively. Characteristics of cough were compared among diagnoses.


RESULTS
Most coughing dogs had a diagnosis of large airway disease (n=88; 77%). Tracheobronchomalacia (TBM) was diagnosed in 59 dogs (51%), including 79% of toy breed dogs. Demographic risk factors included older age, smaller body weight, and being toy breed (P<.001). No association was found between coughing and month (P=.239) or season (P=.414) of presentation. Exposure to ETS was not confirmed to be a risk factor (P=.243). No historical description of cough was unique to a particular diagnosis.


CONCLUSIONS AND CLINICAL IMPORTANCE
Associations with age, size, and toy breeds were strong. TBM is frequent in dogs with chronic cough, but descriptions of cough should be used cautiously in prioritizing differential diagnoses. The association between exposure to ETS and chronic cough deserves additional study.}, number={4}, journal={JOURNAL OF VETERINARY INTERNAL MEDICINE}, author={Hawkins, E. C. and Clay, L. D. and Bradley, J. M. and Davidian, M.}, year={2010}, pages={825–831} }
 @inbook{jonsson funk_westreich_weisen_davidian_2010, place={Cary, NC}, title={Doubly robust estimation of treatment effects}, booktitle={Analysis of Observational Health-Care Data Using SAS}, publisher={SAS Press}, author={Jonsson Funk, M. and Westreich, D. and Weisen, C. and Davidian, M.}, editor={Faries, D. and Leon, A. and Haro, J.M. and Obenchain, R.Editors}, year={2010} }
 @inbook{bang_davidian_2010, place={New York}, title={Experimental Statistics for biological sciences}, booktitle={Statistical Methods in Molecular Biology}, publisher={Springer (Humana Press)}, author={Bang, H. and Davidian, M.}, editor={Bang, H. and Zhou, X.K. and Van Epps, H.L. and Mazumdar, M.Editors}, year={2010}, pages={3–104} }
 @article{kepler_banksa_davidian_rosenberg_2009, title={A model for HCMV infection in immunosuppressed patients}, volume={49}, ISSN={["1872-9479"]}, DOI={10.1016/j.mcm.2008.06.003}, abstractNote={We propose a model for HCMV infection in healthy and immunosuppressed patients. First, we present the biological model and formulate a system of ordinary differential equations to describe the pathogenesis of primary HCMV infection in immunocompetent and immunosuppressed individuals. We then investigate how clinical data can be applied to this model. Approximate parameter values for the model are derived from data available in the literature and from mathematical and physiological considerations. Simulations with the approximated parameter values demonstrates that the model is capable of describing primary, latent, and secondary (reactivated) HCMV infection. Reactivation simulations with this model provide a window into the dynamics of HCMV infection in (D-R+) transplant situations, where latently-infected recipients (R+) receive transplant tissue from HCMV-naive donors (D-).}, number={7-8}, journal={MATHEMATICAL AND COMPUTER MODELLING}, author={Kepler, G. M. and Banksa, H. T. and Davidian, M. and Rosenberg, E. S.}, year={2009}, month={Apr}, pages={1653–1663} }
 @inbook{banks_davidian_samuels_sutton_2009, title={An Inverse Problem Statistical Methodology Summary}, ISBN={9789048123124 9789048123131}, url={http://dx.doi.org/10.1007/978-90-481-2313-1_11}, DOI={10.1007/978-90-481-2313-1_11}, abstractNote={We discuss statistical and computational aspects of inverse or parameter estimation problems for deterministic dynamical systems based on Ordinary Least Squares and Generalized Least Squares with appropriate corresponding data noise assumptions of constant variance and nonconstant variance (relative error), respectively. Among the topics included here are mathematical model, statistical model and data assumptions, and some techniques (residual plots, sensitivity analysis, model comparison tests) for verifying these. The ideas are illustrated throughout with the popular logistic growth model of Verhulst and Pearl as well as with a recently developed population level model of pneumococcal disease spread.}, booktitle={Mathematical and Statistical Estimation Approaches in Epidemiology}, publisher={Springer Netherlands}, author={Banks, H. Thomas and Davidian, Marie and Samuels, John R. and Sutton, Karyn L.}, year={2009}, pages={249–302} }
 @article{tzeng_zhang_chang_thomas_davidian_2009, title={Gene-Trait Similarity Regression for Multimarker-Based Association Analysis}, volume={65}, ISSN={0006-341X}, url={http://dx.doi.org/10.1111/j.1541-0420.2008.01176.x}, DOI={10.1111/j.1541-0420.2008.01176.x}, abstractNote={Summary We propose a similarity‐based regression method to detect associations between traits and multimarker genotypes. The model regresses similarity in traits for pairs of “unrelated” individuals on their haplotype similarities, and detects the significance by a score test for which the limiting distribution is derived. The proposed method allows for covariates, uses phase‐independent similarity measures to bypass the needs to impute phase information, and is applicable to traits of general types (e.g., quantitative and qualitative traits). We also show that the gene‐trait similarity regression is closely connected with random effects haplotype analysis, although commonly they are considered as separate modeling tools. This connection unites the classic haplotype sharing methods with the variance‐component approaches, which enables direct derivation of analytical properties of the sharing statistics even when the similarity regression model becomes analytically challenging.}, number={3}, journal={Biometrics}, publisher={Wiley}, author={Tzeng, Jung-Ying and Zhang, Daowen and Chang, Sheng-Mao and Thomas, Duncan C. and Davidian, Marie}, year={2009}, month={Feb}, pages={822–832} }
 @article{cao_tsiatis_davidian_2009, title={Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data}, volume={96}, ISSN={["0006-3444"]}, DOI={10.1093/biomet/asp033}, abstractNote={Abstract}, number={3}, journal={BIOMETRIKA}, author={Cao, Weihua and Tsiatis, Anastasios A. and Davidian, Marie}, year={2009}, month={Sep}, pages={723–734} }
 @article{huang_stefanski_davidian_2009, title={Latent-Model Robustness in Joint Models for a Primary Endpoint and a Longitudinal Process}, volume={65}, ISSN={["1541-0420"]}, DOI={10.1111/j.1541-0420.2008.01171.x}, abstractNote={Summary Joint modeling of a primary response and a longitudinal process via shared random effects is widely used in many areas of application. Likelihood‐based inference on joint models requires model specification of the random effects. Inappropriate model specification of random effects can compromise inference. We present methods to diagnose random effect model misspecification of the type that leads to biased inference on joint models. The methods are illustrated via application to simulated data, and by application to data from a study of bone mineral density in perimenopausal women and data from an HIV clinical trial.}, number={3}, journal={BIOMETRICS}, author={Huang, Xianzheng and Stefanski, Leonard A. and Davidian, Marie}, year={2009}, month={Sep}, pages={719–727} }
 @article{serroyen_molenberghs_verbeke_davidian_2009, title={Nonlinear Models for Longitudinal Data}, volume={63}, ISSN={["1537-2731"]}, DOI={10.1198/tast.2009.07256}, abstractNote={Whereas marginal models, random-effects models, and conditional models are routinely considered to be the three main modeling families for continuous and discrete repeated measures with linear and generalized linear mean structures, respectively, it is less common to consider nonlinear models, let alone frame them within the above taxonomy. In the latter situation, indeed, when considered at all, the focus is often exclusively on random-effects models. In this article, we consider all three families, exemplify their great flexibility and relative ease of use, and apply them to a simple but illustrative set of data on tree circumference growth of orange trees. This article has supplementary material online.}, number={4}, journal={AMERICAN STATISTICIAN}, author={Serroyen, Jan and Molenberghs, Geert and Verbeke, Geert and Davidian, Marie}, year={2009}, month={Nov}, pages={378–388} }
 @article{zhang_davidian_2008, title={"Smooth" semiparametric regression analysis for arbitrarily censored time-to-event data}, volume={64}, ISSN={["1541-0420"]}, DOI={10.1111/j.1541-0420.2007.00928.x}, abstractNote={Summary A general framework for regression analysis of time‐to‐event data subject to arbitrary patterns of censoring is proposed. The approach is relevant when the analyst is willing to assume that distributions governing model components that are ordinarily left unspecified in popular semiparametric regression models, such as the baseline hazard function in the proportional hazards model, have densities satisfying mild “smoothness” conditions. Densities are approximated by a truncated series expansion that, for fixed degree of truncation, results in a “parametric” representation, which makes likelihood‐based inference coupled with adaptive choice of the degree of truncation, and hence flexibility of the model, computationally and conceptually straightforward with data subject to any pattern of censoring. The formulation allows popular models, such as the proportional hazards, proportional odds, and accelerated failure time models, to be placed in a common framework; provides a principled basis for choosing among them; and renders useful extensions of the models straightforward. The utility and performance of the methods are demonstrated via simulations and by application to data from time‐to‐event studies.}, number={2}, journal={BIOMETRICS}, author={Zhang, Min and Davidian, Marie}, year={2008}, month={Jun}, pages={567–576} }
 @article{doehler_davidian_2008, title={'Smooth' inference for survival functions with arbitrarily censored data}, volume={27}, DOI={10.1002/sim.3368}, abstractNote={Abstract}, number={26}, journal={Statistics in Medicine}, author={Doehler, K. and Davidian, Marie}, year={2008}, pages={5421–5439} }
 @article{tsiatis_davidian_zhang_lu_2008, title={Covariate adjustment for two-sample treatment comparisons in randomized clinical trials: A principled yet flexible approach}, volume={27}, ISSN={["1097-0258"]}, DOI={10.1002/sim.3113}, abstractNote={Abstract}, number={23}, journal={STATISTICS IN MEDICINE}, author={Tsiatis, Anastasios A. and Davidian, Marie and Zhang, Min and Lu, Xiaomin}, year={2008}, month={Oct}, pages={4658–4677} }
 @article{zhang_tsiatis_davidian_2008, title={Improving efficiency of inferences in randomized clinical trials using auxiliary covariates}, volume={64}, ISSN={["1541-0420"]}, DOI={10.1111/j.1541-0420.2007.00976.x}, abstractNote={Summary The primary goal of a randomized clinical trial is to make comparisons among two or more treatments. For example, in a two‐arm trial with continuous response, the focus may be on the difference in treatment means; with more than two treatments, the comparison may be based on pairwise differences. With binary outcomes, pairwise odds ratios or log odds ratios may be used. In general, comparisons may be based on meaningful parameters in a relevant statistical model. Standard analyses for estimation and testing in this context typically are based on the data collected on response and treatment assignment only. In many trials, auxiliary baseline covariate information may also be available, and it is of interest to exploit these data to improve the efficiency of inferences. Taking a semiparametric theory perspective, we propose a broadly applicable approach to adjustment for auxiliary covariates to achieve more efficient estimators and tests for treatment parameters in the analysis of randomized clinical trials. Simulations and applications demonstrate the performance of the methods.}, number={3}, journal={BIOMETRICS}, author={Zhang, Min and Tsiatis, Anastasios A. and Davidian, Marie}, year={2008}, month={Sep}, pages={707–715} }
 @inbook{verbeke_davidian_2008, title={Joint models for longitudinal data}, ISBN={9781584886587 9781420011579}, ISSN={2154-5944}, url={http://dx.doi.org/10.1201/9781420011579.pt4}, DOI={10.1201/9781420011579.pt4}, booktitle={Chapman & Hall/CRC Handbooks of Modern Statistical Methods}, publisher={Chapman and Hall/CRC}, author={Verbeke, Geert and Davidian, Marie}, year={2008}, month={Aug}, pages={319–326} }
 @book{fitzmaurice_davidian_verbeke_molenberghs_2008, title={Longitudinal Data Analysis}, ISBN={9780429142673}, url={http://dx.doi.org/10.1201/9781420011579}, DOI={10.1201/9781420011579}, abstractNote={Course Description: This course covers statistical models for drawing scientific inferences from longitudinal data, longitudinal study design, and repeated measures. Modern methods for the analysis of repeated measures, correlated outcomes, and longitudinal data are included. It is intended for students with an interest in the analysis of longitudinal data and the emphasis will be on practical applications of statistical methods in the health sciences.}, publisher={Chapman and Hall/CRC}, year={2008}, month={Aug} }
 @book{fitzmaurice_davidian_verbeke_molenberghs_2008, place={Boca Raton, FL}, title={Longitudinal Data Analysis}, journal={Chapman & Hall/CRC, Taylor & Francis Group}, publisher={Chapman & Hall/CRC}, author={Fitzmaurice, G. and Davidian, M. and Verbeke, G. and Molenberghs, G.}, year={2008}, month={Aug} }
 @article{banks_davidian_hu_kepler_rosenberg_2008, title={Modelling HIV immune response and validation with clinical data}, volume={2}, ISSN={1751-3758 1751-3766}, url={http://dx.doi.org/10.1080/17513750701813184}, DOI={10.1080/17513750701813184}, abstractNote={A system of ordinary differential equations is formulated to describe the pathogenesis of HIV infection, wherein certain features that have been shown to be important by recent experimental research are incorporated in the model. These include the role of CD4+memory cells that serve as a major reservoir of latently infected cells, a critical role for T-helper cells in the generation of CD8 memory cells capable of efficient recall response, and stimulation by antigens other than HIV. A stability analysis illustrates the capability of this model in admitting multiple locally asymptotically stable (locally a.s.) off-treatment equilibria. We show that this more biologically detailed model can exhibit the phenomenon of transient viremia experienced by some patients on therapy with viral load levels suppressed below the detection limit. We also show that the loss of CD4+T-cell help in the generation of CD8+memory cells leads to larger peak values for the viral load during transient viremia. Censored clinical data is used to obtain parameter estimates. We demonstrate that using a reduced set of 16 free parameters, obtained by fixing some parameters at their population averages, the model provides reasonable fits to the patient data and, moreover, that it exhibits good predictive capability. We further show that parameter values obtained for most clinical patients do not admit multiple locally a.s off-treatment equilibria. This suggests that treatment to move from a high viral load equilibrium state to an equilibrium state with a lower (or zero) viral load is not possible for these patients.}, number={4}, journal={Journal of Biological Dynamics}, publisher={Informa UK Limited}, author={Banks, H. T. and Davidian, M. and Hu, Shuhua and Kepler, Grace M. and Rosenberg, E. S.}, year={2008}, month={Oct}, pages={357–385} }
 @inbook{davidian_2008, title={Non-linear mixed-effects models}, ISBN={9781584886587 9781420011579}, ISSN={2154-5944}, url={http://dx.doi.org/10.1201/9781420011579.ch5}, DOI={10.1201/9781420011579.ch5}, booktitle={Chapman & Hall/CRC Handbooks of Modern Statistical Methods}, publisher={Chapman and Hall/CRC}, author={Davidian, Marie}, year={2008}, month={Aug}, pages={107–141} }
 @article{tsiatis_davidian_2007, title={Comment: Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data}, volume={22}, ISSN={0883-4237}, url={http://dx.doi.org/10.1214/07-sts227b}, DOI={10.1214/07-sts227b}, abstractNote={We congratulate Drs. Kang and Schafer (KS henceforth) for a careful and thought-provoking contribution to the literature regarding the so-called “double robustness” property, a topic that still engenders some confusion and disagreement. The authors’ approach of focusing on the simplest situation of estimation of the population mean μ of a response y when y is not observed on all subjects according to a missing at random (MAR) mechanism (equivalently, estimation of the mean of a potential outcome in a causal model under the assumption of no unmeasured confounders) is commendable, as the fundamental issues can be explored without the distractions of the messier notation and considerations required in more complicated settings. Indeed, as the article demonstrates, this simple setting is sufficient to highlight a number of key points. 
 
As noted eloquently by Molenberghs (2005), in regard to how such missing data/causal inference problems are best addressed, two “schools” may be identified: the “likelihood-oriented” school and the “weighting-based” school. As we have emphasized previously (Davidian, Tsiatis and Leon, 2005), we prefer to view inference from the vantage point of semi-parametric theory, focusing on the assumptions embedded in the statistical models leading to different “types” of estimators (i.e., “likelihood-oriented” or “weighting-based”) rather than on the forms of the estimators themselves. In this discussion, we hope to complement the presentation of the authors by elaborating on this point of view. 
 
Throughout, we use the same notation as in the paper.}, number={4}, journal={Statistical Science}, publisher={Institute of Mathematical Statistics}, author={Tsiatis, Anastasios A. and Davidian, Marie}, year={2007}, month={Nov}, pages={569–573} }
 @article{adams_banks_davidian_rosenberg_2007, title={Estimation and prediction with HIV-treatment interruption data}, volume={69}, ISSN={["0092-8240"]}, DOI={10.1007/s11538-006-9140-6}, abstractNote={We consider longitudinal clinical data for HIV patients undergoing treatment interruptions. We use a nonlinear dynamical mathematical model in attempts to fit individual patient data. A statistically-based censored data method is combined with inverse problem techniques to estimate dynamic parameters. The predictive capabilities of this approach are demonstrated by comparing simulations based on estimation of parameters using only half of the longitudinal observations to the full longitudinal data sets.}, number={2}, journal={BULLETIN OF MATHEMATICAL BIOLOGY}, author={Adams, B. M. and Banks, H. T. and Davidian, M. and Rosenberg, E. S.}, year={2007}, month={Feb}, pages={563–584} }
 @article{li_zhang_davidian_2007, title={Likelihood and pseudo-likelihood methods for semiparametric joint models for a primary endpoint and longitudinal data}, volume={51}, ISSN={0167-9473}, url={http://dx.doi.org/10.1016/j.csda.2006.10.008}, DOI={10.1016/j.csda.2006.10.008}, abstractNote={Inference on the association between a primary endpoint and features of longitudinal profiles of a continuous response is of central interest in medical and public health research. Joint models that represent the association through shared dependence of the primary and longitudinal data on random effects are increasingly popular; however, existing inferential methods may be inefficient or sensitive to assumptions on the random effects distribution. We consider a semiparametric joint model that makes only mild assumptions on this distribution and develop likelihood-based inference on the association and distribution, which offers improved performance relative to existing methods that is insensitive to the true random effects distribution. Moreover, the estimated distribution can reveal interesting population features, as we demonstrate for a study of the association between longitudinal hormone levels and bone status in peri-menopausal women.}, number={12}, journal={Computational Statistics & Data Analysis}, publisher={Elsevier BV}, author={Li, Erning and Zhang, Daowen and Davidian, Marie}, year={2007}, month={Aug}, pages={5776–5790} }
 @article{rosenberg_davidian_banks_2007, title={Using mathematical modeling and control to develop structured treatment interruption strategies for HIV infection}, volume={88}, ISSN={["1879-0046"]}, DOI={10.1016/j.drugalcdep.2006.12.024}, abstractNote={The goal of this article is to suggest that mathematical models describing biological processes taking place within a patient over time can be used to design adaptive treatment strategies. We demonstrate using the key example of treatment strategies for human immunodeficiency virus type-1 (HIV) infection. Although there has been considerable progress in management of HIV infection using highly active antiretroviral therapies, continuous treatment with these agents involves significant cost and burden, toxicities, development of drug resistance, and problems with adherence; these latter complications are of particular concern in substance-abusing individuals. This has inspired interest in structured or supervised treatment interruption (STI) strategies, which involve cycles of treatment withdrawal and re-initiation. We argue that the most promising STI strategies are adaptive treatment strategies. We then describe how biological mechanisms governing the interaction over time between HIV and a patient's immune system may be represented by mathematical models and how control methods applied to these models can be used to design adaptive STI strategies seeking to maintain long-term suppression of the virus. We advocate that, when such mathematical representations of processes underlying a disease or disorder are available, they can be an important tool for suggesting adaptive treatment strategies for clinical study.}, journal={DRUG AND ALCOHOL DEPENDENCE}, author={Rosenberg, Eric S. and Davidian, Marie and Banks, H. Thomas}, year={2007}, month={May}, pages={S41–S51} }
 @misc{davidian_2006, title={Dose Calibration}, ISBN={0471899976 9780471899976 0470057335 9780470057339}, url={http://dx.doi.org/10.1002/9780470057339.vad038}, DOI={10.1002/9780470057339.vad038}, abstractNote={Abstract}, journal={Encyclopedia of Environmetrics}, publisher={John Wiley & Sons, Ltd}, author={Davidian, Marie}, year={2006}, month={Sep} }
 @article{huang_stefanski_davidian_2006, title={Latent-model robustness in structural measurement error models}, volume={93}, ISSN={["1464-3510"]}, DOI={10.1093/biomet/93.1.53}, abstractNote={We present methods for diagnosing the effects of model misspecification of the true-predictor distribution in structural measurement error models. We first formulate latent-model robustness theoretically. Then we provide practical techniques for examining the adequacy of an assumed latent predictor model. The methods are illustrated via analytical examples, application to simulated data and with data from a study of coronary heart disease. Copyright 2006, Oxford University Press.}, number={1}, journal={BIOMETRIKA}, author={Huang, XZ and Stefanski, LA and Davidian, M}, year={2006}, month={Mar}, pages={53–64} }
 @article{lin_zhang_davidian_2006, title={Smoothing spline-based score tests for proportional hazards models}, volume={62}, ISSN={["0006-341X"]}, DOI={10.1111/j.1541-0420.2005.00521.x}, abstractNote={Summary We propose “score‐type” tests for the proportional hazards assumption and for covariate effects in the Cox model using the natural smoothing spline representation of the corresponding nonparametric functions of time or covariate. The tests are based on the penalized partial likelihood and are derived by viewing the inverse of the smoothing parameter as a variance component and testing an equivalent null hypothesis that the variance component is zero. We show that the tests have a size close to the nominal level and good power against general alternatives, and we apply them to data from a cancer clinical trial.}, number={3}, journal={BIOMETRICS}, author={Lin, Jiang and Zhang, Daowen and Davidian, Marie}, year={2006}, month={Sep}, pages={803–812} }
 @misc{davidian_mcgilchrist_2005, title={Biometrics}, ISBN={047084907X 9780470849071 0470011815 9780470011812}, url={http://dx.doi.org/10.1002/0470011815.b2a17011}, DOI={10.1002/0470011815.b2a17011}, abstractNote={Abstract}, journal={Encyclopedia of Biostatistics}, publisher={John Wiley & Sons, Ltd}, author={Davidian, M. and McGilchrist, C. A.}, year={2005}, month={Jul} }
 @article{adams_banks_davidian_kwon_tran_wynne_rosenberg_2005, title={HIV dynamics: Modeling, data analysis, and optimal treatment protocols}, volume={184}, ISSN={["1879-1778"]}, DOI={10.1016/j.cam.2005.02.004}, abstractNote={We present an overview of some concepts and methodologies we believe useful in modeling HIV pathogenesis. After a brief discussion of motivation for and previous efforts in the development of mathematical models for progression of HIV infection and treatment, we discuss mathematical and statistical ideas relevant to Structured Treatment Interruptions (STI). Among these are model development and validation procedures including parameter estimation, data reduction and representation, and optimal control relative to STI. Results from initial attempts in each of these areas by an interdisciplinary team of applied mathematicians, statisticians and clinicians are presented.}, number={1}, journal={JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS}, author={Adams, BM and Banks, HT and Davidian, M and Kwon, HD and Tran, HT and Wynne, SN and Rosenberg, ES}, year={2005}, month={Dec}, pages={10–49} }
 @article{davidian_tsiatis_leon_2005, title={Semiparametric estimation of treatment effect in a pretest-posttest study with missing data}, volume={20}, number={3}, journal={Statistical Science}, author={Davidian, M. and Tsiatis, A. A. and Leon, S.}, year={2005}, pages={261–282} }
 @article{tsiatis_davidian_2005, title={Statistical issues arising in the Women's Health Initiative - Discussion}, volume={61}, ISSN={["1541-0420"]}, DOI={10.1111/j.0006-341X.2005.454_9.x}, abstractNote={Summary. A brief overview of the design of the Women’s Health Initiative (WHI) clinical trial and observational study is provided along with a summary of results from the postmenopausal hormone therapy clinical trial components. Since its inception in 1992, the WHI has encountered a number of statistical issues where further methodology developments are needed. These include measurement error modeling and analysis procedures for dietary and physical activity assessment; clinical trial monitoring methods when treatments may affect multiple clinical outcomes, either beneficially or adversely; study design and analysis procedures for high-dimensional genomic and proteomic data; and failure time data analysis procedures when treatment group hazard ratios are time dependent. This final topic seems important in resolving the discrepancy between WHI clinical trial and observational study results on postmenopausal hormone therapy and cardiovascular disease. Consistent evidence from over 40 epidemiologic studies demonstrates that postmenopausal women who use estrogen therapy after the menopause have significantly lower rates of heart disease than women who do not take estrogen ... the evidence clearly supports a clinically important protection against heart disease for postmenopausal women who use estrogen.}, number={4}, journal={BIOMETRICS}, author={Tsiatis, AA and Davidian, M}, year={2005}, month={Dec}, pages={933–935} }
 @article{eisenstein_bethea_muhlbaier_davidian_peterson_stafford_mark_2005, title={Surgeons? Economic Profiles: Can We Get the ?Right? Answers?}, volume={29}, ISSN={0148-5598 1573-689X}, url={http://dx.doi.org/10.1007/s10916-005-3000-z}, DOI={10.1007/s10916-005-3000-z}, abstractNote={Hospitals and payers use economic profiling to evaluate physician and surgeon performance. However, there is significant variation in the data sources and analytic methods that are used. We used information from a hospital's cardiac surgery and cost accounting information systems to create surgeon economic profiles. Three scenarios were examined: (1) surgeon modeled as fixed effect with no patient-mix adjustment; (2) surgeon modeled as fixed effect with patient-mix adjustment; (3) and surgeon modeled as random effect with patient-mix adjustment. We included 574 patients undergoing coronary artery bypass surgery at Baptist Medical Center, Oklahoma City, OK between July 1, 1995 and April 30, 1996. We found that profiles reporting unadjusted average surgeon costs may incorrectly identify high- and low-cost outliers. Adjusting for patient-mix differences and treating surgeons as random effects was the preferred approach. These results demonstrate the need for hospitals to reexamine their economic profiling methods.}, number={2}, journal={Journal of Medical Systems}, publisher={Springer Science and Business Media LLC}, author={Eisenstein, Eric L. and Bethea, Charles F. and Muhlbaier, Lawrence H. and Davidian, Marie and Peterson, Eric D. and Stafford, Judith A. and Mark, Daniel B.}, year={2005}, month={Apr}, pages={111–124} }
 @article{li_zhang_davidian_2004, title={Conditional estimation for generalized linear models when covariates are subject-specific parameters in a mixed model for longitudinal measurements}, volume={60}, number={1}, journal={Biometrics}, author={Li, E. N. and Zhang, D. W. and Davidian, M.}, year={2004}, pages={07-} }
 @article{pieper_tsiatis_davidian_hasselblad_kleiman_boersma_chang_griffin_armstrong_califf_et al._2004, title={Differential Treatment Benefit of Platelet Glycoprotein IIb/IIIa Inhibition With Percutaneous Coronary Intervention Versus Medical Therapy for Acute Coronary Syndromes}, volume={109}, ISSN={0009-7322 1524-4539}, url={http://dx.doi.org/10.1161/01.cir.0000112570.97220.89}, DOI={10.1161/01.cir.0000112570.97220.89}, abstractNote={
            
              Background—
            
            Although many believe that platelet glycoprotein IIb/IIIa inhibitors should be used only in acute coronary syndrome patients undergoing percutaneous coronary intervention, supporting data from randomized clinical trials are tenuous. The assumption that these agents are useful only in conjunction with percutaneous coronary intervention is based primarily on inappropriate subgroup analyses performed across the glycoprotein IIb/IIIa inhibitor trials.
          }, number={5}, journal={Circulation}, publisher={Ovid Technologies (Wolters Kluwer Health)}, author={Pieper, Karen S. and Tsiatis, Anastasios A. and Davidian, Marie and Hasselblad, Vic and Kleiman, Neal S. and Boersma, Eric and Chang, Wei-Ching and Griffin, Jeffrey and Armstrong, Paul W. and Califf, Robert M. and et al.}, year={2004}, month={Feb}, pages={641–646} }
 @article{marron_muller_rice_wang_wang_wang_davidian_diggle_follmann_louis_et al._2004, title={Discussion of nonparametric and semiparametric regression}, volume={14}, number={3}, journal={Statistica Sinica}, author={Marron, J. S. and Muller, H. G. and Rice, J. and Wang, J. L. and Wang, N. Y. and Wang, Y. D. and Davidian, M. and Diggle, P. and Follmann, D. and Louis, T. A. and et al.}, year={2004}, pages={615–629} }
 @article{davidian_lin_wang_2004, title={Emerging issues in longitudinal and functional data analysis - Introduction}, volume={14}, number={3}, journal={Statistica Sinica}, author={Davidian, M. and Lin, X. H. and Wang, J. L.}, year={2004}, pages={613–614} }
 @article{powell_cheshire_laban_colvocoresses_o'donnell_davidian_2004, title={Growth, mortality, and hatchdate distributions of larval and juvenile spotted seatrout (Cynoscion nebulosus) in Florida Bay, Everglades National Park}, volume={102}, number={1}, journal={Fishery Bulletin (Washington, D.C.)}, author={Powell, A. B. and Cheshire, R. T. and Laban, E. H. and Colvocoresses, J. and O'Donnell, P. and Davidian, M.}, year={2004}, pages={142–155} }
 @article{tsiatis_davidian_2004, title={Joint modeling of longitudinal and time-to-event data: An overview}, volume={14}, url={http://www.jstor.org/stable/24307417}, number={3}, journal={Statistica Sinica}, publisher={Institute of Statistical Science, Academia Sinica}, author={Tsiatis, Anastasios A. and Davidian, Marie}, year={2004}, pages={809–834} }
 @article{zhang_davidian_2004, title={Likelihood and conditional likelihood inference for generalized additive mixed models for clustered data}, volume={91}, ISSN={["0047-259X"]}, DOI={10.1016/j.jmva.2004.04.007}, abstractNote={Lin and Zhang (J. Roy. Statist. Soc. Ser. B 61 (1999) 381) proposed the generalized additive mixed model (GAMM) as a framework for analysis of correlated data, where normally distributed random effects are used to account for correlation in the data, and proposed to use double penalized quasi-likelihood (DPQL) to estimate the nonparametric functions in the model and marginal likelihood to estimate the smoothing parameters and variance components simultaneously. However, the normal distributional assumption for the random effects may not be realistic in many applications, and it is unclear how violation of this assumption affects ensuing inferences for GAMMs. For a particular class of GAMMs, we propose a conditional estimation procedure built on a conditional likelihood for the response given a sufficient statistic for the random effect, treating the random effect as a nuisance parameter, which thus should be robust to its distribution. In extensive simulation studies, we assess performance of this estimator under a range of conditions and use it as a basis for comparison to DPQL to evaluate the impact of violation of the normality assumption. The procedure is illustrated with application to data from the Multicenter AIDS Cohort Study (MACS).}, number={1}, journal={JOURNAL OF MULTIVARIATE ANALYSIS}, author={Zhang, DW and Davidian, M}, year={2004}, month={Oct}, pages={90–106} }
 @inbook{ma_genton_davidian_2004, title={Linear mixed effects models with flexible generalized skew-elliptical random effects}, ISBN={1584884312}, booktitle={Skew-elliptical distibutions and their applications: A journey beyond normality}, publisher={Boca Raton, FL: Chapman & Hall/CRC}, author={Ma, Y. Y. and Genton, M. G. and Davidian, M.}, year={2004} }
 @article{bodnar_davidian_siega-riz_tsiatis_2004, title={Marginal structural models for analyzing causal effects of time-dependent treatments: An application in perinatal epidemiology}, volume={159}, ISSN={["1476-6256"]}, DOI={10.1093/aje/kwh131}, abstractNote={Marginal structural models (MSMs) are causal models designed to adjust for time-dependent confounding in observational studies of time-varying treatments. MSMs are powerful tools for assessing causality with complicated, longitudinal data sets but have not been widely used by practitioners. The objective of this paper is to illustrate the fitting of an MSM for the causal effect of iron supplement use during pregnancy (time-varying treatment) on odds of anemia at delivery in the presence of time-dependent confounding. Data from pregnant women enrolled in the Iron Supplementation Study (Raleigh, North Carolina, 1997-1999) were used. The authors highlight complexities of MSMs and key issues epidemiologists should recognize before and while undertaking an analysis with these methods and show how such methods can be readily interpreted in existing software packages, including SAS and Stata. The authors emphasize that if a data set with rich information on confounders is available, MSMs can be used straightforwardly to make robust inferences about causal effects of time-dependent treatments/exposures in epidemiologic research.}, number={10}, journal={AMERICAN JOURNAL OF EPIDEMIOLOGY}, author={Bodnar, LM and Davidian, M and Siega-Riz, AM and Tsiatis, AA}, year={2004}, month={May}, pages={926–934} }
 @article{lunceford_davidian_2004, title={Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study}, volume={23}, ISSN={["1097-0258"]}, DOI={10.1002/SIM.1903}, abstractNote={Abstract}, number={19}, journal={STATISTICS IN MEDICINE}, author={Lunceford, JK and Davidian, M}, year={2004}, month={Oct}, pages={2937–2960} }
 @article{davidian_giltinan_2003, title={Nonlinear models for repeated measurement data: An overview and update}, volume={8}, ISSN={["1537-2693"]}, DOI={10.1198/1085711032697}, abstractNote={Nonlinear mixed effects models for data in the form of continuous, repeated measurements on each of a number of individuals, also known as hierarchical nonlinear models, are a popular platform for analysis when interest focuses on individual-specific characteristics. This framework first enjoyed widespread attention within the statistical research community in the late 1980s, and the 1990s saw vigorous development of new methodological and computational techniques for these models, the emergence of general-purpose software, and broad application of the models in numerous substantive fields. This article presentsan overview of the formulation, interpretation, and implementation of nonlinear mixed effects models and surveys recent advances and applications.}, number={4}, journal={JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS}, author={Davidian, M and Giltinan, DM}, year={2003}, month={Dec}, pages={387–419} }
 @article{yeap_catalano_ryan_davidian_2003, title={Robust two-stage approach to repeated measurements analysis of chronic ozone exposure in rats}, volume={8}, ISSN={["1085-7117"]}, DOI={10.1198/1085711032552}, abstractNote={A robust two-stage approach is used to reanalyze the repeated measurements from an experiment of airway responsiveness in rats randomized to long-term exposure at four ozone doses. The concentration-response data generated for each rat may be represented as a hierarchical nonlinear model encompasing the sources of variation within and between individual profile for each rat, the conditional modeling approach can assess the adequacy of an assumed mean model, a fundamental advantage not intrinsic to marginal techniques. The two-stage population inference is based on the estimated individual parameters, thus maintaining an intuitive appeal to the toxicologists who traditionally have fitted a separate curve for each animal and then applied ANOVA to the summary statistics. However, we formally adjust the standard errors for the extra variability due to the initial estimation of the individual parameters and also allow for their within-rat correlation. The robust two-stage method appropriately down weights the a berrant responses arising sporadically within individualsand, more importantly, the rats which may be outlying relative to the usual population variation. The true effect of chronic ozone exposure, including a significant gender interaction, may be masked by a few rats which exert undue influence on the population estimates in a nonrobust analysis.}, number={4}, journal={JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS}, author={Yeap, BY and Catalano, PJ and Ryan, LM and Davidian, M}, year={2003}, month={Dec}, pages={438–454} }
 @article{leon_tsiatis_davidian_2003, title={Semiparametric estimation of treatment effect in a pretest-posttest study}, volume={59}, ISSN={["0006-341X"]}, DOI={10.1111/j.0006-341X.2003.00120.x}, abstractNote={Summary.  Inference on treatment effects in a pretest‐posttest study is a routine objective in medicine, public health, and other fields. A number of approaches have been advocated. We take a semiparametric perspective, making no assumptions about the distributions of baseline and posttest responses. By representing the situation in terms of counterfactual random variables, we exploit recent developments in the literature on missing data and causal inference, to derive the class of all consistent treatment effect estimators, identify the most efficient such estimator, and outline strategies for implementation of estimators that may improve on popular methods. We demonstrate the methods and their properties via simulation and by application to a data set from an HIV clinical trial.}, number={4}, journal={BIOMETRICS}, author={Leon, S and Tsiatis, AA and Davidian, M}, year={2003}, month={Dec}, pages={1046–1055} }
 @article{chen_zhang_davidian_2002, title={A Monte Carlo EM algorithm for generalized linear mixed models with flexible random effects distribution.}, volume={3}, url={https://doi.org/10.1093/biostatistics/3.3.347}, DOI={10.1093/biostatistics/3.3.347}, abstractNote={A popular way to represent clustered binary, count, or other data is via the generalized linear mixed model framework, which accommodates correlation through incorporation of random effects. A standard assumption is that the random effects follow a parametric family such as the normal distribution; however, this may be unrealistic or too restrictive to represent the data. We relax this assumption and require only that the distribution of random effects belong to a class of 'smooth' densities and approximate the density by the seminonparametric (SNP) approach of Gallant and Nychka (1987). This representation allows the density to be skewed, multi-modal, fat- or thin-tailed relative to the normal and includes the normal as a special case. Because an efficient algorithm to sample from an SNP density is available, we propose a Monte Carlo EM algorithm using a rejection sampling scheme to estimate the fixed parameters of the linear predictor, variance components and the SNP density. The approach is illustrated by application to a data set and via simulation.}, number={3}, journal={Biostatistics (Oxford, England)}, author={Chen, J and Zhang, D and Davidian, M}, year={2002}, month={Sep}, pages={347–360} }
 @article{song_davidian_tsiatis_2002, title={A semiparametric likelihood approach to joint modeling of longitudinal and time-to-event data}, volume={58}, ISSN={["0006-341X"]}, DOI={10.1111/j.0006-341X.2002.00742.x}, abstractNote={Summary. Joint models for a time‐to‐event (e.g., survival) and a longitudinal response have generated considerable recent interest. The longitudinal data are assumed to follow a mixed effects model, and a proportional hazards model depending on the longitudinal random effects and other covariates is assumed for the survival endpoint. Interest may focus on inference on the longitudinal data process, which is informatively censored, or on the hazard relationship. Several methods for fitting such models have been proposed, most requiring a parametric distributional assumption (normality) on the random effects. A natural concern is sensitivity to violation of this assumption; moreover, a restrictive distributional assumption may obscure key features in the data. We investigate these issues through our proposal of a likelihood‐based approach that requires only the assumption that the random effects have a smooth density. Implementation via the EM algorithm is described, and performance and the benefits for uncovering noteworthy features are illustrated by application to data from an HIV clinical trial and by simulation.}, number={4}, journal={BIOMETRICS}, author={Song, X and Davidian, M and Tsiatis, AA}, year={2002}, month={Dec}, pages={742–753} }
 @article{song_davidian_tsiatis_2002, title={An estimator for the proportional hazards model with multiple longitudinal covariates measured with error}, volume={3}, number={4}, journal={Biostatistics (Oxford, England)}, author={Song, X. A. and Davidian, M. and Tsiatis, A. A.}, year={2002}, pages={511–528} }
 @inbook{altan_manola_davidian_raghavarao_2002, place={Basel}, series={Developments in Biologicals}, title={Constrained four parameter logistic model}, volume={107}, booktitle={The Design and Analysis of Potency Assays for Biotechnology Products}, publisher={Karger}, author={Altan, S. and Manola, A. and Davidian, M. and Raghavarao, D.}, year={2002}, pages={71–76}, collection={Developments in Biologicals} }
 @article{lunceford_davidian_tsiatis_2002, title={Estimation of survival distributions of treatment policies in two-stage randomization designs in clinical trials}, volume={58}, ISSN={["0006-341X"]}, DOI={10.1111/j.0006-341X.2002.00048.x}, abstractNote={Summary.  Some clinical trials follow a design where patients are randomized to a primary therapy at entry followed by another randomization to maintenance therapy contingent upon disease remission. Ideally, analysis would allow different treatment policies, i.e., combinations of primary and maintenance therapy if specified up‐front, to be compared. Standard practice is to conduct separate analyses for the primary and follow‐up treatments, which does not address this issue directly. We propose consistent estimators for the survival distribution and mean restricted survival time for each treatment policy in such two‐stage studies and derive large‐sample properties. The methods are demonstrated on a leukemia clinical trial data set and through simulation.}, number={1}, journal={BIOMETRICS}, author={Lunceford, JK and Davidian, M and Tsiatis, AA}, year={2002}, month={Mar}, pages={48–57} }
 @article{tsiatis_davidian_mcneney_2002, title={Multiple imputation methods for testing treatment differences in survival distributions with missing cause of failure}, volume={89}, number={1}, journal={Biometrika}, author={Tsiatis, A. A. and Davidian, M. and Mcneney, B.}, year={2002}, pages={238–244} }
 @article{randomized comparison of platelet inhibition with abciximab, tirofiban and eptifibatide during percutaneous coronary intervention in acute coronary syndromes - the compare trial_2002, volume={106}, number={12}, journal={Circulation (New York, N.Y. : 1950)}, year={2002}, pages={1470–1476} }
 @article{tsiatis_davidian_2001, title={A semiparametric estimator for the proportional hazards model with longitudinal covariates measured with error}, volume={88}, ISSN={["0006-3444"]}, url={http://www.jstor.org/stable/2673492}, DOI={10.1093/biomet/88.2.447}, abstractNote={SUMMARY A common objective in longitudinal studies is to characterise the relationship between a failure time process and time-independent and time-dependent covariates. Timedependent covariates are generally available as longitudinal data collected periodically during the course of the study. We assume that these data follow a linear mixed effects model with normal measurement error and that the hazard of failure depends both on the underlying random effects describing the covariate process and other time-independent covariates through a proportional hazards relationship. A routine assumption is that the random effects are normally distributed; however, this need not hold in practice. Within this framework, we develop a simple method for estimating the proportional hazards model parameters that requires no assumption on the distribution of the random effects. Large-sample properties are discussed, and finite-sample performance is assessed and compared to competing methods via simulation.}, number={2}, journal={BIOMETRIKA}, publisher={[Oxford University Press, Biometrika Trust]}, author={Tsiatis, AA and Davidian, M}, year={2001}, month={Jun}, pages={447–458} }
 @article{zhang_davidian_2001, title={Linear mixed models with flexible distributions of random effects for longitudinal data}, volume={57}, ISSN={["0006-341X"]}, DOI={10.1111/j.0006-341X.2001.00795.x}, abstractNote={Summary. Normality of random effects is a routine assumption for the linear mixed model, but it may be unrealistic, obscuring important features of among‐individual variation. We relax this assumption by approximating the random effects density by the seminonparameteric (SNP) representation of Gallant and Nychka (1987, Econometrics55, 363–390), which includes normality as a special case and provides flexibility in capturing a broad range of nonnormal behavior, controlled by a user‐chosen tuning parameter. An advantage is that the marginal likelihood may be expressed in closed form, so inference may be carried out using standard optimization techniques. We demonstrate that standard information criteria may be used to choose the tuning parameter and detect departures from normality, and we illustrate the approach via simulation and using longitudinal data from the Framingham study.}, number={3}, journal={BIOMETRICS}, author={Zhang, DW and Davidian, M}, year={2001}, month={Sep}, pages={795–802} }
 @article{yeap_davidian_2001, title={Robust two-stage estimation in hierarchical nonlinear models}, volume={57}, ISSN={["0006-341X"]}, DOI={10.1111/j.0006-341X.2001.00266.x}, abstractNote={Summary.  Hierarchical models encompass two sources of variation, namely within and among individuals in the population; thus, it is important to identify outliers that may arise at each sampling level. A two‐stage approach to analyzing nonlinear repeated measurements naturally allows parametric modeling of the respective variance structure for the intraindividual random errors and interindividual random effects. We propose a robust two‐stage procedure based on Huber's (1981, Robust Statistics) theory of M‐estimation to accommodate separately aberrant responses within an experimental unit and subjects deviating from the study population when the usual assumptions of normality are violated. A toxicology study of chronic ozone exposure in rats illustrates the impact of outliers on the population inference and hence the advantage of adopting the robust methodology. The robust weights generated by the two‐stage M‐estimation process also serve as diagnostics for gauging the relative influence of outliers at each level of the hierarchical model. A practical appeal of our proposal is the computational simplicity since the estimation algorithm may be implemented using standard statistical software with a nonlinear least squares routine and iterative capability.}, number={1}, journal={BIOMETRICS}, author={Yeap, BY and Davidian, M}, year={2001}, month={Mar}, pages={266–272} }
 @article{hartford_davidian_2000, title={Consequences of misspecifying assumptions in nonlinear mixed effects models}, volume={34}, ISSN={["0167-9473"]}, DOI={10.1016/S0167-9473(99)00076-6}, abstractNote={The nonlinear mixed effects model provides a framework for inference in a number of applications, most notably pharmacokinetics and pharmacodynamics, but also in HIV and other disease dynamics and in a host of other longitudinal-data settings. In these models, to characterize population variation, individual-specific parameters are modeled as functions of fixed effects and mean-zero random effects. A standard assumption is that of normality of the random effects, but this assumption may not always be realistic, and, because the random effects are not observed, it may be difficult to verify. An additional issue is specifying the form of the function relating individual-specific parameters to fixed and random effects. Again, because this relationship is not observed explicitly, it may be difficult to specify. Popular methods for fitting these models are predicated on the normality assumption, and past studies evaluating their performance have assumed that normality and the form of the model are correct specifications. We investigate the consequences for population inferences using these methods when the normality assumption is inappropriate and/or the model is misspecified.}, number={2}, journal={COMPUTATIONAL STATISTICS & DATA ANALYSIS}, author={Hartford, A and Davidian, M}, year={2000}, month={Aug}, pages={139–164} }
 @article{ko_davidian_2000, title={Correcting for measurement error in individual-level covariates in nonlinear mixed effects models}, volume={56}, ISSN={["0006-341X"]}, DOI={10.1111/j.0006-341X.2000.00368.x}, abstractNote={Summary.  The nonlinear mixed effects model is used to represent data in pharmacokinetics, viral dynamics, and other areas where an objective is to elucidate associations among individual‐specific model parameters and covariates; however, covariates may be measured with error. For additive measurement error, we show substitution of mismeasured covariates for true covariates may lead to biased estimators for fixed effects and random effects covariance parameters, while regression calibration may eliminate bias in fixed effects but fail to correct that in covariance parameters. We develop methods to take account of measurement error that correct this bias and may be implemented with standard software, and we demonstrate their utility via simulation and application to data from a study of HIV dynamics.}, number={2}, journal={BIOMETRICS}, author={Ko, HJ and Davidian, M}, year={2000}, month={Jun}, pages={368–375} }
 @article{neumann_lam_dahari_davidian_wiley_mika_perelson_layden_2000, title={Differences in viral dynamics between genotypes 1 and 2 of hepatitis C virus}, volume={182}, ISSN={["1537-6613"]}, DOI={10.1086/315661}, abstractNote={Many studies have shown that patients infected with hepatitis C virus (HCV) of genotype 2 have better response to interferon (IFN)-alpha treatment than genotype 1 patients; however, the mechanisms responsible for this difference are not understood. In this study, viral dynamics during high-dose IFN induction treatment were compared between the genotypes. Patients in each group received 10 MU of IFN-alpha2b for 14 days, and HCV RNA levels were frequently determined. Nonlinear fitting, both individually for each patient and using a mixed-effects approach, of the viral kinetic data to a mathematical model of the IFN effect on HCV infection was performed. The antiviral effectiveness of IFN in blocking virus production, the free virion clearance rate, and the HCV-infected cell death rate were all significantly higher for genotype 2 patients than for genotype 1 patients. Thus, the better response rate of patients infected with HCV genotype 2 is multifactorial. This is the first finding of a difference in viral dynamics between subtypes of the same virus and demonstrates the importance of subtype-specific virus-host-drug interactions.}, number={1}, journal={JOURNAL OF INFECTIOUS DISEASES}, author={Neumann, AU and Lam, NP and Dahari, H and Davidian, M and Wiley, TE and Mika, BP and Perelson, AS and Layden, TJ}, year={2000}, month={Jul}, pages={28–35} }
 @article{oberg_davidian_2000, title={Estimating data transformations in nonlinear mixed effects models}, volume={56}, ISSN={["0006-341X"]}, DOI={10.1111/j.0006-341X.2000.00065.x}, abstractNote={Summary.  A routine practice in the analysis of repeated measurement data is to represent individual responses by a mixed effects model on some transformed scale. For example, for pharmacokinetic, growth, and other data, both the response and the regression model are typically transformed to achieve approximate within‐individual normality and constant variance on the new scale; however, the choice of transformation is often made subjectively or by default, with adoption of a standard choice such as the log. We propose a mixed effects framework based on the transform‐both‐sides model, where the transformation is represented by a monotone parametric function and is estimated from the data. For this model, we describe a practical fitting strategy based on approximation of the marginal likelihood. Inference is complicated by the fact that estimation of the transformation requires modification of the usual standard errors for estimators of fixed effects; however, we show that, under conditions relevant to common applications, this complication is asymptotically negligible, allowing straightforward implementation via standard software.}, number={1}, journal={BIOMETRICS}, author={Oberg, A and Davidian, M}, year={2000}, month={Mar}, pages={65–72} }
 @article{betts_krowka_kepler_davidian_christopherson_kwok_louie_eron_sheppard_frelinger_1999, title={Human immunodeficiency virus type 1-specific cytotoxic T lymphocyte activity is inversely correlated with HIV type 1 viral load in HIV type 1-infected long-term survivors}, volume={15}, ISSN={["1931-8405"]}, DOI={10.1089/088922299310313}, abstractNote={HIV-1-specific cytotoxic T cell (CTL) activity has been suggested to correlate with protection from progression to AIDS. We have examined the relationship between HIV-specific CTL activity and maintenance of peripheral blood CD4+ T lymphocyte counts and control of viral load in 17 long-term survivors (LTSs) of HIV-1 infection. Longitudinal analysis indicated that the LTS cohort demonstrated a decreased rate of CD4+ T cell loss (18 cells/mm3/year) compared with typical normal progressors (approximately 60 cells/mm3/year). The majority of the LTSs had detectable, variable, and in some individuals, quite high (>10(4) RNA copies/ml) plasma viral load during the study period. In a cross-sectional analysis, HIV-specific CTL activity to HIV Gag, Pol, and Env proteins was detectable in all 17 LTSs. Simultaneous analysis of HIV-1 Gag-Pol, and Env-specific CTLs and virus load in protease inhibitor-naive individuals showed a significant inverse correlation between Pol-specific CTL activity and plasma HIV-1 RNA levels (p = 0.001). Furthermore, using a mixed linear effects model the combined effects of HIV-1 Pol- and Env-specific CTL activity on the viral load were significantly stronger than the effects of HIV-1 Pol-specific CTL activity alone on predicted virus load. These data suggest that the presence of HIV-1-specific CTL activity in HIV-1-infected long-term survivors is an important component in the effective control of HIV-1 replication.}, number={13}, journal={AIDS RESEARCH AND HUMAN RETROVIRUSES}, author={Betts, MR and Krowka, JF and Kepler, TB and Davidian, M and Christopherson, C and Kwok, S and Louie, L and Eron, J and Sheppard, H and Frelinger, JA}, year={1999}, month={Sep}, pages={1219–1228} }
 @inbook{davidian_1999, place={New York}, title={Invited discussion of “The Bayesian approach to population pharmacokinetic-pharmacodynamic modeling” by Wakefield, Aarons, and Racine-Poon}, volume={IV}, booktitle={Case Studies in Bayesian Statistics}, publisher={Springer-Verlag}, author={Davidian, M.}, editor={Gatsonis, C. and Kass, R.E. and Carlin, B. and Carriquiry, A. and Gelman, A. and Verdinelli, I. and West, M.Editors}, year={1999}, pages={257–263} }
 @article{hu_tsiatis_davidian_1998, title={Estimating the parameters in the Cox model when covariate variables are measured with error}, volume={54}, ISSN={["0006-341X"]}, DOI={10.2307/2533667}, abstractNote={The Cox proportional hazards model is commonly used to model survival data as a function of covariates. Because of the measuring mechanism or the nature of the environment, covariates are often measured with error and are not directly observable. A naive approach is to use the observed values of the covariates in the Cox model, which usually produces biased estimates of the true association of interest. An alternative strategy is to take into account the error in measurement, which may be carried out for the Cox model in a number of ways. We examine several such approaches and compare and contrast them through several simulation studies. We introduce a likelihood-based approach, which we refer to as the semiparametric method, and show that this method is an appealing alternative. The methods are applied to analyze the relationship between survival and CD4 count in patients with AIDS.}, number={4}, journal={BIOMETRICS}, author={Hu, P and Tsiatis, AA and Davidian, M}, year={1998}, month={Dec}, pages={1407–1419} }
 @article{smith_evans_davidian_1998, title={Statistical properties of fitted estimates of apparent in vivo metabolic constants obtained from gas uptake data. I. Lipophilic and slowly metabolized VOCs}, volume={10}, number={5}, journal={Inhalation Toxicology}, author={Smith, A. E. and Evans, M. V. and Davidian, M.}, year={1998}, pages={383–409} }
 @article{higgins_davidian_chew_burge_1998, title={The effect of serial dilution error on calibration inference in immunoassay}, volume={54}, ISSN={["0006-341X"]}, DOI={10.2307/2533992}, abstractNote={A common practice in immunoassay is the use of sequential dilutions of an initial stock solution of the antigen of interest to obtain standard samples in a desired concentration range. Nonlinear, heteroscedastic regression models are a common framework for analysis, and the usual methods for fitting the model assume that measured responses on the standards are independent. However, the dilution procedure introduces a propagation of random measurement error that may invalidate this assumption. We demonstrate that failure to account for serial dilution error in calibration inference on unknown samples leads to serious inaccuracy of assessments of assay precision such as confidence intervals and precision profiles. Techniques for taking serial dilution error into account based on data from multiple assay runs are discussed and are shown to yield valid calibration inferences.}, number={1}, journal={BIOMETRICS}, author={Higgins, KM and Davidian, M and Chew, G and Burge, H}, year={1998}, month={Mar}, pages={19–32} }
 @article{higgins_davidian_giltinan_1997, title={A two-step approach to measurement error in time-dependent covariates in nonlinear mixed-effects models, with application to IGF-I pharmacokinetics}, volume={92}, DOI={10.1080/01621459.1997.10473995}, abstractNote={Abstract The usual approach to the analysis of population pharmacokinetic studies is to represent the concentration-time data by a nonlinear mixed-effects model. Primary objectives are to characterize the pattern of drug disposition in the population and to identify individual-specific covariates associated with pharmacokinetic behavior. We consider data from a study of insulin-like growth factor I (IGF-I) administered by intravenous infusion to patients with severe head trauma. Failure to maintain steady-state levels of IGF-I was thought to be related to the temporal pattern of several covariates measured in the study, and an analysis investigating this issue was of interest. Observations on these potentially relevant covariates for each subject were made at time points different from those at which IGF-I concentrations were determined; moreover, the covariates themselves were likely subject to measurement error. The usual approach to time-dependent covariates in population analysis is to invoke a simple...}, number={438}, journal={Journal of the American Statistical Association}, author={Higgins, K. M. and Davidian, Marie and Giltinan, D. M.}, year={1997}, pages={436–448} }
 @article{zeng_davidian_1997, title={Bootstrap-Adjusted Calibration Confidence Intervals for Immunoassay}, volume={92}, ISSN={0162-1459 1537-274X}, url={http://dx.doi.org/10.1080/01621459.1997.10473625}, DOI={10.1080/01621459.1997.10473625}, abstractNote={Abstract In immunoassay, a nonlinear heteroscedastic regression model is used to characterize assay concentration-response, and the model fitted to data from standard samples is used to calibrate unknown test samples. Usual large-sample methods to construct individual confidence intervals for calibrated concentrations have been observed in empirical studies to be seriously inaccurate in terms of achieving the nominal level of coverage. We show theoretically that this inaccuracy is due largely to estimation of parameters characterizing assay response variance. By exploiting the theory, we propose a bootstrap procedure to adjust the usual intervals to achieve a higher degree of accuracy. We provide both theoretical results and simulation evidence to show that the proposed method attains the nominal level. A practical advantage of the procedure is that it may be implemented reliably using far fewer bootstrap samples than are needed in other resampling schemes.}, number={437}, journal={Journal of the American Statistical Association}, publisher={Informa UK Limited}, author={Zeng, Qi and Davidian, Marie}, year={1997}, month={Mar}, pages={278–290} }
 @article{zeng_davidian_1997, title={Calibration inference based on multiple runs of an immunoassay}, volume={53}, ISSN={["0006-341X"]}, DOI={10.2307/2533499}, abstractNote={Several authors have documented the poor performance of usual large-sample, individual calibration confidence intervals based on a single run of an immunoassay. Inaccuracy of these intervals may be attributed to the paucity of information on model parameters available in a single run. Methods for combining information from multiple runs to estimate assay response variance parameters and to refine characterization of the standard curve for the current run via empirical Bayes techniques have been proposed. We investigate formally the utility of these techniques for improving the quality of routine individual calibration inference.}, number={4}, journal={BIOMETRICS}, author={Zeng, Q and Davidian, M}, year={1997}, month={Dec}, pages={1304–1317} }
 @article{zeng_davidian_1997, title={Testing homogeneity of intra-run variance parameters in immunoassay}, volume={16}, ISSN={["0277-6715"]}, DOI={10.1002/(SICI)1097-0258(19970815)16:15<1765::AID-SIM603>3.0.CO;2-P}, abstractNote={A common assumption in the analysis of immunoassay data is a similar pattern of within-run variation across runs of the assays. One makes this assumption without formal investigation of its validity, despite the widely acknowledged fact that accurate understanding of intra-run variation is critical to reliable calibration inference. We propose a simple procedure for a formal test of the assumption of the homogeneity of parameters that characterize intra-run variation based on representation of standard curve data from multiple assay runs by a non-linear mixed effects model. We examine the performance of the procedure and investigate the robustness of calibration inference to incorrect assumptions about the pattern of intra-run variation.}, number={15}, journal={STATISTICS IN MEDICINE}, author={Zeng, Q and Davidian, M}, year={1997}, month={Aug}, pages={1765–1776} }
 @article{wang_davidian_1996, title={A note on covariate measurement error in nonlinear mixed effects models}, volume={83}, ISSN={0006-3444 1464-3510}, url={http://dx.doi.org/10.1093/biomet/83.4.801}, DOI={10.1093/biomet/83.4.801}, abstractNote={SUMMARY Little is known about the effects of measurement error in intra-individual covariates on inference for the nonlinear mixed effects model. We investigate this issue for a controlled variable measurement error model, and find that a major consequence may be substantial bias in estimates of parameters characterising intra-individual variation. Estimation of population parameters may also be affected. The dramatic effect of measurement error on estimation of intra-individual variance parameters has implications not only in the repeated measurement context, but also in individual nonlinear regression models.}, number={4}, journal={Biometrika}, publisher={Oxford University Press (OUP)}, author={Wang, N. and Davidian, M.}, year={1996}, month={Dec}, pages={801–812} }
 @article{liu_foegeding_wang_smith_davidian_1996, title={Denaturation and Aggregation of Chicken Myosin Isoforms}, volume={44}, ISSN={0021-8561 1520-5118}, url={http://dx.doi.org/10.1021/jf9503422}, DOI={10.1021/jf9503422}, abstractNote={Heat-induced denaturation and aggregation of chicken myosins isolated from one white muscle (pectoralis) and two red muscles (iliotibialis and gastrocnemius) were investigated using differential scanning calorimetry and turbidity (OD350nm) measurements. Peptide mapping showed that the three myosins had different heavy chain isoforms. Pectoralis and iliotibialis myosins had higher calorimetric enthalpies and a higher denaturation onset temperature than gastrocnemius myosin. Aggregation occurred for the pectoralis myosin at 45 °C, while the red muscle myosins required T > 45 °C for aggregation. The rates of aggregation were rapid at lower temperatures and then decreased as temperature increased. The red muscle myosins were more similar in peptide maps in comparison to the white muscle myosin; however, there were unique denaturation and aggregation properties for each type of myosin. Keywords: Myosin; aggregation; denaturation; muscle protein}, number={6}, journal={Journal of Agricultural and Food Chemistry}, publisher={American Chemical Society (ACS)}, author={Liu, Martha N. and Foegeding, E. Allen and Wang, Shue-Fung and Smith, Denise M. and Davidian, Marie}, year={1996}, month={Jan}, pages={1435–1440} }
 @article{trettin_davidian_jurgensen_lea_1996, title={Organic Matter Decomposition following Harvesting and Site Preparation of a Forested Wetland}, volume={60}, ISSN={0361-5995}, url={http://dx.doi.org/10.2136/sssaj1996.03615995006000060053x}, DOI={10.2136/sssaj1996.03615995006000060053x}, abstractNote={Abstract}, number={6}, journal={Soil Science Society of America Journal}, publisher={Wiley}, author={Trettin, C. C. and Davidian, M. and Jurgensen, M. F. and Lea, R.}, year={1996}, month={Nov}, pages={1994–2003} }
 @article{jacobson_davidian_rainey_hafner_raasch_luft_1996, title={Pyrimethamine pharmacokinetics in human immunodeficiency virus-positive patients seropositive for Toxoplasma gondii.}, volume={40}, ISSN={0066-4804 1098-6596}, url={http://dx.doi.org/10.1128/aac.40.6.1360}, DOI={10.1128/aac.40.6.1360}, abstractNote={Pyrimethamine pharmacokinetics were studied in 11 human immunodeficiency virus (HIV)-positive patients who were seropositive for exposure to Toxoplasma gondii and were taking zidovudine (AIDS Clinical Trials Group Protocol 102). Pyrimethamine was administered at 50 mg daily for 3 weeks to achieve steady state, and pharmacokinetic profiles were determined after administration of the last dose. Noncompartmental and compartmental analyses were performed. Population pharmacokinetic analysis assuming a one-compartment model yielded the following estimates: area under the 24-h concentration-time curve, 42.7 +/- 12.3 micrograms.h/ml; halflife, 139 +/- 34 h; clearance, 1.28 +/- 0.41 liters/h; volume of distribution, 246 +/- 641; and absorption rate constant, 1.5 +/- 1.3 liters/h. These values are similar to those seen in subjects without HIV infection. Pyrimethamine pharmacokinetics did not differ significantly in those subjects who were intravenous drug users. Adverse effects were noted in 73% of those initially enrolled in this study, leading to discontinuation for 38%. No association was noted between pyrimethamine levels and the incidence of adverse events. No significant differences were seen in zidovudine pharmacokinetic parameters obtained from studies performed before and during treatment with pyrimethamine. In summary, pyrimethamine exhibited pharmacokinetics in HIV-infected patients that were similar to those in non-HIV-infected subjects and it did not alter the pharmacokinetics of zidovudine in these patients.}, number={6}, journal={Antimicrobial Agents and Chemotherapy}, publisher={American Society for Microbiology}, author={Jacobson, J M and Davidian, M and Rainey, P M and Hafner, R and Raasch, R H and Luft, B J}, year={1996}, month={Jun}, pages={1360–1365} }
 @article{belanger_davidian_giltinan_1996, title={The Effect of Variance Function Estimation on Nonlinear Calibration Inference in Immunoassay Data}, volume={52}, ISSN={0006-341X}, url={http://dx.doi.org/10.2307/2533153}, DOI={10.2307/2533153}, abstractNote={Often with data from immunoassays, the concentration-response relationship is nonlinear and intra-assay response variance is heterogeneous. Estimation of the standard curve is usually based on a nonlinear heteroscedastic regression model for concentration-response, where variance is modeled as a function of mean response and additional variance parameters. This paper discusses calibration inference for immunoassay data which exhibit this nonlinear heteroscedastic mean-variance relationship. An assessment of the effect of variance function estimation in three types of approximate large-sample confidence intervals for unknown concentrations is given by theoretical and empirical investigation and application to two examples. A major finding is that the accuracy of such calibration intervals depends critically on the nature of response variance and the quality with which variance parameters are estimated.}, number={1}, journal={Biometrics}, publisher={JSTOR}, author={Belanger, Bruce A. and Davidian, Marie and Giltinan, David M.}, year={1996}, month={Mar}, pages={158} }
 @book{davidian_giltinan_1995, place={Boca Raton, FL}, series={Monographs on statistics and applied probability}, title={Nonlinear models for repeated measurement data}, ISBN={9780412983412}, journal={Chapman & Hall/CRC}, publisher={Boca Raton: Chapman & Hall}, author={Davidian, M. and Giltinan, D. M.}, year={1995}, collection={Monographs on statistics and applied probability} }
 @article{nelson_sellon_novotney_devera_davidian_english_tompkins_tompkins_1995, title={Therapeutic effects of diethylcarbamazine and 3′-azido-3′-deoxythymidine on feline leukemia virus lymphoma formation}, volume={46}, ISSN={0165-2427}, url={http://dx.doi.org/10.1016/0165-2427(94)07017-2}, DOI={10.1016/0165-2427(94)07017-2}, abstractNote={Twenty-four specific pathogen-free kittens were infected with the Rickard strain of feline leukemia virus (FeLVR). The kittens were divided into four equal groups and were orally administered either a high dose of diethylcarbamazine (DECH, 12 mg kg−1), a low dose of diethylcarbamazine (DECL, 3 mg kg−1), 3′-azido-3′-deoxythymidine (AZT, 15 mg kg−1, b.i.d.), or a placebo (250 mg granular dextrose) daily for 10 weeks. Blood was collected at 2-week intervals for complete blood counts (CBC) and flow cytometric analysis (FACS) of peripheral blood lymphocytes (PBL). Plasma was assayed for antibodies to FeLV gp70 and for FeLV p27 antigen using ELISA assays. For FACS analysis, lymphocytes were incubated with monoclonal antibodies to feline Pan T, CD8+, CD4+, and B cell (Anti-Ig) antigens. In the placebo treated cats, FeLVR infection caused an early (2 weeks p.i.) and persistent decrease in leukocyte numbers attributable primarily to a decrease in neutrophil numbers and a secondary lesser decrease in B and CD4+ lymphocyte numbers. The DEC-treated groups showed a delayed but similar leukopenia by 4 weeks p.i. The lymphopenia in the DEC groups (primarily B cells and CD4+ cells) was reversed by 10 weeks p.i., but the neutropenia persisted. AZT treatment inhibited FeLVR-induced lymphopenia but did not prevent a reduction in neutrophil numbers. A marked p27 antigenemia that peaked at 4 weeks p.i. was noted in the placebo treated cats and in most cats (1112) treated with either dose of DEC. However, AZT significantly inhibited the p27 antigenemia and all cats were negative for p27 antigen between 6 and 10 weeks of treatment. In general, placebo treated cats as well as DECH and DECL cats had low levels of antibody to gp70 throughout the study, suggesting FeLVR-induced immunosuppression. In contrast, significantly higher titers of anti-gp70 antibodies were seen in AZT-treated cats at 6 weeks p.i., and were maintained throughout treatment. Eighteen month survival rates provide efficacy data for AZT as well as both DEC treatment groups. While all placebo treated cats were euthanized by 52 weeks p.i. due to FeLV associated lymphomas with a mean survival time of 35.5 weeks p.i., median survival time of the AZT treated group was ≥ 102 weeks p.i., while that of the DECH and DECL groups was 69.7 and 72 weeks p.i., respectively. Thus, DEC as well as AZT therapy delays the development of lymphomas associated with FeLV infection and significantly improves survival.}, number={1-2}, journal={Veterinary Immunology and Immunopathology}, publisher={Elsevier BV}, author={Nelson, Phillip and Sellon, Rance and Novotney, Carol and Devera, Cristina and Davidian, Marie and English, Robert and Tompkins, Mary and Tompkins, Wayne}, year={1995}, month={May}, pages={181–194} }
 @article{giltinan_davidian_1994, title={Assays for recombinant proteins: A problem in non-linear calibration}, volume={13}, ISSN={0277-6715 1097-0258}, url={http://dx.doi.org/10.1002/sim.4780131107}, DOI={10.1002/sim.4780131107}, abstractNote={Abstract}, number={11}, journal={Statistics in Medicine}, publisher={Wiley}, author={Giltinan, David M. and Davidian, Marie}, year={1994}, month={Jun}, pages={1165–1179} }
 @article{noga_engel_arroll_mckenna_davidian_1994, title={Low serum antibacterial activity coincides with increased prevalence of shell disease in blue crabs Callinectes sapidus}, volume={19}, ISSN={0177-5103 1616-1580}, url={http://dx.doi.org/10.3354/dao019121}, DOI={10.3354/dao019121}, abstractNote={We provide epidemiological evidence that shell disease, a very common problem of crustaceans in polluted environments, coincides with low serum antibacterial activity. In commercially important blue crabs Callinectes sapidus from the Albemarle-Pamlico Estuary, North Carolina, USA, we identified potent antibacterial activity which was active against most of the bacteria inhabiting the shell of both clinically normal crabs as well as those with shell disease. Clinically normal blue crabs from riverine sites, presumed to be at high risk for developing shell disease, had as little as one-fifth the antibacterial activity of crabs from more oceanic control sites, which had low prevalence of shell disease. Crabs with shell disease had lower serum antibacterial activity than cohorts without shell disease from the same geographic site. This antibacterial activity may be an important mechanism protecting crabs against shell disease and may be a useful biomarker of blue crab health.}, number={2}, journal={Diseases of Aquatic Organisms}, publisher={Inter-Research Science Center}, author={Noga, EJ and Engel, DP and Arroll, TW and McKenna, S and Davidian, M}, year={1994}, pages={121–128} }
 @article{yuh_beal_davidian_harrison_hester_kowalski_vonesh_wolfinger_1994, title={Population Pharmacokinetic/Pharmacodynamic Methodology and Applications: A Bibliography}, volume={50}, ISSN={0006-341X}, url={http://dx.doi.org/10.2307/2533402}, DOI={10.2307/2533402}, abstractNote={This bibliography lists all published methodological works (statistical methodology, implementation methodology, review papers, descriptions of software) in the area of population pharmacokinetics/pharmacodynamics up to 1993 and all published works describing applications of population pharmacokinetics/pharmacodynamics up to 1992.}, number={2}, journal={Biometrics}, publisher={JSTOR}, author={Yuh, Lianng and Beal, Stuart and Davidian, Marie and Harrison, Ferrin and Hester, Allen and Kowalski, Kenneth and Vonesh, Edward and Wolfinger, Russell}, year={1994}, month={Jun}, pages={566} }
 @article{davidian_giltinan_1993, title={Analysis of repeated measurement data using the nonlinear mixed effects model}, volume={20}, ISSN={0169-7439}, url={http://dx.doi.org/10.1016/0169-7439(93)80017-c}, DOI={10.1016/0169-7439(93)80017-c}, abstractNote={Abstract Davidian, M. and Giltinan, D.M., 1993. Analysis of repeated measurement data using the nonlinear mixed effects model. Chemometrics and Intelligent Laboratory Systems, 20: 1–24. Situations in which repeated measurements are taken on each of several individual items arise in many areas. These include assay development, where concentration—response data are available for each assay run in a series of assay experiments; pharmacokinetic analysis, where repeated blood concentration measurements are obtained from each of several subjects; and growth or decay studies, where growth or decay are measured over time for each plant, animal, or some other experimental unit. In these situations the model describing the response is often nonlinear in the parameters to be estimated, as is the case for the four-parameter logistic model, which is frequently used to characterize concentration—response relationships for radioimmunoassay enzyme-linked immunosorbent assay. Furthermore, response variability typically increases with level of response. The objectives of an analysis vary according to the application: for assay analysis, calibration of unknowns for the most recent run may be of interest; in pharmacokinetics, characterization of drug disposition for a patient population may be the focus. The nonlinear mixed effects (NME) model has been used to describe repeated measurement data for which the mean response function is nonlinear. In this tutorial, the NME model is motivated and described, and several methods are given for estimation and inference in the context of the model. The methods are illustrated by application to examples from the fields of water transport kinetics, assay development, and pharmacokinetics.}, number={1}, journal={Chemometrics and Intelligent Laboratory Systems}, publisher={Elsevier BV}, author={Davidian, Marie and Giltinan, David M.}, year={1993}, month={Aug}, pages={1–24} }
 @article{davidian_giltinan_1993, title={Some Simple Methods for Estimating Intraindividual Variability in Nonlinear Mixed Effects Models}, volume={49}, ISSN={0006-341X}, url={http://dx.doi.org/10.2307/2532602}, DOI={10.2307/2532602}, abstractNote={Methods are proposed to improve both population and individual inference within the nonlinear random coefficient regression framework by incorporating possible heterogeneity in the intraindividual variance structure. These methods extend existing variance function estimation techniques by pooling information across individuals. The methods are appropriate when it is reasonable to assume that there exists a common intraindividual variance structure.}, number={1}, journal={Biometrics}, publisher={JSTOR}, author={Davidian, Marie and Giltinan, David M.}, year={1993}, month={Mar}, pages={59} }
 @article{davidian_giltinan_1993, title={Some general estimation methods for nonlinear mixed-effects model}, volume={3}, ISSN={1054-3406 1520-5711}, url={http://dx.doi.org/10.1080/10543409308835047}, DOI={10.1080/10543409308835047}, abstractNote={A nonlinear mixed-effects model suitable for characterizing repeated measurement data is described. The model allows dependence of random coefficients on covariate information and accommodates general specifications of a common intraindividual covariance structure, such as models for variance within individuals that depend on individual mean response and autocorrelation. Two classes of procedures for estimation in this model are described, which incorporate estimation of unknown parameters in the assumed intraindividual covariance structure. The procedures are straightforward to implement using standard statistical software. The techniques are illustrated by examples in growth analysis and assay development.}, number={1}, journal={Journal of Biopharmaceutical Statistics}, publisher={Informa UK Limited}, author={Davidian, Marie and Giltinan, David M.}, year={1993}, month={Jan}, pages={23–55} }
 @article{davidian_gallant_1993, title={The nonlinear mixed effects model with a smooth random effects density}, volume={80}, ISSN={0006-3444 1464-3510}, url={http://dx.doi.org/10.1093/biomet/80.3.475}, DOI={10.1093/biomet/80.3.475}, abstractNote={The fixed parameters of the nonlinear mixed effects model and the density of the random effects are estimated jointly by maximum likelihood. The density of the random effects is assumed to be smooth but is otherwise unrestricted. The method uses a series expansion that follows from the smoothness assumption to represent the density and quadrature to compute the likelihood. Standard algorithms are used for optimization. Empirical Bayes estimates of random coefficients are obtained by computing posterior modes. The method is applied to data from pharmacokinetics, and properties of the method are investigated by application to simulated data. Key Words: Maximum likelihood; nonlinear mixed effects models; nonparametric; pharmacokinetics.}, number={3}, journal={Biometrika}, publisher={Oxford University Press (OUP)}, author={Davidian, Marie and Gallant, A. Ronald}, year={1993}, pages={475–488} }
 @article{davidian_gallant_1992, title={Smooth nonparametric maximum likelihood estimation for population pharmacokinetics, with application to quinidine}, volume={20}, ISSN={0090-466X}, url={http://dx.doi.org/10.1007/bf01061470}, DOI={10.1007/bf01061470}, abstractNote={The seminonparametric (SNP) method, popular in the econometrics literature, is proposed for use in population pharmacokinetic analysis. For data that can be described by the nonlinear mixed effects model, the method produces smooth nonparametric estimates of the entire random effects density and simultaneous estimates of fixed effects by maximum likelihood. A graphical model-building strategy based on the SNP method is described. The methods are illustrated by a population analysis of plasma levels in 136 patients undergoing oral quinidine therapy.}, number={5}, journal={Journal of Pharmacokinetics and Biopharmaceutics}, publisher={Springer Science and Business Media LLC}, author={Davidian, Marie and Gallant, A. Ronald}, year={1992}, month={Oct}, pages={529–556} }
 @book{rives_davidian_ley_1991, title={Infectious bursal disease virus titers may be misleading}, volume={15}, number={2}, journal={Breakthrough, North Carolina Cooperative Extension Service}, author={Rives, D.V. and Davidian, M. and Ley, D.H.}, year={1991} }
 @inproceedings{davidian_gupta_1991, place={Atlanta, Georgia}, title={The use of regression analysis in nonwovens research}, booktitle={Proceedings of the TAPPI 1991 Nonwovens Conference}, publisher={TAPPI Press}, author={Davidian, M. and Gupta, B.S.}, year={1991}, pages={27–33} }
 @article{davidian_1990, title={Estimation of variance functions in assays with possibly unequal replication and nonnormal data}, volume={77}, ISSN={0006-3444 1464-3510}, url={http://dx.doi.org/10.1093/biomet/77.1.43}, DOI={10.1093/biomet/77.1.43}, abstractNote={SUMMARY Estimation of parametric variance functions using transformations of standard deviations based on replication at each design point is common in, but not limited to, assay analysis. It is shown that ignoring unequal replication can lead to bias and inefficiency in estimation. Efficiency comparisons for different transformations for nonnormal distributions are given. A method to account for bias is described that can offer robustness to nonnormality and leads to a comparison of Gini's mean difference to sample standard deviation. A method for computing all of these estimators using standard software is described.}, number={1}, journal={Biometrika}, publisher={Oxford University Press (OUP)}, author={Davidian, M.}, year={1990}, month={Mar}, pages={43–54} }
 @article{davidian_haaland_1990, title={Regression and calibration with nonconstant error variance}, volume={9}, ISSN={0169-7439}, url={http://dx.doi.org/10.1016/0169-7439(90)80074-g}, DOI={10.1016/0169-7439(90)80074-g}, abstractNote={Davidian, M. and Haaland, P., 1990. Regression and calibration with nonconstant error variance. Chemometrics and Intelligent Laboratory Systems, 9: 231–248. Ordinary least squares regression analysis is generally inappropriate for calibration and regression problems when the usual assumption of constant variance across all observations does not hold. Estimators of regression parameters are of relatively poor quality and the resulting inference can be misleading. The use of standard data transformations is a common alternative but may not provide enough flexibility for some cases. The use of weighted regression with weights estimated from replicates is generally unreliable for reasonable sample sizes. However, when the error variance changes systematically with the mean response or other variables, generalized least squares (GLS) and variance function estimation (VFE) methods can be used. The GLS-VFE approach allows the experimenter to specify a model for the systematic change in variance, estimate unknown parameters, and to use this information to provide more efficient estimates of the regression parameters. In this tutorial, GLS-VFE methods are introduced and described in the context of regression and calibration. An example of calibration for a chemical assay is used to motivate discussion and illustrate the implementation of these methods using standard software packages.}, number={3}, journal={Chemometrics and Intelligent Laboratory Systems}, publisher={Elsevier BV}, author={Davidian, Marie and Haaland, Perry D.}, year={1990}, month={Dec}, pages={231–248} }
 @article{davidian_carroll_1988, title={A Note on Extended Quasi-Likelihood}, volume={50}, ISSN={0035-9246}, url={http://dx.doi.org/10.1111/j.2517-6161.1988.tb01712.x}, DOI={10.1111/j.2517-6161.1988.tb01712.x}, abstractNote={SUMMARY}, number={1}, journal={Journal of the Royal Statistical Society: Series B (Methodological)}, publisher={Wiley}, author={Davidian, M. and Carroll, R. J.}, year={1988}, month={Sep}, pages={74–82} }
 @article{davidian_carroll_smith_1988, title={Variance functions and the minimum detectable concentration in assays}, volume={75}, ISSN={0006-3444 1464-3510}, url={http://dx.doi.org/10.1093/biomet/75.3.549}, DOI={10.1093/biomet/75.3.549}, abstractNote={Abstract : Assay data are often fit by a nonlinear regression model incorporating heterogeneity of variance, as ion radioimmunoassay, for example. Typically, the standard deviation of the response is taken to be proportional to a power theta of the mean. There is considerable empirical evidence suggesting that for assays of a reseasonable size, how one estimates the parameter theta does not greatly affect how well one estimates the the mean regression function. An additional component of assay analysis is the estimation of auxillary constructs such as the minimum detectable concentration, for which many definitions exist; we focus on one such definition. The minimum detectable concentration depends both on theta and the mean regression function. We compare three standard method of estimating the parameter theta due to Rodbard (1978), Raab (1981a) and Carroll and Ruppert (1982b). When duplicate counts are taken at each concentration, the first method is only 20% efficient asymptotically in comparison to the third, and the resulting estimate of the minimum detectable concentration is asymptotically 3.3 times more variable for first than the third. Less dramatic results obtain for the second estimator compared to the third; this estimator is still not efficient, however. Simulation results and an example are supportive of the asymptotic theory. Keywords: Least squares method.}, number={3}, journal={Biometrika}, publisher={Oxford University Press (OUP)}, author={Davidian, M. and Carroll, R. J. and Smith, W.}, year={1988}, pages={549–556} }
 @article{davidian_carroll_1987, title={VARIANCE FUNCTION ESTIMATION}, volume={82}, ISSN={["0162-1459"]}, DOI={10.2307/2289384}, abstractNote={Abstract Heteroscedastic regression models are used in fields including economics, engineering, and the biological and physical sciences. Often, the heteroscedasticity is modeled as a function of the covariates or the regression and other structural parameters. Standard asymptotic theory implies that how one estimates the variance function, in particular the structural parameters, has no effect on the first-order properties of the regression parameter estimates; there is evidence, however, both in practice and higher-order theory to suggest that how one estimates the variance function does matter. Further, in some settings, estimation of the variance function is of independent interest or plays an important role in estimation of other quantities. In this article, we study variance function estimation in a unified way, focusing on common methods proposed in the statistical and other literature, to make both general observations and compare different estimation schemes. We show that there are significant differences in both efficiency and robustness for many common methods. We develop a general theory for variance function estimation, focusing on estimation of the structural parameters and including most methods in common use in our development. The general qualitative conclusions are these. First, most variance function estimation procedures can be looked upon as regressions with “responses” being transformations of absolute residuals from a preliminary fit or sample standard deviations from replicates at a design point. Our conclusion is that the former is typically more efficient, but not uniformly so. Second, for variance function estimates based on transformations of absolute residuals, we show that efficiency is a monotone function of the efficiency of the fit from which the residuals are formed, at least for symmetric errors. Our conclusion is that one should iterate so that residuals are based on generalized least squares. Finally, robustness issues are of even more importance here than in estimation of a regression function for the mean. The loss of efficiency of the standard method away from the normal distribution is much more rapid than in the regression problem. As an example of the type of model and estimation methods we consider, for observation-covariate pairs (Yi, xi ), one may model the variance as proportional to a power of the mean response, for example, Where f(xi , β) is the possibly nonlinear mean function and θ is the structural parameter of interest. “Regression methods” for estimation of θ and σ based on residuals for some regression fit involve minimizing a sum of squares where some function T of the ‖ri ‖ plays the role of the “responses” and an appropriate function of the variance plays the role of the “regression function.” For example, if T(x) = x 2, the responses would be ri 2, and the form of the regression function would be suggested by the aproximate fact . One could weight the sum of squares appropriately by considering the approximate variance of ri 2. For the case of replication at each xi , some methods suggest replacing the ri , in the function T by sample standard deviations at each xi . Other functions T, such as T(x) = x or log x have also been proposed.}, number={400}, journal={JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION}, author={DAVIDIAN, M and CARROLL, RJ}, year={1987}, month={Dec}, pages={1079–1091} }