@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{liu_clifton_laber_drake_fang_2023, title={Deep Spatial Q-Learning for Infectious Disease Control}, ISSN={["1537-2693"]}, DOI={10.1007/s13253-023-00551-4}, journal={JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS}, author={Liu, Zhishuai and Clifton, Jesse and Laber, Eric B. and Drake, John and Fang, Ethan X.}, year={2023}, month={Jul} }
 @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{mustanski_saber_macapagal_matson_laber_rodrgiuez-diaz_moran_carrion_moskowitz_newcomb_2022, title={Effectiveness of the SMART Sex Ed program among 13-18 year old English and Spanish speaking adolescent men who have sex with men}, ISSN={["1573-3254"]}, DOI={10.1007/s10461-022-03806-2}, abstractNote={Adolescent men who have sex with men (AMSM) have a high HIV incidence and low utilization of testing and prevention services. However, very few HIV prevention programs exist that focus on the unique sexual health needs of AMSM. SMART is a stepped care package of eHealth interventions that comprehensively address the sexual and HIV prevention needs of AMSM. This study examines the impact of the first step of SMART, "SMART Sex Ed," on 13- to 18-year-old AMSM (n = 983) from baseline to three-month follow-up across 18 separate outcomes measuring HIV prevention attitudes, skills, and behaviors. We observed significant change from baseline to three-month post-intervention in nine HIV-related outcomes (e.g., receipt of HIV and STI test, HIV knowledge), as well as largely consistent effects across demographic subgroups (e.g., race, age, rural, low SES). Analyses observed no effects on condom use behaviors. SMART Sex Ed shows promise as an effective sexual health education program for diverse AMSM.Los adolescentes hombres que tienen sexo con otros hombres (AHSH) experimentan alta incidencia del VIH y baja utilización de servicios de prueba y prevención. Sin embargo, existen muy pocos programas de prevención del VIH enfocados en las necesidades particulares para la salud sexual de AHSH. SMART es un paquete de intervenciones de cuidado escalonado que usa plataformas electrónicas (eHealth) y que atiende de forma integrada las necesidades de salud sexual y prevención del VIH de AHSH. Este estudio examina el impacto de la primera etapa de SMART, llamada “SMART Sex Ed”, entre AHSH (n = 983) entre las edades de 13 a 18 años e integra datos desde el reclutamiento con seguimiento cada 3 meses. Se recopilaron datos de 18 indicadores de actitudes, destrezas y prácticas de prevención del VIH (Ej. Historial de pruebas de VIH o ITS; conocimiento sobre VIH), así como los efectos en diferentes grupos demográficos (Ej. Raza, edad, área rural, y bajo nivel socioeconómico). Los análisis realizados demuestran que las características demográficas no tienen efecto en las prácticas de uso de condón. SMART Sex Ed es una intervención prometedora para educación sexual efectiva para AHSH.}, journal={AIDS AND BEHAVIOR}, author={Mustanski, Brian and Saber, Rana and Macapagal, Kathryn and Matson, Maggie and Laber, Eric and Rodrgiuez-Diaz, Carlos and Moran, Kevin O. and Carrion, Andres and Moskowitz, David A. and Newcomb, Michael E.}, year={2022}, month={Aug} }
 @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{guan_reich_laber_2021, title={A spatiotemporal recommendation engine for malaria control}, volume={4}, ISSN={["1468-4357"]}, DOI={10.1093/biostatistics/kxab010}, abstractNote={Summary}, journal={BIOSTATISTICS}, author={Guan, Qian and Reich, Brian J. and Laber, Eric B.}, year={2021}, month={Apr} }
 @article{kosorok_laber_small_zeng_2021, title={Introduction to the Theory and Methods Special Issue on Precision Medicine and Individualized Policy Discovery}, volume={116}, ISSN={["1537-274X"]}, DOI={10.1080/01621459.2020.1863224}, abstractNote={Abstract We introduce the Theory and Methods Special Issue on Precision Medicine and Individualized Policy Discovery. The issue consists of four discussion papers, grouped into two pairs, and sixteen regular research papers that cover many important lines of research on data-driven decision making. We hope that the many provocative and original ideas presented herein will inspire further work and development in precision medicine and personalization.}, number={533}, journal={JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION}, author={Kosorok, Michael R. and Laber, Eric B. and Small, Dylan S. and Zeng, Donglin}, year={2021}, month={Mar}, pages={159–161} }
 @article{xu_laber_staicu_lascelles_2021, title={Novel approach to modeling high-frequency activity data to assess therapeutic effects of analgesics in chronic pain conditions}, volume={11}, ISSN={["2045-2322"]}, DOI={10.1038/s41598-021-87304-w}, abstractNote={Abstract}, number={1}, journal={SCIENTIFIC REPORTS}, author={Xu, Zekun and Laber, Eric and Staicu, Ana-Maria and Lascelles, B. Duncan X.}, year={2021}, month={Apr} }
 @article{luckett_laber_el-kamary_fan_jhaveri_perou_shebl_kosorok_2021, title={Receiver operating characteristic curves and confidence bands for support vector machines}, volume={77}, ISSN={["1541-0420"]}, DOI={10.1111/biom.13365}, abstractNote={Abstract}, number={4}, journal={BIOMETRICS}, author={Luckett, Daniel J. and Laber, Eric B. and El-Kamary, Samer S. and Fan, Cheng and Jhaveri, Ravi and Perou, Charles M. and Shebl, Fatma M. and Kosorok, Michael R.}, year={2021}, month={Dec}, pages={1422–1430} }
 @article{pan_laber_smith_zhao_2021, title={Reinforced Risk Prediction With Budget Constraint Using Irregularly Measured Data From Electronic Health Records}, ISSN={["1537-274X"]}, DOI={10.1080/01621459.2021.1978467}, abstractNote={Abstract Uncontrolled glycated hemoglobin (HbA1c) levels are associated with adverse events among complex diabetic patients. These adverse events present serious health risks to affected patients and are associated with significant financial costs. Thus, a high-quality predictive model that could identify high-risk patients so as to inform preventative treatment has the potential to improve patient outcomes while reducing healthcare costs. Because the biomarker information needed to predict risk is costly and burdensome, it is desirable that such a model collect only as much information as is needed on each patient so as to render an accurate prediction. We propose a sequential predictive model that uses accumulating patient longitudinal data to classify patients as: high-risk, low-risk, or uncertain. Patients classified as high-risk are then recommended to receive preventative treatment and those classified as low-risk are recommended to standard care. Patients classified as uncertain are monitored until a high-risk or low-risk determination is made. We construct the model using claims and enrollment files from Medicare, linked with patient electronic health records (EHR) data. The proposed model uses functional principal components to accommodate noisy longitudinal data and weighting to deal with missingness and sampling bias. The proposed method demonstrates higher predictive accuracy and lower cost than competing methods in a series of simulation experiments and application to data on complex patients with diabetes. Supplementary materials for this article are available online.}, journal={JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION}, author={Pan, Yinghao and Laber, Eric B. and Smith, Maureen A. and Zhao, Ying-Qi}, year={2021}, month={Nov} }
 @article{cloud_laber_2021, title={Variance Decompositions for Extensive-Form Games}, ISSN={["2325-4270"]}, DOI={10.1109/COG52621.2021.9619045}, abstractNote={The extent to which an individual or chance can influence the outcome of a game is a central question in the analysis of games. Consequently, the ability to characterize sources of variation in game outcomes may have significant implications in areas such as game design, law, and multi-agent reinforcement learning. We derive a closed-form expression and estimators for the variance in the outcome of a general multi-agent game that is attributable to a player or chance. We analyze poker hands to show that randomness in the cards dealt has surprisingly little influence on the outcomes of each hand. A simple example is given that demonstrates how variance decompositions can be used to measure other interesting properties of games.}, journal={2021 IEEE CONFERENCE ON GAMES (COG)}, author={Cloud, Alex and Laber, Eric B.}, year={2021}, pages={380–387} }
 @article{dong_laber_goldberg_song_yang_2020, title={Ascertaining properties of weighting in the estimation of optimal treatment regimes under monotone missingness}, volume={39}, ISSN={["1097-0258"]}, DOI={10.1002/sim.8678}, abstractNote={Dynamic treatment regimes operationalize precision medicine as a sequence of decision rules, one per stage of clinical intervention, that map up‐to‐date patient information to a recommended intervention. An optimal treatment regime maximizes the mean utility when applied to the population of interest. Methods for estimating an optimal treatment regime assume the data to be fully observed, which rarely occurs in practice. A common approach is to first use multiple imputation and then pool the estimators across imputed datasets. However, this approach requires estimating the joint distribution of patient trajectories, which can be high‐dimensional, especially when there are multiple stages of intervention. We examine the application of inverse probability weighted estimating equations as an alternative to multiple imputation in the context of monotonic missingness. This approach applies to a broad class of estimators of an optimal treatment regime including both Q‐learning and a generalization of outcome weighted learning. We establish consistency under mild regularity conditions and demonstrate its advantages in finite samples using a series of simulation experiments and an application to a schizophrenia study.}, number={25}, journal={STATISTICS IN MEDICINE}, author={Dong, Lin and Laber, Eric and Goldberg, Yair and Song, Rui and Yang, Shu}, year={2020}, month={Nov}, pages={3503–3520} }
 @article{guan_reich_laber_bandyopadhyay_2020, title={Bayesian Nonparametric Policy Search With Application to Periodontal Recall Intervals}, volume={115}, ISSN={["1537-274X"]}, DOI={10.1080/01621459.2019.1660169}, abstractNote={Abstract Tooth loss from periodontal disease is a major public health burden in the United States. Standard clinical practice is to recommend a dental visit every six months; however, this practice is not evidence-based, and poor dental outcomes and increasing dental insurance premiums indicate room for improvement. We consider a tailored approach that recommends recall time based on patient characteristics and medical history to minimize disease progression without increasing resource expenditures. We formalize this method as a dynamic treatment regime which comprises a sequence of decisions, one per stage of intervention, that follow a decision rule which maps current patient information to a recommendation for their next visit time. The dynamics of periodontal health, visit frequency, and patient compliance are complex, yet the estimated optimal regime must be interpretable to domain experts if it is to be integrated into clinical practice. We combine nonparametric Bayesian dynamics modeling with policy-search algorithms to estimate the optimal dynamic treatment regime within an interpretable class of regimes. Both simulation experiments and application to a rich database of electronic dental records from the HealthPartners HMO shows that our proposed method leads to better dental health without increasing the average recommended recall time relative to competing methods. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.}, number={531}, journal={JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION}, author={Guan, Qian and Reich, Brian J. and Laber, Eric B. and Bandyopadhyay, Dipankar}, year={2020}, month={Jul}, pages={1066–1078} }
 @article{luckett_laber_kahkoska_maahs_mayer-davis_kosorok_2020, title={Estimating Dynamic Treatment Regimes in Mobile Health Using V-Learning}, volume={115}, ISSN={["1537-274X"]}, DOI={10.1080/01621459.2018.1537919}, abstractNote={Abstract The vision for precision medicine is to use individual patient characteristics to inform a personalized treatment plan that leads to the best possible healthcare for each patient. Mobile technologies have an important role to play in this vision as they offer a means to monitor a patient’s health status in real-time and subsequently to deliver interventions if, when, and in the dose that they are needed. Dynamic treatment regimes formalize individualized treatment plans as sequences of decision rules, one per stage of clinical intervention, that map current patient information to a recommended treatment. However, most existing methods for estimating optimal dynamic treatment regimes are designed for a small number of fixed decision points occurring on a coarse time-scale. We propose a new reinforcement learning method for estimating an optimal treatment regime that is applicable to data collected using mobile technologies in an outpatient setting. The proposed method accommodates an indefinite time horizon and minute-by-minute decision making that are common in mobile health applications. We show that the proposed estimators are consistent and asymptotically normal under mild conditions. The proposed methods are applied to estimate an optimal dynamic treatment regime for controlling blood glucose levels in patients with type 1 diabetes.}, number={530}, journal={JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION}, author={Luckett, Daniel J. and Laber, Eric B. and Kahkoska, Anna R. and Maahs, David M. and Mayer-Davis, Elizabeth and Kosorok, Michael R.}, year={2020}, month={Apr}, pages={692–706} }
 @article{mustanski_moskowitz_moran_newcomb_macapagal_rodriguez-diaz_rendina_laber_li_matson_et al._2020, title={Evaluation of a Stepped-Care eHealth HIV Prevention Program for Diverse Adolescent Men Who Have Sex With Men: Protocol for a Hybrid Type 1 Effectiveness Implementation Trial of SMART}, volume={9}, ISSN={["1929-0748"]}, DOI={10.2196/19701}, abstractNote={
            Background
            Adolescent men who have sex with men (AMSM), aged 13 to 18 years, account for more than 80% of teen HIV occurrences. Despite this disproportionate burden, there is a conspicuous lack of evidence-based HIV prevention programs. Implementation issues are critical as traditional HIV prevention delivery channels (eg, community-based organizations, schools) have significant access limitations for AMSM. As such, eHealth interventions, such as our proposed SMART program, represent an excellent modality for delivering AMSM-specific intervention material where youth are.
          }, number={8}, journal={JMIR RESEARCH PROTOCOLS}, author={Mustanski, Brian and Moskowitz, David A. and Moran, Kevin O. and Newcomb, Michael E. and Macapagal, Kathryn and Rodriguez-Diaz, Carlos and Rendina, H. Jonathon and Laber, Eric B. and Li, Dennis H. and Matson, Margaret and et al.}, year={2020}, month={Aug} }
 @article{grantham_reich_laber_pacifici_dunn_fierer_gebert_allwood_faith_2020, title={Global forensic geolocation with deep neural networks}, volume={69}, ISSN={["1467-9876"]}, DOI={10.1111/rssc.12427}, abstractNote={Summary}, number={4}, journal={JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS}, author={Grantham, Neal S. and Reich, Brian J. and Laber, Eric B. and Pacifici, Krishna and Dunn, Robert R. and Fierer, Noah and Gebert, Matthew and Allwood, Julia S. and Faith, Seth A.}, year={2020}, month={Aug}, pages={909–929} }
 @article{rashid_luckett_chen_lawson_wang_zhang_laber_liu_yeh_zeng_et al._2020, title={High-Dimensional Precision Medicine From Patient-Derived Xenografts}, volume={116}, ISSN={["1537-274X"]}, DOI={10.1080/01621459.2020.1828091}, abstractNote={Abstract The complexity of human cancer often results in significant heterogeneity in response to treatment. Precision medicine offers the potential to improve patient outcomes by leveraging this heterogeneity. Individualized treatment rules (ITRs) formalize precision medicine as maps from the patient covariate space into the space of allowable treatments. The optimal ITR is that which maximizes the mean of a clinical outcome in a population of interest. Patient-derived xenograft (PDX) studies permit the evaluation of multiple treatments within a single tumor, and thus are ideally suited for estimating optimal ITRs. PDX data are characterized by correlated outcomes, a high-dimensional feature space, and a large number of treatments. Here we explore machine learning methods for estimating optimal ITRs from PDX data. We analyze data from a large PDX study to identify biomarkers that are informative for developing personalized treatment recommendations in multiple cancers. We estimate optimal ITRs using regression-based (Q-learning) and direct-search methods (outcome weighted learning). Finally, we implement a superlearner approach to combine multiple estimated ITRs and show that the resulting ITR performs better than any of the input ITRs, mitigating uncertainty regarding user choice. Our results indicate that PDX data are a valuable resource for developing individualized treatment strategies in oncology. Supplementary materials for this article are available online.}, number={535}, journal={JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION}, author={Rashid, Naim U. and Luckett, Daniel J. and Chen, Jingxiang and Lawson, Michael T. and Wang, Longshaokan and Zhang, Yunshu and Laber, Eric B. and Liu, Yufeng and Yeh, Jen Jen and Zeng, Donglin and et al.}, year={2020}, month={Nov}, pages={1140–1154} }
 @article{clifton_laber_2020, title={Q-Learning: Theory and Applications}, volume={7}, ISSN={["2326-831X"]}, DOI={10.1146/annurev-statistics-031219-041220}, abstractNote={ Q-learning, originally an incremental algorithm for estimating an optimal decision strategy in an infinite-horizon decision problem, now refers to a general class of reinforcement learning methods widely used in statistics and artificial intelligence. In the context of personalized medicine, finite-horizon Q-learning is the workhorse for estimating optimal treatment strategies, known as treatment regimes. Infinite-horizon Q-learning is also increasingly relevant in the growing field of mobile health. In computer science, Q-learning methods have achieved remarkable performance in domains such as game-playing and robotics. In this article, we ( a) review the history of Q-learning in computer science and statistics, ( b) formalize finite-horizon Q-learning within the potential outcomes framework and discuss the inferential difficulties for which it is infamous, and ( c) review variants of infinite-horizon Q-learning and the exploration-exploitation problem, which arises in decision problems with a long time horizon. We close by discussing issues arising with the use of Q-learning in practice, including arguments for combining Q-learning with direct-search methods; sample size considerations for sequential, multiple assignment randomized trials; and possibilities for combining Q-learning with model-based methods. }, journal={ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, VOL 7, 2020}, author={Clifton, Jesse and Laber, Eric}, year={2020}, pages={279–301} }
 @article{allwood_fierer_dunn_breen_reich_laber_clifton_grantham_faith_2020, title={Use of standardized bioinformatics for the analysis of fungal DNA signatures applied to sample provenance}, volume={310}, ISSN={["1872-6283"]}, DOI={10.1016/j.forsciint.2020.110250}, abstractNote={The use of environmental trace material to aid criminal investigations is an ongoing field of research within forensic science. The application of environmental material thus far has focused upon a variety of different objectives relevant to forensic biology, including sample provenance (also referred to as sample attribution). The capability to predict the provenance or origin of an environmental DNA sample would be an advantageous addition to the suite of investigative tools currently available. A metabarcoding approach is often used to predict sample provenance, through the extraction and comparison of the DNA signatures found within different environmental materials, such as the bacteria within soil or fungi within dust. Such approaches are combined with bioinformatics workflows and statistical modelling, often as part of large-scale study, with less emphasis on the investigation of the adaptation of these methods to a smaller scale method for forensic use. The present work was investigating a small-scale approach as an adaptation of a larger metabarcoding study to develop a model for global sample provenance using fungal DNA signatures collected from dust swabs. This adaptation was to facilitate a standardized method for consistent, reproducible sample treatment, including bioinformatics processing and final application of resulting data to the available prediction model. To investigate this small-scale method, 76 DNA samples were treated as anonymous test samples and analyzed using the standardized process to demonstrate and evaluate processing and customized sequence data analysis. This testing included samples originating from countries previously used to train the model, samples artificially mixed to represent multiple or mixed countries, as well as outgroup samples. Positive controls were also developed to monitor laboratory processing and bioinformatics analysis. Through this evaluation we were able to demonstrate that the samples could be processed and analyzed in a consistent manner, facilitated by a relatively user-friendly bioinformatic pipeline for sequence data analysis. Such investigation into standardized analyses and application of metabarcoding data is of key importance for the future use of applied microbiology in forensic science.}, journal={FORENSIC SCIENCE INTERNATIONAL}, author={Allwood, Julia S. and Fierer, Noah and Dunn, Robert R. and Breen, Matthew and Reich, Brian J. and Laber, Eric B. and Clifton, Jesse and Grantham, Neal S. and Faith, Seth A.}, year={2020}, month={May} }
 @article{hu_laber_barker_stefanski_2019, title={Assessing Tuning Parameter Selection Variability in Penalized Regression}, volume={61}, ISSN={["1537-2723"]}, DOI={10.1080/00401706.2018.1513380}, abstractNote={ABSTRACT Penalized regression methods that perform simultaneous model selection and estimation are ubiquitous in statistical modeling. The use of such methods is often unavoidable as manual inspection of all possible models quickly becomes intractable when there are more than a handful of predictors. However, automated methods usually fail to incorporate domain-knowledge, exploratory analyses, or other factors that might guide a more interactive model-building approach. A hybrid approach is to use penalized regression to identify a set of candidate models and then to use interactive model-building to examine this candidate set more closely. To identify a set of candidate models, we derive point and interval estimators of the probability that each model along a solution path will minimize a given model selection criterion, for example, Akaike information criterion, Bayesian information criterion (AIC, BIC), etc., conditional on the observed solution path. Then models with a high probability of selection are considered for further examination. Thus, the proposed methodology attempts to strike a balance between algorithmic modeling approaches that are computationally efficient but fail to incorporate expert knowledge, and interactive modeling approaches that are labor intensive but informed by experience, intuition, and domain knowledge. Supplementary materials for this article are available online.}, number={2}, journal={TECHNOMETRICS}, author={Hu, Wenhao and Laber, Eric B. and Barker, Clay and Stefanski, Leonard A.}, year={2019}, month={Apr}, pages={154–164} }
 @book{tsiatis_davidian_holloway_laber_2019, title={Dynamic Treatment Regimes}, ISBN={9780429192692}, url={http://dx.doi.org/10.1201/9780429192692}, DOI={10.1201/9780429192692}, publisher={Chapman and Hall/CRC}, author={Tsiatis, Anastasios A. and Davidian, Marie and Holloway, Shannon T. and Laber, Eric B.}, year={2019}, month={Dec} }
 @article{jiang_song_li_zeng_lu_he_xu_wang_qian_cheng_et al._2019, title={ENTROPY LEARNING FOR DYNAMIC TREATMENT REGIMES}, volume={29}, ISSN={["1996-8507"]}, DOI={10.5705/ss.202018.0076}, abstractNote={Estimating optimal individualized treatment rules (ITRs) in single or multi-stage clinical trials is one key solution to personalized medicine and has received more and more attention in statistical community. Recent development suggests that using machine learning approaches can significantly improve the estimation over model-based methods. However, proper inference for the estimated ITRs has not been well established in machine learning based approaches. In this paper, we propose a entropy learning approach to estimate the optimal individualized treatment rules (ITRs). We obtain the asymptotic distributions for the estimated rules so further provide valid inference. The proposed approach is demonstrated to perform well in finite sample through extensive simulation studies. Finally, we analyze data from a multi-stage clinical trial for depression patients. Our results offer novel findings that are otherwise not revealed with existing approaches.}, number={4}, journal={STATISTICA SINICA}, author={Jiang, Binyan and Song, Rui and Li, Jialiang and Zeng, Donglin and Lu, Wenbin and He, Xin and Xu, Shirong and Wang, Junhui and Qian, Min and Cheng, Bin and et al.}, year={2019}, month={Oct}, pages={1633–1710} }
 @article{kosorok_laber_louis_2019, title={Precision Medicine}, volume={6}, ISSN={["2326-831X"]}, DOI={10.1146/annurev-statistics-030718-105251}, abstractNote={ Precision medicine seeks to maximize the quality of health care by individualizing the health-care process to the uniquely evolving health status of each patient. This endeavor spans a broad range of scientific areas including drug discovery, genetics/genomics, health communication, and causal inference, all in support of evidence-based, i.e., data-driven, decision making. Precision medicine is formalized as a treatment regime that comprises a sequence of decision rules, one per decision point, which map up-to-date patient information to a recommended action. The potential actions could be the selection of which drug to use, the selection of dose, the timing of administration, the recommendation of a specific diet or exercise, or other aspects of treatment or care. Statistics research in precision medicine is broadly focused on methodological development for estimation of and inference for treatment regimes that maximize some cumulative clinical outcome. In this review, we provide an overview of this vibrant area of research and present important and emerging challenges. }, journal={ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, VOL 6}, author={Kosorok, Michael R. and Laber, Eric B. and Louis, TA}, year={2019}, pages={263–286} }
 @article{lewis_laber_olby_2019, title={Predictors of Response to 4-Aminopyridine in Chronic Canine Spinal Cord Injury}, volume={36}, ISSN={0897-7151 1557-9042}, url={http://dx.doi.org/10.1089/neu.2018.5975}, DOI={10.1089/neu.2018.5975}, abstractNote={4-Aminopyridine (4AP), a potassium channel antagonist, can improve hindlimb motor function in dogs with chronic thoracolumbar spinal cord injury (SCI); however, individual response is variable. We hypothesized that injury characteristics would differ between dogs that do and do not respond to 4AP. Our objective was to compare clinical, electrodiagnostic, gait, and imaging variables between dogs that do and do not respond to 4AP, to identify predictors of response. Thirty-four dogs with permanent deficits after acute thoracolumbar SCI were enrolled. Spasticity, motor and sensory evoked potentials (MEPs, SEPs), H-reflex, F-waves, gait scores, and magnetic resonance imaging (MRI) with diffusion tensor imaging (DTI) were evaluated at baseline and after 4AP administration. Baseline variables were assessed as predictors of response; response was defined as ≥1 point change in open field gait score. Variables were compared pre- and post-4AP to evaluate 4AP effects. Fifteen of 33 (45%) dogs were responders, 18/33 (55%) were non-responders and 1 was eliminated because of an adverse event. Pre-H-reflex threshold <1.2 mA predicted non-response; pre-H-reflex threshold >1.2 mA and Canine Spasticity Scale overall score <7 were predictive of response. All responders had translesional connections on DTI. MEPs were more common post-4AP than pre-4AP (10 vs. 6 dogs) and 4AP decreased H-reflex threshold and increased spasticity in responders. 4-AP impacts central conduction and motor neuron pool excitability in dogs with chronic SCI. Severity of spasticity and H-reflex threshold might allow prediction of response. Further exploration of electrodiagnostic and imaging characteristics might elucidate additional factors contributing to response or non-response.}, number={9}, journal={Journal of Neurotrauma}, publisher={Mary Ann Liebert Inc}, author={Lewis, Melissa J. and Laber, Eric and Olby, Natasha J.}, year={2019}, month={May}, pages={1428–1434} }
 @article{zottola_desmarais_neupert_dong_laber_lowder_van dorn_2019, title={Results of the Brief Jail Mental Health Screen Across Repeated Jail Bookings}, volume={70}, ISSN={1075-2730 1557-9700}, url={http://dx.doi.org/10.1176/appi.ps.201800377}, DOI={10.1176/appi.ps.201800377}, abstractNote={OBJECTIVE
The Brief Jail Mental Health Screen (BJMHS) is widely used at intake in county jails to identify detainees who may have serious mental illness and who should be referred for further mental health evaluation. The BJMHS may be administered multiple times across repeated jail bookings; however, the extent to which results may change over time is unclear. To that end, the authors examined the odds of screening positive on the BJMHS across repeated jail bookings.


METHODS
Data were drawn from the administrative and medical records of a large, urban county jail that used the BJMHS at jail booking. The study sample comprised BJMHS results for the 12,531 jail detainees who were booked at least twice during the 3.5-year period (N=41,965 bookings). Multilevel logistic modeling was used to examine changes over time overall and within the four decision rules (current psychiatric medication, prior hospitalization, two or more current symptoms, and referral for any other reason).


RESULTS
Results show that the odds of a positive screen overall increased with each jail booking, as did the odds of referral for any other reason. In contrast, the odds of screening positive for two or more current symptoms and prior hospitalization decreased. There was no change in the odds of screening positive for current psychiatric medication across bookings.


CONCLUSIONS
Findings show that BJMHS results changed across bookings. Further research is needed to determine whether changes reflect true changes in mental health status, issues with fidelity, the repeated nature of the screening process, or other factors.}, number={11}, journal={Psychiatric Services}, publisher={American Psychiatric Association Publishing}, author={Zottola, Samantha A. and Desmarais, Sarah L. and Neupert, Shevaun D. and Dong, Lin and Laber, Eric and Lowder, Evan M. and Van Dorn, Richard A.}, year={2019}, month={Nov}, pages={1006–1012} }
 @article{laber_staicu_2018, title={Functional Feature Construction for Individualized Treatment Regimes}, volume={113}, ISSN={["1537-274X"]}, DOI={10.1080/01621459.2017.1321545}, abstractNote={ABSTRACT Evidence-based personalized medicine formalizes treatment selection as an individualized treatment regime that maps up-to-date patient information into the space of possible treatments. Available patient information may include static features such race, gender, family history, genetic and genomic information, as well as longitudinal information including the emergence of comorbidities, waxing and waning of symptoms, side-effect burden, and adherence. Dynamic information measured at multiple time points before treatment assignment should be included as input to the treatment regime. However, subject longitudinal measurements are typically sparse, irregularly spaced, noisy, and vary in number across subjects. Existing estimators for treatment regimes require equal information be measured on each subject and thus standard practice is to summarize longitudinal subject information into a scalar, ad hoc summary during data preprocessing. This reduction of the longitudinal information to a scalar feature precedes estimation of a treatment regime and is therefore not informed by subject outcomes, treatments, or covariates. Furthermore, we show that this reduction requires more stringent causal assumptions for consistent estimation than are necessary. We propose a data-driven method for constructing maximally prescriptive yet interpretable features that can be used with standard methods for estimating optimal treatment regimes. In our proposed framework, we treat the subject longitudinal information as a realization of a stochastic process observed with error at discrete time points. Functionals of this latent process are then combined with outcome models to estimate an optimal treatment regime. The proposed methodology requires weaker causal assumptions than Q-learning with an ad hoc scalar summary and is consistent for the optimal treatment regime. Supplementary materials for this article are available online.}, number={523}, journal={JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION}, author={Laber, Eric B. and Staicu, Ana-Maria}, year={2018}, pages={1219–1227} }
 @article{laber_wu_munera_lipkovich_colucci_ripa_2018, title={Identifying optimal dosage regimes under safety constraints: An application to long term opioid treatment of chronic pain}, volume={37}, ISSN={0277-6715}, url={http://dx.doi.org/10.1002/SIM.7566}, DOI={10.1002/SIM.7566}, abstractNote={There is growing interest and investment in precision medicine as a means to provide the best possible health care. A treatment regime formalizes precision medicine as a sequence of decision rules, one per clinical intervention period, that specify if, when and how current treatment should be adjusted in response to a patient's evolving health status. It is standard to define a regime as optimal if, when applied to a population of interest, it maximizes the mean of some desirable clinical outcome, such as efficacy. However, in many clinical settings, a high‐quality treatment regime must balance multiple competing outcomes; eg, when a high dose is associated with substantial symptom reduction but a greater risk of an adverse event. We consider the problem of estimating the most efficacious treatment regime subject to constraints on the risk of adverse events. We combine nonparametric Q‐learning with policy‐search to estimate a high‐quality yet parsimonious treatment regime. This estimator applies to both observational and randomized data, as well as settings with variable, outcome‐dependent follow‐up, mixed treatment types, and multiple time points. This work is motivated by and framed in the context of dosing for chronic pain; however, the proposed framework can be applied generally to estimate a treatment regime which maximizes the mean of one primary outcome subject to constraints on one or more secondary outcomes. We illustrate the proposed method using data pooled from 5 open‐label flexible dosing clinical trials for chronic pain.}, number={9}, journal={Statistics in Medicine}, publisher={Wiley}, author={Laber, Eric B. and Wu, Fan and Munera, Catherine and Lipkovich, Ilya and Colucci, Salvatore and Ripa, Steve}, year={2018}, month={Feb}, pages={1407–1418} }
 @article{butler_laber_davis_kosorok_2018, title={Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules}, volume={74}, ISSN={["1541-0420"]}, DOI={10.1111/biom.12743}, abstractNote={Summary}, number={1}, journal={BIOMETRICS}, author={Butler, Emily L. and Laber, Eric B. and Davis, Sonia M. and Kosorok, Michael R.}, year={2018}, month={Mar}, pages={18–26} }
 @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{shortreed_laber_scott stroup_pineau_murphy_2017, title={A multiple imputation strategy for sequential multiple assignment randomized trials}, volume={36}, ISSN={0277-6715}, url={http://dx.doi.org/10.1002/SIM.7285}, DOI={10.1002/SIM.7285}, abstractNote={The code and example data set had been added as supporting information for this paper 1 and can be downloaded at: http://onlinelibrary.wiley.com/wol1/doi/10.1002/sim.6223/suppinfo.}, number={23}, journal={Statistics in Medicine}, publisher={Wiley}, author={Shortreed, Susan M. and Laber, Eric and Scott Stroup, T. and Pineau, Joelle and Murphy, Susan A.}, year={2017}, month={Mar}, pages={3760–3760} }
 @article{fenn_laber_williams_rousse_early_mariani_munana_decker_volk_olby_et al._2017, title={Associations Between Anesthetic Variables and Functional Outcome in Dogs With Thoracolumbar Intervertebral Disk Extrusion Undergoing Decompressive Hemilaminectomy}, volume={31}, ISSN={0891-6640}, url={http://dx.doi.org/10.1111/jvim.14677}, DOI={10.1111/jvim.14677}, abstractNote={BackgroundOutcome of acute experimental spinal cord injury is strongly associated with tissue perfusion and oxygenation. Cardiopulmonary depression could affect outcome in dogs undergoing general anesthesia for surgical treatment of thoracolumbar intervertebral disk extrusion (IVDE).}, number={3}, journal={Journal of Veterinary Internal Medicine}, publisher={Wiley}, author={Fenn, J. and Laber, E. and Williams, K. and Rousse, C. A. and Early, P. J. and Mariani, C. L. and Munana, Karen and Decker, S. De and Volk, H. A. and Olby, N. J. and et al.}, year={2017}, month={Mar}, pages={814–824} }
 @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{linn_laber_stefanski_2017, title={Interactive Q-Learning for Quantiles}, volume={112}, ISSN={["1537-274X"]}, DOI={10.1080/01621459.2016.1155993}, abstractNote={ABSTRACT A dynamic treatment regime is a sequence of decision rules, each of which recommends treatment based on features of patient medical history such as past treatments and outcomes. Existing methods for estimating optimal dynamic treatment regimes from data optimize the mean of a response variable. However, the mean may not always be the most appropriate summary of performance. We derive estimators of decision rules for optimizing probabilities and quantiles computed with respect to the response distribution for two-stage, binary treatment settings. This enables estimation of dynamic treatment regimes that optimize the cumulative distribution function of the response at a prespecified point or a prespecified quantile of the response distribution such as the median. The proposed methods perform favorably in simulation experiments. We illustrate our approach with data from a sequentially randomized trial where the primary outcome is remission of depression symptoms. Supplementary materials for this article are available online.}, number={518}, journal={JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION}, author={Linn, Kristin A. and Laber, Eric B. and Stefanski, Leonard A.}, year={2017}, month={Jun}, pages={638–649} }
 @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{blau_davis_gorney_dohse_williams_lim_pfitzner_laber_sawicki_olby_2017, title={Quantifying center of pressure variability in chondrodystrophoid dogs}, volume={226}, ISSN={1090-0233}, url={http://dx.doi.org/10.1016/j.tvjl.2017.07.001}, DOI={10.1016/j.tvjl.2017.07.001}, abstractNote={The center of pressure (COP) position reflects a combination of proprioceptive, motor and mechanical function. As such, it can be used to quantify and characterize neurologic dysfunction. The aim of this study was to describe and quantify the movement of COP and its variability in healthy chondrodystrophoid dogs while walking to provide a baseline for comparison to dogs with spinal cord injury due to acute intervertebral disc herniations. Fifteen healthy adult chondrodystrophoid dogs were walked on an instrumented treadmill that recorded the location of each dog’s COP as it walked. Center of pressure (COP) was referenced from an anatomical marker on the dogs’ back. The root mean squared (RMS) values of changes in COP location in the sagittal (y) and horizontal (x) directions were calculated to determine the range of COP variability. Three dogs would not walk on the treadmill. One dog was too small to collect interpretable data. From the remaining 11 dogs, 206 trials were analyzed. Mean RMS for change in COPx per trial was 0.0138 (standard deviation, SD 0.0047) and for COPy was 0.0185 (SD 0.0071). Walking speed but not limb length had a significant effect on COP RMS. Repeat measurements in six dogs had high test retest consistency in the x and fair consistency in the y direction. In conclusion, COP variability can be measured consistently in dogs, and a range of COP variability for normal chondrodystrophoid dogs has been determined to provide a baseline for future studies on dogs with spinal cord injury.}, journal={The Veterinary Journal}, publisher={Elsevier BV}, author={Blau, S.R. and Davis, L.M. and Gorney, A.M. and Dohse, C.S. and Williams, K.D. and Lim, J-H. and Pfitzner, W.G. and Laber, E. and Sawicki, G.S. and Olby, N.J.}, year={2017}, month={Aug}, pages={26–31} }
 @article{laber_shedden_2017, title={Statistical Significance and the Dichotomization of Evidence: The Relevance of the ASA Statement on Statistical Significance and p-Values for Statisticians}, volume={112}, ISSN={["1537-274X"]}, DOI={10.1080/01621459.2017.1311265}, abstractNote={Empirical efforts to document the practices and thought processes of data analysis are a promising way to understanding the real-world impact of statistical methodology. The empirical findings of M...}, number={519}, journal={JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION}, author={Laber, Eric B. and Shedden, Kerby}, year={2017}, pages={902–904} }
 @inbook{laber_shedden_yang_2016, title={An Imputation Method for Estimating the Learning Curve in Classification Problems}, ISBN={9783319270975 9783319270999}, ISSN={2193-2808 2197-8549}, url={http://dx.doi.org/10.1007/978-3-319-27099-9_9}, DOI={10.1007/978-3-319-27099-9_9}, abstractNote={The learning curve expresses the error rate of a predictive modeling procedure, when applied to a particular population, as a function of the sample size of the training dataset. It typically is a decreasing function with a positive limiting value (bounded below by the Bayes error rate). An estimate of the learning curve can be used to assess whether a modeling procedure is expected to become substantially more accurate if additional training data were obtained. Here, we consider an imputation-based procedure for estimating learning curves. We focus on classification, although the idea is applicable to other predictive modeling settings. Simulation studies indicate that useful estimates of learning curves can be obtained for roughly a four-fold increase in the size of the training set relative to the available data, and that the proposed imputation approach outperforms an alternative estimation approach based on parameterizing the learning curve. We illustrate the method with an application that predicts the risk of disease progression for people with chronic lymphocytic leukemia.}, booktitle={Statistical Analysis for High-Dimensional Data}, publisher={Springer International Publishing}, author={Laber, Eric B. and Shedden, Kerby and Yang, Yang}, year={2016}, pages={189–209} }
 @article{guan_laber_reich_2016, title={Bayesian nonparametric estimation for dynamic treatment regimes with sequential transition times comment}, volume={111}, number={515}, journal={Journal of the American Statistical Association}, author={Guan, Q. and Laber, E. B. and Reich, B. J.}, year={2016}, pages={936–942} }
 @article{guan_laber_reich_2016, title={Comment}, volume={111}, ISSN={0162-1459 1537-274X}, url={http://dx.doi.org/10.1080/01621459.2016.1200911}, DOI={10.1080/01621459.2016.1200911}, abstractNote={Material change: a universe of ideas for the new school year Gary Williams}, number={515}, journal={Journal of the American Statistical Association}, publisher={Informa UK Limited}, author={Guan, Qian and Laber, Eric B. and Reich, Brian J.}, year={2016}, month={Jul}, pages={936–942} }
 @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{lizotte_laber_2016, title={Multi-objective markov decision processes for data-driven decision support}, volume={17}, journal={Journal of Machine Learning Research}, author={Lizotte, D. J. and Laber, E. B.}, year={2016} }
 @article{huang_laber_2016, title={Personalized Evaluation of Biomarker Value: A Cost-Benefit Perspective}, volume={8}, ISSN={["1867-1772"]}, DOI={10.1007/s12561-014-9122-4}, abstractNote={For a patient who is facing a treatment decision, the added value of information provided by a biomarker depends on the individual patient’s expected response to treatment with and without the biomarker, as well as his/her tolerance of disease and treatment harm. However, individualized estimators of the value of a biomarker are lacking. We propose a new graphical tool named the subject-specific expected benefit curve for quantifying the personalized value of a biomarker in aiding a treatment decision. We develop semiparametric estimators for two general settings: (i) when biomarker data are available from a randomized trial; and (ii) when biomarker data are available from a cohort or a cross-sectional study, together with external information about a multiplicative treatment effect. We also develop adaptive bootstrap confidence intervals for consistent inference in the presence of nonregularity. The proposed method is used to evaluate the individualized value of the serum creatinine marker in informing treatment decisions for the prevention of renal artery stenosis.}, number={1}, journal={STATISTICS IN BIOSCIENCES}, author={Huang, Ying and Laber, Eric}, year={2016}, month={Jun}, pages={43–65} }
 @article{rodrigues_laber_2016, title={Preface}, volume={45}, ISSN={["1532-4141"]}, DOI={10.1080/03610918.2015.1111110}, abstractNote={Biometrics in modern computer science is defined as the automated use of biological properties to identify individuals. The early use of biometrics can be dated back to nearly 4000 years ago when the Babylon Empire legislated the use of fingerprints to protect a legal contract against forgery and falsification by having the fingerprints impressed into the clay tablet on which the contract had been written. Nowadays, the wide use of the Internet and mobile devices has brought out the booming of the biometric applications, and research on biometrics has been drastically expanded into many new domains. The research trends in biometric research may be categorized into three directions. The first direction is toward the broader Internet and mobile applications. This brings out a number of new topics to utilize biometrics in mobile banking, health care, medical archiving, cybersecurity, and privacy as a service, etc. These new applications have created a huge market of billion dollars for biometric technologies and the industry needs comes back to push the research further and vigorously. The second direction is towards algorithmic development, which includes the investigation of many new AI techniques in biometrics, such as fuzzy approaches, ensemble learning, and deep learning. These new approaches can often help improve the accuracy of automated recognition, making many new applications available for business. Especially, with the vast amount of data coming from billions of users on internet/mobile, biometrics now becomes a new Big Data challenge in its streaming, processing, classification and storage. The third research direction aims at discovering more types of biometrics for various uses. Besides the conventional fingerprints and signatures, other types of biometrics (such as iris, vein pattern, gait, and touch dynamics) have been investigated in recent biometric research. Their combination as multimodal biometrics is another popular way to exploit these types of biometrics in research. This book includes 16 chapters highlighting recent research advances in biometric security. Chapters 1–3 present new research developments using various biometric modalities including Fingerprints, Vein Patterns and Palmprints. New tools and techniques such as Deep Learning are investigated and presented. Chapter 4 reports a new biometric recognition approach based on the acoustic}, number={7}, journal={COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION}, author={Rodrigues, Paulo C. and Laber, Eric}, year={2016}, pages={2657–2657} }
 @article{huang_laber_janes_2015, title={Characterizing expected benefits of biomarkers in treatment selection}, volume={16}, ISSN={["1468-4357"]}, DOI={10.1093/biostatistics/kxu039}, abstractNote={Biomarkers associated with heterogeneity in subject responses to treatment hold potential for treatment selection. In practice, the decision regarding whether to adopt a treatment-selection marker depends on the effect of using the marker on the rate of targeted disease and on the cost associated with treatment. We propose an expected benefit measure that incorporates both effects to quantify a marker's treatment-selection capacity. This measure builds upon an existing decision-theoretic framework, but is expanded to account for the fact that optimal treatment absent marker information varies with the cost of treatment. In addition, we establish upper and lower bounds on the expected benefit for a perfect treatment-selection rule which provides the basis for a standardized expected benefit measure. We develop model-based estimators for these measures in a randomized trial setting and evaluate their asymptotic properties. An adaptive bootstrap confidence interval is proposed for inference in the presence of non-regularity. Alternative estimators robust to risk model misspecification are also investigated. We illustrate our methods using the Diabetes Control and Complications Trial where we evaluate the expected benefit of baseline hemoglobin A1C in selecting diabetes treatment.}, number={2}, journal={BIOSTATISTICS}, author={Huang, Ying and Laber, Eric B. and Janes, Holly}, year={2015}, month={Apr}, pages={383–399} }
 @article{zhang_laber_2015, title={Comment}, volume={110}, ISSN={0162-1459 1537-274X}, url={http://dx.doi.org/10.1080/01621459.2015.1106403}, DOI={10.1080/01621459.2015.1106403}, abstractNote={regime can shed further light on the finite sample behavior of the estimator. Although not covered by the theory, their simulation results already suggest that ART can be useful in the p > n setting (as shown in the simulations for p = 200 and n = 100). Another important direction would be to consider the generalization of the results to achieve uniform validity over n = {βn : ‖βn‖ ≤ C}. This includes the local asymptotic results discussed here but encompasses cases in which two component βnj are βnk are close, for example, |βnj − βnk| = O(n−1/2). Finally, it would be interesting to understand how the results would carry over to more general models. For example, the case of Z-estimators which have been considered in the post-model selection discussed above. A natural starting point would be a logistic model, where the outcome Y is binary. The recent literature on formal hypothesis testing accounting for misspecification that arise from model selection mistakes is still in its initial stages. For instance, several procedures that have been recently proposed are asymptotically equivalent but enjoy very different finite sample performances. Clearly, much research is still needed to better understand the finite sample behavior of estimators. Although it is unlikely one procedure will dominate others in all regimes it is important to better characterize their performance. Indeed, the wealth of different asymptotic regimes and different uniformity guarantees can be used as potential guidance for practitioners on which estimators they should focus on. The work of McKeague and Qian definitely contribute to this debate and will certainly stimulate future research.}, number={512}, journal={Journal of the American Statistical Association}, publisher={Informa UK Limited}, author={Zhang, Yichi and Laber, Eric B.}, year={2015}, month={Oct}, pages={1451–1454} }
 @article{grantham_reich_pacifici_laber_menninger_henley_barberán_leff_fierer_dunn_2015, title={Fungi Identify the Geographic Origin of Dust Samples}, volume={10}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0122605}, DOI={10.1371/journal.pone.0122605}, abstractNote={There is a long history of archaeologists and forensic scientists using pollen found in a dust sample to identify its geographic origin or history. Such palynological approaches have important limitations as they require time-consuming identification of pollen grains, a priori knowledge of plant species distributions, and a sufficient diversity of pollen types to permit spatial or temporal identification. We demonstrate an alternative approach based on DNA sequencing analyses of the fungal diversity found in dust samples. Using nearly 1,000 dust samples collected from across the continental U.S., our analyses identify up to 40,000 fungal taxa from these samples, many of which exhibit a high degree of geographic endemism. We develop a statistical learning algorithm via discriminant analysis that exploits this geographic endemicity in the fungal diversity to correctly identify samples to within a few hundred kilometers of their geographic origin with high probability. In addition, our statistical approach provides a measure of certainty for each prediction, in contrast with current palynology methods that are almost always based on expert opinion and devoid of statistical inference. Fungal taxa found in dust samples can therefore be used to identify the origin of that dust and, more importantly, we can quantify our degree of certainty that a sample originated in a particular place. This work opens up a new approach to forensic biology that could be used by scientists to identify the origin of dust or soil samples found on objects, clothing, or archaeological artifacts.}, number={4}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Grantham, Neal S. and Reich, Brian J. and Pacifici, Krishna and Laber, Eric B. and Menninger, Holly L. and Henley, Jessica B. and Barberán, Albert and Leff, Jonathan W. and Fierer, Noah and Dunn, Robert R.}, editor={Rokas, AntonisEditor}, year={2015}, month={Apr}, pages={e0122605} }
 @article{zhao_zeng_laber_kosorok_2015, title={New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes}, volume={110}, ISSN={["1537-274X"]}, DOI={10.1080/01621459.2014.937488}, abstractNote={Dynamic treatment regimes (DTRs) are sequential decision rules for individual patients that can adapt over time to an evolving illness. The goal is to accommodate heterogeneity among patients and find the DTR which will produce the best long-term outcome if implemented. We introduce two new statistical learning methods for estimating the optimal DTR, termed backward outcome weighted learning (BOWL), and simultaneous outcome weighted learning (SOWL). These approaches convert individualized treatment selection into an either sequential or simultaneous classification problem, and can thus be applied by modifying existing machine learning techniques. The proposed methods are based on directly maximizing over all DTRs a nonparametric estimator of the expected long-term outcome; this is fundamentally different than regression-based methods, for example, Q-learning, which indirectly attempt such maximization and rely heavily on the correctness of postulated regression models.  We prove that the resulting rules are consistent, and provide finite sample bounds for the errors using the estimated rules. Simulation results suggest the proposed methods produce superior DTRs compared with Q-learning especially in small samples. We illustrate the methods using data from a clinical trial for smoking cessation. Supplementary materials for this article are available online.}, number={510}, journal={JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION}, author={Zhao, Ying-Qi and Zeng, Donglin and Laber, Eric B. and Kosorok, Michael R.}, year={2015}, month={Jun}, pages={583–598} }
 @article{song_kosorok_zeng_zhao_laber_yuan_2015, title={On sparse representation for optimal individualized treatment selection with penalized outcome weighted learning}, volume={4}, ISSN={2049-1573}, url={http://dx.doi.org/10.1002/STA4.78}, DOI={10.1002/STA4.78}, abstractNote={As a new strategy for treatment, which takes individual heterogeneity into consideration, personalized medicine is of growing interest. Discovering individualized treatment rules for patients who have heterogeneous responses to treatment is one of the important areas in developing personalized medicine. As more and more information per individual is being collected in clinical studies and not all of the information is relevant for treatment discovery, variable selection becomes increasingly important in discovering individualized treatment rules. In this article, we develop a variable selection method based on penalized outcome weighted learning through which an optimal treatment rule is considered as a classification problem where each subject is weighted proportional to his or her clinical outcome. We show that the resulting estimator of the treatment rule is consistent and establish variable selection consistency and the asymptotic distribution of the estimators. The performance of the proposed approach is demonstrated via simulation studies and an analysis of chronic depression data. Copyright © 2015 John Wiley & Sons, Ltd.}, number={1}, journal={Stat}, publisher={Wiley}, author={Song, Rui and Kosorok, Michael and Zeng, Donglin and Zhao, Yingqi and Laber, Eric and Yuan, Ming}, year={2015}, month={Feb}, pages={59–68} }
 @article{laber_zhao_2015, title={Tree-based methods for individualized treatment regimes}, volume={102}, ISSN={["1464-3510"]}, DOI={10.1093/biomet/asv028}, abstractNote={Individualized treatment rules recommend treatments on the basis of individual patient characteristics. A high-quality treatment rule can produce better patient outcomes, lower costs and less treatment burden. If a treatment rule learned from data is to be used to inform clinical practice or provide scientific insight, it is crucial that it be interpretable; clinicians may be unwilling to implement models they do not understand, and black-box models may not be useful for guiding future research. The canonical example of an interpretable prediction model is a decision tree. We propose a method for estimating an optimal individualized treatment rule within the class of rules that are representable as decision trees. The class of rules we consider is interpretable but expressive. A novel feature of this problem is that the learning task is unsupervised, as the optimal treatment for each patient is unknown and must be estimated. The proposed method applies to both categorical and continuous treatments and produces favourable marginal mean outcomes in simulation experiments. We illustrate it using data from a study of major depressive disorder.}, number={3}, journal={BIOMETRIKA}, author={Laber, E. B. and Zhao, Y. Q.}, year={2015}, month={Sep}, pages={501–514} }
 @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} }
 @article{linn_laber_stefanski_2015, title={iqLearn: Interactive Q-Learning in R}, volume={64}, number={1}, journal={Journal of Statistical Software}, author={Linn, K. A. and Laber, E. B. and Stefanski, L. A.}, year={2015} }
 @article{shortreed_laber_scott stroup_pineau_murphy_2014, title={A multiple imputation strategy for sequential multiple assignment randomized trials}, volume={33}, ISSN={0277-6715}, url={http://dx.doi.org/10.1002/SIM.6223}, DOI={10.1002/SIM.6223}, abstractNote={Sequential multiple assignment randomized trials (SMARTs) are increasingly being used to inform clinical and intervention science. In a SMART, each patient is repeatedly randomized over time. Each randomization occurs at a critical decision point in the treatment course. These critical decision points often correspond to milestones in the disease process or other changes in a patient's health status. Thus, the timing and number of randomizations may vary across patients and depend on evolving patient‐specific information. This presents unique challenges when analyzing data from a SMART in the presence of missing data. This paper presents the first comprehensive discussion of missing data issues typical of SMART studies: we describe five specific challenges and propose a flexible imputation strategy to facilitate valid statistical estimation and inference using incomplete data from a SMART. To illustrate these contributions, we consider data from the Clinical Antipsychotic Trial of Intervention and Effectiveness, one of the most well‐known SMARTs to date. Copyright © 2014 John Wiley & Sons, Ltd.}, number={24}, journal={Statistics in Medicine}, publisher={Wiley}, author={Shortreed, Susan M. and Laber, Eric and Scott Stroup, T. and Pineau, Joelle and Murphy, Susan A.}, year={2014}, month={Jun}, pages={4202–4214} }
 @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} }
 @article{zhao_zeng_laber_song_yuan_kosorok_2014, title={Doubly robust learning for estimating individualized treatment with censored data}, volume={102}, ISSN={0006-3444 1464-3510}, url={http://dx.doi.org/10.1093/biomet/asu050}, DOI={10.1093/biomet/asu050}, abstractNote={Individualized treatment rules recommend treatments based on individual patient characteristics in order to maximize clinical benefit. When the clinical outcome of interest is survival time, estimation is often complicated by censoring. We develop nonparametric methods for estimating an optimal individualized treatment rule in the presence of censored data. To adjust for censoring, we propose a doubly robust estimator which requires correct specification of either the censoring model or survival model, but not both; the method is shown to be Fisher consistent when either model is correct. Furthermore, we establish the convergence rate of the expected survival under the estimated optimal individualized treatment rule to the expected survival under the optimal individualized treatment rule. We illustrate the proposed methods using simulation study and data from a Phase III clinical trial on non-small cell lung cancer.}, number={1}, journal={Biometrika}, publisher={Oxford University Press (OUP)}, author={Zhao, Y. Q. and Zeng, D. and Laber, E. B. and Song, R. and Yuan, M. and Kosorok, M. R.}, year={2014}, month={Dec}, pages={151–168} }
 @article{zhao_laber_2014, title={Estimation of optimal dynamic treatment regimes}, volume={11}, ISSN={["1740-7753"]}, DOI={10.1177/1740774514532570}, abstractNote={BackgroundRecent advances in medical research suggest that the optimal treatment rules should be adaptive to patients over time. This has led to an increasing interest in studying dynamic treatment regime, a sequence of individualized treatment rules, one per stage of clinical intervention, which maps present patient information to a recommended treatment. There has been a recent surge of statistical work for estimating optimal dynamic treatment regimes from randomized and observational studies. The purpose of this article is to review recent methodological progress and applied issues associated with estimating optimal dynamic treatment regimes.}, number={4}, journal={CLINICAL TRIALS}, author={Zhao, Ying-Qi and Laber, Eric B.}, year={2014}, month={Aug}, pages={400–407} }
 @article{chakraborty_laber_zhao_2014, title={Inference about the expected performance of a data-driven dynamic treatment regime}, volume={11}, ISSN={["1740-7753"]}, DOI={10.1177/1740774514537727}, abstractNote={Background A dynamic treatment regime (DTR) comprises a sequence of decision rules, one per stage of intervention, that recommends how to individualize treatment to patients based on evolving treatment and covariate history. These regimes are useful for managing chronic disorders, and fit into the larger paradigm of personalized medicine. The Value of a DTR is the expected outcome when the DTR is used to assign treatments to a population of interest. }, number={4}, journal={CLINICAL TRIALS}, author={Chakraborty, Bibhas and Laber, Eric B. and Zhao, Ying-Qi}, year={2014}, month={Aug}, pages={408–417} }
 @article{laber_linn_stefanski_2014, title={Interactive model building for Q-learning}, volume={101}, ISSN={["1464-3510"]}, DOI={10.1093/biomet/asu043}, abstractNote={Evidence-based rules for optimal treatment allocation are key components in the quest for efficient, effective health care delivery. Q-learning, an approximate dynamic programming algorithm, is a popular method for estimating optimal sequential decision rules from data. Q-learning requires the modeling of nonsmooth, nonmonotone transformations of the data, complicating the search for adequately expressive, yet parsimonious, statistical models. The default Q-learning working model is multiple linear regression, which is not only provably misspecified under most data-generating models, but also results in nonregular regression estimators, complicating inference. We propose an alternative strategy for estimating optimal sequential decision rules for which the requisite statistical modeling does not depend on nonsmooth, nonmonotone transformed data, does not result in nonregular regression estimators, is consistent under a broader array of data-generation models than Q-learning, results in estimated sequential decision rules that have better sampling properties, and is amenable to established statistical approaches for exploratory data analysis, model building, and validation. We derive the new method, IQ-learning, via an interchange in the order of certain steps in Q-learning. In simulated experiments IQ-learning improves on Q-learning in terms of integrated mean squared error and power. The method is illustrated using data from a study of major depressive disorder.}, number={4}, journal={BIOMETRIKA}, author={Laber, Eric B. and Linn, Kristin A. and Stefanski, Leonard A.}, year={2014}, month={Dec}, pages={831–847} }
 @article{lim_muguet-chanoit_smith_laber_olby_2014, title={Potassium channel antagonists 4-aminopyridine and the t-butyl carbamate derivative of 4-aminopyridine improve hind limb function in chronically non-ambulatory dogs; a blinded, placebo-controlled trial}, volume={9}, number={12}, journal={PLoS One}, author={Lim, J. H. and Muguet-Chanoit, A. C. and Smith, D. T. and Laber, E. and Olby, N. J.}, year={2014} }
 @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} }
 @article{laber_lizotte_qian_pelham_murphy_2014, title={Rejoinder of "Dynamic treatment regimes: Technical challenges and applications"}, volume={8}, journal={Electronic Journal of Statistics}, author={Laber, E. B. and Lizotte, D. J. and Qian, M. and Pelham, W. E. and Murphy, S. A.}, year={2014}, pages={1312–1321} }
 @article{laber_lizotte_ferguson_2014, title={Set-Valued Dynamic Treatment Regimes for Competing Outcomes}, volume={70}, ISSN={["1541-0420"]}, DOI={10.1111/biom.12132}, abstractNote={Summary}, number={1}, journal={BIOMETRICS}, author={Laber, Eric B. and Lizotte, Daniel J. and Ferguson, Bradley}, year={2014}, month={Mar}, pages={53–61} }
 @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} }
 @article{chakraborty_laber_zhao_2013, title={Inference for Optimal Dynamic Treatment Regimes Using an Adaptive m-Out-of-n Bootstrap Scheme}, volume={69}, ISSN={["1541-0420"]}, DOI={10.1111/biom.12052}, abstractNote={Abstract}, number={3}, journal={BIOMETRICS}, author={Chakraborty, Bibhas and Laber, Eric B. and Zhao, Yingqi}, year={2013}, month={Sep}, pages={714–723} }
 @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{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{laber_murphy_2011, title={Adaptive confidence intervals for the test error in classification rejoinder}, volume={106}, number={495}, journal={Journal of the American Statistical Association}, author={Laber, E. B. and Murphy, S. A.}, year={2011}, pages={940–945} }
 @article{shortreed_laber_lizotte_stroup_pineau_murphy_2010, title={Informing sequential clinical decision-making through reinforcement learning: an empirical study}, volume={84}, ISSN={0885-6125 1573-0565}, url={http://dx.doi.org/10.1007/S10994-010-5229-0}, DOI={10.1007/S10994-010-5229-0}, abstractNote={This paper highlights the role that reinforcement learning can play in the optimization of treatment policies for chronic illnesses. Before applying any off-the-shelf reinforcement learning methods in this setting, we must first tackle a number of challenges. We outline some of these challenges and present methods for overcoming them. First, we describe a multiple imputation approach to overcome the problem of missing data. Second, we discuss the use of function approximation in the context of a highly variable observation set. Finally, we discuss approaches to summarizing the evidence in the data for recommending a particular action and quantifying the uncertainty around the Q-function of the recommended policy. We present the results of applying these methods to real clinical trial data of patients with schizophrenia.}, number={1-2}, journal={Machine Learning}, publisher={Springer Science and Business Media LLC}, author={Shortreed, Susan M. and Laber, Eric and Lizotte, Daniel J. and Stroup, T. Scott and Pineau, Joelle and Murphy, Susan A.}, year={2010}, month={Dec}, pages={109–136} }