Eric Benjamin Laber

Inference for change-plane regression

Kang, C., Cho, H., Song, R., Banerjee, M., Laber, E. B., & Kosorok, M. R. (2025, April 9). Bernoulli.

Behavioral cancer pain intervention dosing: results of a Sequential Multiple Assignment Randomized Trial

Somers, T. J., Winger, J. G., Fisher, H. M., Hyland, K. A., Davidian, M., Laber, E. B., … Keefe, F. J. (2023, April 20). Pain, Vol. 164, pp. 1935–1941.

Cost-Effectiveness Analysis Evaluating Delivery Strategies for Pain Coping Skills Training in Women With Breast Cancer

Li, Y., Reed, S. D., Winger, J. G., Hyland, K. A., Fisher, H. M., Kelleher, S. A., … Somers, T. J. (2023, May 15). Journal of Pain, Vol. 24, pp. 1712–1720.

Deep Spatial Q-Learning for Infectious Disease Control

Liu, Z., Clifton, J., Laber, E. B., Drake, J., & Fang, E. X. (2023, July 8). Journal of Agricultural Biological and Environmental Statistics.

Interim Monitoring of Sequential Multiple Assignment Randomized Trials Using Partial Information

Manschot, C., Laber, E., & Davidian, M. (2023, March 10). Biometrics, Vol. 3.

Effectiveness of the SMART Sex Ed program among 13–18 year old English and Spanish speaking adolescent men who have sex with men

Mustanski, B., Saber, R., Macapagal, K., Matson, M., Laber, E., Rodrgiuez-Diaz, C., … Newcomb, M. E. (2022, August 11). AIDS and Behavior.

Telehealth and racial disparities in colorectal cancer screening: A pilot study of how virtual clinician characteristics influence screening intentions

Cooks, E. J., Duke, K. A., Neil, J. M., Vilaro, M. J., Wilson-Howard, D., Modave, F., … Krieger, J. L. (2022, January 1). Journal of Clinical and Translational Science, Vol. 4.

A spatiotemporal recommendation engine for malaria control

Guan, Q., Reich, B. J., & Laber, E. B. (2021, March 20). Biostatistics, Vol. 4.

Introduction to the Theory and Methods Special Issue on Precision Medicine and Individualized Policy Discovery

Kosorok, M. R., Laber, E. B., Small, D. S., & Zeng, D. (2021, January 2). Journal of the American Statistical Association.

Novel approach to modeling high-frequency activity data to assess therapeutic effects of analgesics in chronic pain conditions

Xu, Z., Laber, E., Staicu, A.-M., & Lascelles, B. D. X. (2021, April 8). Scientific Reports, Vol. 4.

Reinforced Risk Prediction With Budget Constraint Using Irregularly Measured Data From Electronic Health Records

Pan, Y., Laber, E. B., Smith, M. A., & Zhao, Y.-Q. (2021, September 9). Journal of the American Statistical Association.

Variance Decompositions for Extensive-Form Games

Cloud, A., & Laber, E. B. (2021, August 17). 2021 IEEE Conference on Games (CoG).

Ascertaining properties of weighting in the estimation of optimal treatment regimes under monotone missingness

Statistics in Medicine, 39(25), 3503–3520.

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

Mustanski, B., Moskowitz, D. A., Moran, K. O., Newcomb, M. E., Macapagal, K., Rodriguez-Díaz, C., … Cabral, C. (2020, July 7). JMIR Research Protocols.

Global Forensic Geolocation with Deep Neural Networks

Grantham, N. S., Reich, B. J., Laber, E. B., Pacifici, K., Dunn, R. R., Fierer, N., … Faith, S. A. (2020, June 23). Journal of the Royal Statistical Society Series C (Applied Statistics).

High-Dimensional Precision Medicine From Patient-Derived Xenografts

Rashid, N. U., Luckett, D. J., Chen, J., Lawson, M. T., Wang, L., Zhang, Y., … Kosorok, M. R. (2020, September 28). Journal of the American Statistical Association.

Q-Learning: Theory and Applications

Clifton, J., & Laber, E. (2020, March 9). Annual Review of Statistics and Its Application.

Receiver operating characteristic curves and confidence bands for support vector machines

Luckett, D. J., Laber, E. B., El‐Kamary, S. S., Fan, C., Jhaveri, R., Perou, C. M., … Kosorok, M. R. (2020, August 31). Biometrics.

Use of standardized bioinformatics for the analysis of fungal DNA signatures applied to sample provenance

Allwood, J. S., Fierer, N., Dunn, R. R., Breen, M., Reich, B. J., Laber, E. B., … Faith, S. A. (2020, March 12). Forensic Science International, Vol. 310.

Bayesian Nonparametric Policy Search With Application to Periodontal Recall Intervals

Guan, Q., Reich, B. J., Laber, E. B., & Bandyopadhyay, D. (2019, September 12). Journal of the American Statistical Association.

Dynamic Treatment Regimes

Precision Medicine

Kosorok, M. R., & Laber, E. B. (2019, March 7). Annual Review of Statistics and Its Application.

Results of the Brief Jail Mental Health Screen Across Repeated Jail Bookings

Psychiatric Services, 70(11), 1006–1012.

Assessing Tuning Parameter Selection Variability in Penalized Regression

Hu, W., Laber, E. B., Barker, C., & Stefanski, L. A. (2018, October 16). Technometrics, Vol. 61, pp. 154–164.

Entropy Learning for Dynamic Treatment Regimes

Jiang, B., Song, R., Li, J., Zeng, D., Lu, W., He, X., … Kallus, N. (2018, October 22). Statistica Sinica, Vol. 29, pp. 1633–1710.

Estimating Dynamic Treatment Regimes in Mobile Health Using V-Learning

Luckett, D. J., Laber, E. B., Kahkoska, A. R., Maahs, D. M., Mayer-Davis, E., & Kosorok, M. R. (2018, December 12). Journal of the American Statistical Association.

Identifying optimal dosage regimes under safety constraints: An application to long term opioid treatment of chronic pain

Statistics in Medicine, 37(9), 1407–1418.

Predictors of Response to 4-Aminopyridine in Chronic Canine Spinal Cord Injury

Journal of Neurotrauma, 36(9), 1428–1434.

A multiple imputation strategy for sequential multiple assignment randomized trials

Statistics in Medicine, 36(23), 3760–3760.

Associations Between Anesthetic Variables and Functional Outcome in Dogs With Thoracolumbar Intervertebral Disk Extrusion Undergoing Decompressive Hemilaminectomy

Journal of Veterinary Internal Medicine, 31(3), 814–824.

Dynamic treatment regimes, past, present, and future: A conversation with experts

Laber, E. B., & Davidian, M. (2017, May 8). Statistical Methods in Medical Research, Vol. 26, pp. 1605–1610.

Functional Feature Construction for Individualized Treatment Regimes

Laber, E. B., & Staicu, A.-M. (2017, June 26). Journal of the American Statistical Association, Vol. 113, pp. 1219–1227.

Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules

Butler, E. L., Laber, E. B., Davis, S. M., & Kosorok, M. R. (2017, July 25). Biometrics.

Interpretable Dynamic Treatment Regimes

Zhang, Y., Laber, E. B., Davidian, M., & Tsiatis, A. A. (2017, August 4). Journal of the American Statistical Association, Vol. 113, pp. 1541–1549.

Optimizing delivery of a behavioral pain intervention in cancer patients using a sequential multiple assignment randomized trial SMART

Contemporary Clinical Trials, 57, 51–57.

Quantifying center of pressure variability in chondrodystrophoid dogs

The Veterinary Journal, 226, 26–31.

Statistical Significance and the Dichotomization of Evidence: The Relevance of the ASA Statement on Statistical Significance and p-Values for Statisticians

Laber, E. B., & Shedden, K. (2017, July 3). Journal of the American Statistical Association.

An Imputation Method for Estimating the Learning Curve in Classification Problems

In Statistical Analysis for High-Dimensional Data (pp. 189–209).

Bayesian nonparametric estimation for dynamic treatment regimes with sequential transition times comment

Journal of the American Statistical Association, 111(515), 936–942.

Comment

Journal of the American Statistical Association, 111(515), 936–942.

Estimating optimal treatment regimes from a classification perspective

Zhang, B., Anastasios, A. T., Davidian, M., Zhang, M., & Laber, E. (2016, January 1). Stat, Vol. 5, pp. 278–278.

Interactive Q -Learning for Quantiles

Linn, K. A., Laber, E. B., & Stefanski, L. A. (2016, March 22). Journal of the American Statistical Association, Vol. 112, pp. 638–649.

Multi-objective markov decision processes for data-driven decision support

Journal of Machine Learning Research, 17.

Comment

Journal of the American Statistical Association, 110(512), 1451–1454.

Fungi Identify the Geographic Origin of Dust Samples

PLOS ONE, 10(4), e0122605.

On sparse representation for optimal individualized treatment selection with penalized outcome weighted learning

Stat, 4(1), 59–68.

Preface

Rodrigues, P. C., & Laber, E. (2015, October 23). Communications in Statistics - Simulation and Computation.

Tree-based methods for individualized treatment regimes

Laber, E. B., & Zhao, Y. Q. (2015, July 15). Biometrika.

Using Decision Lists to Construct Interpretable and Parsimonious Treatment Regimes

Zhang, Y., Laber, E. B., Tsiatis, A., & Davidian, M. (2015, July 20). Biometrics, Vol. 71, pp. 895–904.

Using pilot data to size a two-arm randomized trial to find a nearly optimal personalized treatment strategy

Statistics in Medicine, 35(8), 1245–1256.

iqLearn: Interactive Q-Learning in R

Journal of Statistical Software, 64(1).

$\mathbf{Q}$- and $\mathbf{A}$-Learning Methods for Estimating Optimal Dynamic Treatment Regimes

Schulte, P. J., Tsiatis, A. A., Laber, E. B., & Davidian, M. (2014, November 1). Statistical Science, Vol. 29, pp. 640–661.

A multiple imputation strategy for sequential multiple assignment randomized trials

Statistics in Medicine, 33(24), 4202–4214.

Characterizing expected benefits of biomarkers in treatment selection

Huang, Y., Laber, E. B., & Janes, H. (2014, September 3). Biostatistics.

Discussion of “Combining biomarkers to optimize patient treatment recommendation”

Laber, E. B., Tsiatis, A. A., Davidian, M., & Holloway, S. T. (2014, June 2). Discussion of “Combining biomarkers to optimize patient treatment recommendation.” Biometrics, Vol. 70, pp. 707–710.

Doubly robust learning for estimating individualized treatment with censored data

Biometrika, 102(1), 151–168.

Dynamic treatment regimes: Technical challenges and applications

Electronic Journal of Statistics, 8, 1225–1273.

Estimation of optimal dynamic treatment regimes

Zhao, Y.-Q., & Laber, E. B. (2014, May 28). Clinical Trials.

Inference about the expected performance of a data-driven dynamic treatment regime

Chakraborty, B., Laber, E. B., & Zhao, Y.-Q. (2014, June 12). Clinical Trials.

Interactive model building for Q-learning

Laber, E. B., Linn, K. A., & Stefanski, L. A. (2014, October 22). Biometrika.

New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes

Zhao, Y.-Q., Zeng, D., Laber, E. B., & Kosorok, M. R. (2014, July 10). Journal of the American Statistical Association.

Personalized Evaluation of Biomarker Value: A Cost-Benefit Perspective

Huang, Y., & Laber, E. (2014, November 21). Statistics in Biosciences.

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

PLoS One, 9(12).

Set‐valued dynamic treatment regimes for competing outcomes

Laber, E. B., Lizotte, D. J., & Ferguson, B. (2014, January 8). Biometrics.

Assessing the Causal Effect of Organ Transplantation on the Distribution of Residual Lifetime

Vock, D. M., Tsiatis, A. A., Davidian, M., Laber, E. B., Tsuang, W. M., Copeland, C. A. F., & Palmer, S. M. (2013, October 15). Biometrics, Vol. 69, pp. 820–829.

Inference for Optimal Dynamic Treatment Regimes Using an Adaptive m‐Out‐of‐n Bootstrap Scheme

Chakraborty, B., Laber, E. B., & Zhao, Y. (2013, July 11). Biometrics.

Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions

Zhang, B., Tsiatis, A. A., Laber, E. B., & Davidian, M. (2013, May 30). Biometrika, Vol. 100, pp. 681–694.

A Robust Method for Estimating Optimal Treatment Regimes

Zhang, B., Tsiatis, A. A., Laber, E. B., & Davidian, M. (2012, May 2). Biometrics, Vol. 68, pp. 1010–1018.

Estimating optimal treatment regimes from a classification perspective

Stat, 1(1), 103–114.

Adaptive confidence intervals for the test error in classification rejoinder

Journal of the American Statistical Association, 106(495), 940–945.

Informing sequential clinical decision-making through reinforcement learning: an empirical study

Machine Learning, 84(1-2), 109–136.