Project 2.4 | Innovative Methods Program for Advancing Clinical Trials (IMPACT)

Goldberg, Yair, Rui Song, and Michael R. Kosorok. "Adaptive Q-learning." In From Probability to Statistics and Back: High-Dimensional Models and Processes -- A Festschrift in Honor of Jon A. Wellner, 150-162. Vol. 9. Beachwood, Ohio: Institute of Mathematical Statistics, 2013.

An Exponential Bound for Cox Regression.

Goldberg, Y, and M R. Kosorok. "An Exponential Bound for Cox Regression." Stat Probab Lett 82, no. 7 (2012): 1267-1272.

Application of a sequential multiple assignment randomized trial (SMART) design in older patients with chronic lymphocytic leukemia.

Ruppert, A S., J Yin, M Davidian, A A. Tsiatis, J C. Byrd, J A. Woyach, and S J. Mandrekar. "Application of a sequential multiple assignment randomized trial (SMART) design in older patients with chronic lymphocytic leukemia." Ann Oncol 30, no. 4 (2019): 542-550.

Assessing the causal effect of organ transplantation on the distribution of residual lifetime.

Vock, David M., Anastasios A. Tsiatis, Marie Davidian, Eric B. Laber, Wayne M. Tsuang, Ashley Finlen C Copeland, and Scott M. Palmer. "Assessing the causal effect of organ transplantation on the distribution of residual lifetime." Biometrics 69, no. 4 (2013): 820-9.

Assessing Tuning Parameter Selection Variability in Penalized Regression.

Hu, Wenhao, Eric B. Laber, Clay Barker, and Leonard A. Stefanski. "Assessing Tuning Parameter Selection Variability in Penalized Regression." Technometrics 61, no. 2 (2019): 154-164.

Augmented outcome-weighted learning for estimating optimal dynamic treatment regimens.

Liu, Ying, Yuanjia Wang, Michael R. Kosorok, Yingqi Zhao, and Donglin Zeng. "Augmented outcome-weighted learning for estimating optimal dynamic treatment regimens." Stat Med 37, no. 26 (2018): 3776-3788.

Automatic structure recovery for additive models.

Wu, Yichao, and Leonard A. Stefanski. "Automatic structure recovery for additive models." Biometrika 102, no. 2 (2015): 381-395.

Auxiliary marker-assisted classification in the absence of class identifiers.

Wang, Yuanjia, Huaihou Chen, Donglin Zeng, Christine Mauro, Naihua Duan, and Katherine M Shear. "Auxiliary marker-assisted classification in the absence of class identifiers." J Am Stat Assoc 108, no. 502 (2013): 553-565.

CasANOVA: Simultaneous factor selection and collapsing levels in ANOVA (R).

Bondell, Howard D., and Shannon T. Holloway. CasANOVA: Simultaneous factor selection and collapsing levels in ANOVA (R).. R 3.0 ed., 2013.

Change-Plane Analysis for Subgroup Detection and Sample Size Calculation.

Fan, Ailin, Rui Song, and Wenbin Lu. "Change-Plane Analysis for Subgroup Detection and Sample Size Calculation." J Am Stat Assoc 112, no. 518 (2017): 769-778.

Characterizing expected benefits of biomarkers in treatment selection.

Huang, Ying, Eric B. Laber, and Holly Janes. "Characterizing expected benefits of biomarkers in treatment selection." Biostatistics 16, no. 2 (2015): 383-99.

Comment on "Adaptive Confidence Intervals for the Test Error in Classification".

Goldberg, Yair, and Michael R. Kosorok. "Comment on "Adaptive Confidence Intervals for the Test Error in Classification"." J Am Stat Assoc 106, no. 495 (2011): 920-924.

Comment on "Dynamic treatment regimes: technical challenges and applications".

Goldberg, Yair, Rui Song, Donglin Zeng, and Michael R. Kosorok. "Comment on "Dynamic treatment regimes: technical challenges and applications"." Electron J Stat 8, no. 1 (2014): 1290-1300.

Comment.

Guan, Qian, Eric B. Laber, and Brian J. Reich. "Comment." J Am Stat Assoc 111, no. 515 (2016): 936-942.

Comment.

Chen, Jingxiang, Yufeng Liu, Donglin Zeng, Rui Song, Yingqi Zhao, and Michael R. Kosorok. "Comment." J Am Stat Assoc 111, no. 515 (2016): 942-947.

Comparison of adaptive treatment strategies based on longitudinal outcomes in sequential multiple assignment randomized trials.

Li, Zhiguo. "Comparison of adaptive treatment strategies based on longitudinal outcomes in sequential multiple assignment randomized trials." Stat Med 36, no. 3 (2017): 403-415.

Composite large margin classifiers with latent subclasses for heterogeneous biomedical data.

Chen, Guanhua, Yufeng Liu, Dinggang Shen, and Michael R. Kosorok. "Composite large margin classifiers with latent subclasses for heterogeneous biomedical data." Stat Anal Data Min 9, no. 2 (2016): 75-88.

Concordance-Assisted Learning for Estimating Optimal Individualized Treatment Regimes.

Fan, Caiyun, Wenbin Lu, Rui Song, and Yong Zhou. "Concordance-Assisted Learning for Estimating Optimal Individualized Treatment Regimes." J R Stat Soc Series B Stat Methodol 79, no. 5 (2017): 1565-1582.

Consistent Group Identification and Variable Selection in Regression with Correlated Predictors.

Sharma, Dhruv B., Howard D. Bondell, and Hao Helen Zhang. "Consistent Group Identification and Variable Selection in Regression with Correlated Predictors." J Comput Graph Stat 22, no. 2 (2013): 319-340.

Consistent high-dimensional Bayesian variable selection via penalized credible regions.

Bondell, Howard D., and Brian J. Reich. "Consistent high-dimensional Bayesian variable selection via penalized credible regions." J Am Stat Assoc 107, no. 500 (2012): 1610-1624.

Creating an mHealth App for Colorectal Cancer Screening: User-Centered Design Approach.

Griffin, Lauren, Donghee Lee, Alyssa Jaisle, Peter Carek, Thomas George, Eric Laber, Benjamin Lok, François Modave, Electra Paskett, and Janice Krieger. "Creating an mHealth App for Colorectal Cancer Screening: User-Centered Design Approach." JMIR Hum Factors 6, no. 2 (2019): e12700.

cSFM: Covariate-adjusted skewed functional model (R).

Li, Meng, Ana-Maria Staicu, and Howard D. Bondell. cSFM: Covariate-adjusted skewed functional model (R).. 1.1 ed., 2014.

Deep advantage learning for optimal dynamic treatment regime.

Liang, Shuhan, Wenbin Lu, and Rui Song. "Deep advantage learning for optimal dynamic treatment regime." Stat Theory Relat Fields 2, no. 1 (2018): 80-88.

Discussion of "Combining biomarkers to optimize patient treatment recommendation".

Laber, Eric B., Anastasios A. Tsiatis, Marie Davidian, and Shannon T. Holloway. "Discussion of "Combining biomarkers to optimize patient treatment recommendation"." Biometrics 70, no. 3 (2014): 707-10.

Discussion of combining biomarkers to optimize patient treatment recommendations.

Zhao, Ying-Qi, and Michael R. Kosorok. "Discussion of combining biomarkers to optimize patient treatment recommendations." Biometrics 70, no. 3 (2014): 713-6.

Discussion of Laber et al. "Optimal treatment allocations in space and time for on-line control of an emerging infectious disease".

Lawson, Michael T., Hunyong Cho, Arkopal Choudhury, Yifan Cui, Xiaotong Jiang, Teeranan Pokaprakarn, and Michael R. Kosorok. "Discussion of Laber et al. "Optimal treatment allocations in space and time for on-line control of an emerging infectious disease"." J R Stat Soc Ser C Appl Stat 67, no. 4 (2018): 779-780.

Domain selection for the varying coefficient model via local polynomial regression.

Kong, Dehan, Howard Bondell, and Yichao Wu. "Domain selection for the varying coefficient model via local polynomial regression." Comput Stat Data Anal 83 (2015): 236-250.

DOUBLY ROBUST ESTIMATION OF OPTIMAL TREATMENT REGIMES FOR SURVIVAL DATA-WITH APPLICATION TO AN HIV/AIDS STUDY.

Jiang, Runchao, Wenbin Lu, Rui Song, Michael G. Hudgens, and Sonia Naprvavnik. "DOUBLY ROBUST ESTIMATION OF OPTIMAL TREATMENT REGIMES FOR SURVIVAL DATA-WITH APPLICATION TO AN HIV/AIDS STUDY." Ann Appl Stat 11, no. 3 (2017): 1763-1786.

Doubly Robust Learning for Estimating Individualized Treatment with Censored Data.

Zhao, Y Q., D Zeng, E B. Laber, R Song, M Yuan, and M R. Kosorok. "Doubly Robust Learning for Estimating Individualized Treatment with Censored Data." Biometrika 102, no. 1 (2015): 151-168.

Doubly-robust estimators of treatment-specific survival distributions in observational studies with stratified sampling.

Bai, Xiaofei, Anastasios A. Tsiatis, and Sean M. O'Brien. "Doubly-robust estimators of treatment-specific survival distributions in observational studies with stratified sampling." Biometrics 69, no. 4 (2013): 830-9.

DTRlearn: Learning Algorithms for Dynamic Treatment Regimes (R)

Liu, Ying, Yuanjia Wang, and Donglin Zeng. DTRlearn: Learning Algorithms for Dynamic Treatment Regimes (R). 1.3 ed., 2018.

dtrSurv: Dynamic Treatment Regimes for Survival Analysis (R)

Holloway, Shannon, and Hunyong Cho. dtrSurv: Dynamic Treatment Regimes for Survival Analysis (R). 1.1 ed., 2020.

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

Laber, Eric B., and Marie Davidian. "Dynamic treatment regimes, past, present, and future: A conversation with experts." Stat Methods Med Res 26, no. 4 (2017): 1605-1610.

Dynamic treatment regimes.

Davidian, Marie, Anastasios A. Tsiatis, and Eric B. Laber. "Dynamic treatment regimes." In Cancer Clinical Trials: Current and Controversial Issues in Design and Analysis. Boca Raton: Chapman & Hall / CRC Press, 2016.

Dynamic Treatment Regimes: Statistical Methods for Precision Medicine

Tsiatis, Anastasios A., Marie Davidian, Shannon T. Holloway, and Eric B. Laber. Dynamic Treatment Regimes: Statistical Methods for Precision Medicine In Chapman & Hall/CRC Monographs on Statistics and Applied Probability. Boca Raton: Chapman and Hall/CRC, 2019.

Dynamic treatment regimes: technical challenges and applications.

Laber, Eric B., Daniel J. Lizotte, Min Qian, William E. Pelham, and Susan A. Murphy. "Dynamic treatment regimes: technical challenges and applications." Electron J Stat 8, no. 1 (2014): 1225-1272.

DynTxRegime: A comprehensive package for analysis of dynamic treatment regimes (R).

Holloway, Shannon T., Eric B. Laber, Kristin A. Linn, Baqun Zhang, Marie Davidian, and Anastasios A. Tsiatis. DynTxRegime: A comprehensive package for analysis of dynamic treatment regimes (R).. v3.1 ed., 2017.

Effective dimension reduction for sparse functional data.

Yao, F, E Lei, and Y Wu. "Effective dimension reduction for sparse functional data." Biometrika 102, no. 2 (2015): 421-437.

Efficient augmentation and relaxation learning for individualized treatment rules using observational data.

Zhao, Ying-Qi, Eric B. Laber, Yang Ning, Sumona Saha, and Bruce E. Sands. "Efficient augmentation and relaxation learning for individualized treatment rules using observational data." J Mach Learn Res 20 (2019).

Efficient estimation of the distribution of time to composite endpoint when some endpoints are only partially observed.

Daniel, Rhian M., and Anastasios A. Tsiatis. "Efficient estimation of the distribution of time to composite endpoint when some endpoints are only partially observed." Lifetime Data Anal 19, no. 4 (2013): 513-46.

Entropy Learning for Dynamic Treatment Regimes.

Jiang, Binyan, Rui Song, Jialiang Li, and Donglin Zeng. "Entropy Learning for Dynamic Treatment Regimes." Stat Sin 29, no. 4 (2019): 1633-1655.

Estimating individualized treatment regimes from crossover designs.

Nguyen, Crystal T., Daniel J. Luckett, Anna R. Kahkoska, Grace E. Shearrer, Donna Spruijt-Metz, Jaimie N. Davis, and Michael R. Kosorok. "Estimating individualized treatment regimes from crossover designs." Biometrics 76, no. 3 (2020): 778-788.

Estimating individualized treatment rules for ordinal treatments.

Chen, Jingxiang, Haoda Fu, Xuanyao He, Michael R. Kosorok, and Yufeng Liu. "Estimating individualized treatment rules for ordinal treatments." Biometrics 74, no. 3 (2018): 924-933.

Estimating Individualized Treatment Rules Using Outcome Weighted Learning.

Zhao, Yingqi, Donglin Zeng, John A Rush, and Michael R. Kosorok. "Estimating Individualized Treatment Rules Using Outcome Weighted Learning." J Am Stat Assoc 107, no. 449 (2012): 1106-1118.

Estimating Optimal Treatment Regimes from a Classification Perspective.

Zhang, Baqun, Anastasios A. Tsiatis, Marie Davidian, Min Zhang, and Eric Laber. "Estimating Optimal Treatment Regimes from a Classification Perspective." Stat 1, no. 1 (2012): 103-114.

Estimating personalized diagnostic rules depending on individualized characteristics.

Liu, Ying, Yuanjia Wang, Chaorui Huang, and Donglin Zeng. "Estimating personalized diagnostic rules depending on individualized characteristics." Stat Med 36, no. 7 (2017): 1099-1117.

Estimation of dynamic treatment regimes for complex outcomes: Balancing benefits and risks.

Linn, Kristin A., Eric B. Laber, and Leonard A. Stefanski. "Estimation of dynamic treatment regimes for complex outcomes: Balancing benefits and risks." In Adaptive Treatment Strategies in Practice: Planning Trials and Analyzing Data for Personalized Medicine, 249-262. Philadelphia: ASA-SIAM, 2016.

Estimation of optimal dynamic treatment regimes.

Zhao, Ying-Qi, and Eric B. Laber. "Estimation of optimal dynamic treatment regimes." Clin Trials 11, no. 4 (2014): 400-407.

Evaluating personalized treatment regimes.

Laber, Eric B., and Min Qian. "Evaluating personalized treatment regimes." In Methods in Comparative Effectiveness Research. New York: Chapman and Hall/CRC, 2017.

Factor selection and structural identification in the interaction ANOVA model.

Post, Justin B., and Howard D. Bondell. "Factor selection and structural identification in the interaction ANOVA model." Biometrics 69, no. 1 (2013): 70-9.

FEATURE ELIMINATION IN KERNEL MACHINES IN MODERATELY HIGH DIMENSIONS.

Dasgupta, Sayan, Yair Goldberg, and Michael R. Kosorok. "FEATURE ELIMINATION IN KERNEL MACHINES IN MODERATELY HIGH DIMENSIONS." Ann Stat 47, no. 1 (2019): 497-526.

Functional feature construction for individualized treatment regimes.

Laber, Eric B., and Ana-Maria Staicu. "Functional feature construction for individualized treatment regimes." J Am Stat Assoc 113, no. 523 (2017): 1219-1227.

Greedy outcome weighted tree learning of optimal personalized treatment rules.

Zhu, Ruoqing, Ying-Qi Zhao, Guanhua Chen, Shuangge Ma, and Hongyu Zhao. "Greedy outcome weighted tree learning of optimal personalized treatment rules." Biometrics 73, no. 2 (2017): 391-400.

Haplo.CasGLM: Haplotype specific simultaneous factor selection and collapsing levels in GLM (R).

Bondell, Howard D., Jung-Ying Tzeng, Megan L. Koehler, and Shannon T. Holloway. Haplo.CasGLM: Haplotype specific simultaneous factor selection and collapsing levels in GLM (R).. 3rd ed., 2013.

HIGH-DIMENSIONAL A-LEARNING FOR OPTIMAL DYNAMIC TREATMENT REGIMES.

Shi, Chengchun, Alin Fan, Rui Song, and Wenbin Lu. "HIGH-DIMENSIONAL A-LEARNING FOR OPTIMAL DYNAMIC TREATMENT REGIMES." Ann Stat 46, no. 3 (2018): 925-957.

High-Dimensional Inference for Personalized Treatment Decision.

X Jeng, Jessie, Wenbin Lu, and Huimin Peng. "High-Dimensional Inference for Personalized Treatment Decision." Electron J Stat 12, no. 1 (2018): 2074-2089.

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

Laber, Eric B., Fan Wu, Catherine Munera, Ilya Lipkovich, Salvatore Colucci, and Steve Ripa. "Identifying optimal dosage regimes under safety constraints: An application to long term opioid treatment of chronic pain." Stat Med 37, no. 9 (2018): 1407-1418.

Impact of sex and gonadal steroids on neonatal brain structure.

Knickmeyer, Rebecca C., Jiaping Wang, Hongtu Zhu, Xiujuan Geng, Sandra Woolson, Robert M. Hamer, Thomas Konneker, Martin Styner, and John H. Gilmore. "Impact of sex and gonadal steroids on neonatal brain structure." Cereb Cortex 24, no. 10 (2014): 2721-31.

Incorporating covariates in skewed functional data models.

Li, Meng, Ana-Maria Staicu, and Howard D. Bondell. "Incorporating covariates in skewed functional data models." Biostatistics 16, no. 3 (2015): 413-26.

Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules.

Butler, Emily L., Eric B. Laber, Sonia M. Davis, and Michael R. Kosorok. "Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules." Biometrics 74, no. 1 (2018): 18-26.

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

Chakraborty, Bibhas, Eric B. Laber, and Ying-Qi Zhao. "Inference about the expected performance of a data-driven dynamic treatment regime." Clin Trials 11, no. 4 (2014): 408-417.

Inference for optimal dynamic treatment regimes using an adaptive m-out-of-n bootstrap scheme.

Chakraborty, Bibhas, Eric B. Laber, and Yingqi Zhao. "Inference for optimal dynamic treatment regimes using an adaptive m-out-of-n bootstrap scheme." Biometrics 69, no. 3 (2013): 714-23.

Inference on treatment effects from a randomized clinical trial in the presence of premature treatment discontinuation: the SYNERGY trial.

Zhang, Min, Anastasios A. Tsiatis, Marie Davidian, Karen S. Pieper, and Kenneth W. Mahaffey. "Inference on treatment effects from a randomized clinical trial in the presence of premature treatment discontinuation: the SYNERGY trial." Biostatistics 12, no. 2 (2011): 258-69.

Interactive model building for -learning.

Laber, Eric B., Kristin A. Linn, and Leonard A. Stefanski. "Interactive model building for -learning." Biometrika 101, no. 4 (2014): 831-847.

Interactive Q-learning for Quantiles.

Linn, Kristin A., Eric B. Laber, and Leonard A. Stefanski. "Interactive Q-learning for Quantiles." J Am Stat Assoc 112, no. 518 (2017): 638-649.

Interpretable Dynamic Treatment Regimes.

Zhang, Yichi, Eric B. Laber, Marie Davidian, and Anastasios A. Tsiatis. "Interpretable Dynamic Treatment Regimes." J Am Stat Assoc 113, no. 524 (2018): 1541-1549.

Interquantile Shrinkage and Variable Selection in Quantile Regression.

Jiang, Liewen, Howard D. Bondell, and Huixia Judy Wang. "Interquantile Shrinkage and Variable Selection in Quantile Regression." Comput Stat Data Anal 69 (2014): 208-219.

Interquantile Shrinkage in Regression Models.

Jiang, Liewen, Huixia Judy Wang, and Howard D. Bondell. "Interquantile Shrinkage in Regression Models." J Comput Graph Stat 22, no. 4 (2013).

iqLearn: Interactive Q-learning (R).

Linn, Kristin A., Eric B. Laber, and Leonard A. Stefanski. iqLearn: Interactive Q-learning (R).., 2013.

iqLearn: Interactive Q-Learning in R.

Linn, Kristin A., Eric B. Laber, and Leonard A. Stefanski. "iqLearn: Interactive Q-Learning in R." J Stat Softw 64, no. 1 (2015).

Learning Optimal Individualized Treatment Rules from Electronic Health Record Data.

Wang, Yuanjia, Peng Wu, Ying Liu, Chunhua Weng, and Donglin Zeng. "Learning Optimal Individualized Treatment Rules from Electronic Health Record Data." IEEE Int Conf Healthc Inform 2016 (2016): 65-71.

LIBERTI: A SMART study in plastic surgery.

Hibbard, Jonathan C., Jonathan S. Friedstat, Sonia M. Thomas, Renee E. Edkins, Scott C Hultman, and Michael R. Kosorok. "LIBERTI: A SMART study in plastic surgery." Clin Trials 15, no. 3 (2018): 286-293.

LOCAL INDEPENDENCE FEATURE SCREENING FOR NONPARAMETRIC AND SEMIPARAMETRIC MODELS BY MARGINAL EMPIRICAL LIKELIHOOD.

Chang, Jinyuan, Cheng Yong Tang, and Yichao Wu. "LOCAL INDEPENDENCE FEATURE SCREENING FOR NONPARAMETRIC AND SEMIPARAMETRIC MODELS BY MARGINAL EMPIRICAL LIKELIHOOD." Ann Stat 44, no. 2 (2016): 515-539.

Longitudinal dynamic functional regression.

Staicu, Ana-Maria, Md Nazmul Islam, Raluca Dumitru, and Eric van Heugten. "Longitudinal dynamic functional regression." J R Stat Soc Ser C Appl Stat 69, no. 1 (2020): 25-46.

Maximin Projection Learning for Optimal Treatment Decision with Heterogeneous Individualized Treatment Effects.

Shi, Chengchun, Rui Song, Wenbin Lu, and Bo Fu. "Maximin Projection Learning for Optimal Treatment Decision with Heterogeneous Individualized Treatment Effects." J R Stat Soc Series B Stat Methodol 80, no. 4 (2018): 681-702.

Modeling survival distribution as a function of time to treatment discontinuation: A dynamic treatment regime approach.

Yang, Shu, Anastasios A. Tsiatis, and Michael Blazing. "Modeling survival distribution as a function of time to treatment discontinuation: A dynamic treatment regime approach." Biometrics 74, no. 3 (2018): 900-909.

Modelling and estimation for optimal treatment decision with interference.

Su, Lin, Wenbin Lu, and Rui Song. "Modelling and estimation for optimal treatment decision with interference." Stat (Int Stat Inst) 8, no. 1 (2019).

Multi-Objective Markov Decision Processes for Data-Driven Decision Support.

Lizotte, Daniel J., and Eric B. Laber. "Multi-Objective Markov Decision Processes for Data-Driven Decision Support." J Mach Learn Res 17 (2016).

New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes.

Zhao, Ying-Qi, Donglin Zeng, Eric B. Laber, and Michael R. Kosorok. "New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes." J Am Stat Assoc 110, no. 510 (2015): 583-598.

Nonconvex Optimization.

Chen, G, Michael R. Kosorok, and X Zhou. Nonconvex Optimization. In Wiley StatsRef: Statistics Reference Online., 2018.

Noncrossing quantile regression curve estimation.

Bondell, Howard D., Brian J. Reich, and Huixia Wang. "Noncrossing quantile regression curve estimation." Biometrika 97, no. 4 (2010): 825-838.

On Estimation of Optimal Treatment Regimes For Maximizing -Year Survival Probability.

Jiang, Runchao, Wenbin Lu, Rui Song, and Marie Davidian. "On Estimation of Optimal Treatment Regimes For Maximizing -Year Survival Probability." J R Stat Soc Series B Stat Methodol 79, no. 4 (2017): 1165-1185.

ON ESTIMATION OF THE OPTIMAL TREATMENT REGIME WITH THE ADDITIVE HAZARDS MODEL.

Kang, Suhyun, Wenbin Lu, and Jiajia Zhang. "ON ESTIMATION OF THE OPTIMAL TREATMENT REGIME WITH THE ADDITIVE HAZARDS MODEL." Stat Sin 28, no. 3 (2018): 1539-1560.

On optimal treatment regimes selection for mean survival time.

Geng, Yuan, Hao Helen Zhang, and Wenbin Lu. "On optimal treatment regimes selection for mean survival time." Stat Med 34, no. 7 (2015): 1169-84.

On Quantile Regression in Reproducing Kernel Hilbert Spaces with Data Sparsity Constraint.

Zhang, Chong, Yufeng Liu, and Yichao Wu. "On Quantile Regression in Reproducing Kernel Hilbert Spaces with Data Sparsity Constraint." J Mach Learn Res 17, no. 40 (2016): 1-45.

On Sparse representation for Optimal Individualized Treatment Selection with Penalized Outcome Weighted Learning.

Song, Rui, Michael Kosorok, Donglin Zeng, Yingqi Zhao, Eric Laber, and Ming Yuan. "On Sparse representation for Optimal Individualized Treatment Selection with Penalized Outcome Weighted Learning." Stat 4, no. 1 (2015): 59-68.

ON TESTING CONDITIONAL QUALITATIVE TREATMENT EFFECTS.

Shi, Chengchun, Rui Song, and Wenbin Lu. "ON TESTING CONDITIONAL QUALITATIVE TREATMENT EFFECTS." Ann Stat 47, no. 4 (2019): 2348-2377.

On the impact of covariate measurement error on spatial regression modelling.

Huque, Md Hamidul, Howard Bondell, and Louise Ryan. "On the impact of covariate measurement error on spatial regression modelling." Environmetrics 25, no. 8 (2014): 560-570.

On Varying-coefficient Independence Screening for High-dimensional Varying-coefficient Models.

Song, Rui, Feng Yi, and Hui Zou. "On Varying-coefficient Independence Screening for High-dimensional Varying-coefficient Models." Stat Sin 24, no. 4 (2014): 1735-1752.

Optimal dynamic treatment regimes

Davidian, Marie, Anastasios A. Tsiatis, and Eric B. Laber. Optimal dynamic treatment regimes In Wiley Statsref. Optimal dynamic treatment regimes., 2016.

Optimal treatment allocations in space and time for on-line control of an emerging infectious disease.

Laber, Eric B., Nick J. Meyer, Brian J. Reich, Krishna Pacifici, Jaime A. Collazo, and John M. Drake. "Optimal treatment allocations in space and time for on-line control of an emerging infectious disease." J R Stat Soc Ser C Appl Stat 67, no. 4 (2018): 743-770.

Optimal treatment regimes for survival endpoints using a locally-efficient doubly-robust estimator from a classification perspective.

Bai, Xiaofei, Anastasios A. Tsiatis, Wenbin Lu, and Rui Song. "Optimal treatment regimes for survival endpoints using a locally-efficient doubly-robust estimator from a classification perspective." Lifetime Data Anal 23, no. 4 (2017): 585-604.

Optimal two-stage dynamic treatment regimes from a classification perspective with censored survival data.

Hager, Rebecca, Anastasios A. Tsiatis, and Marie Davidian. "Optimal two-stage dynamic treatment regimes from a classification perspective with censored survival data." Biometrics 74, no. 4 (2018): 1180-1192.

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

Kelleher, Sarah A., Caroline S. Dorfman, Jen C. Plumb Vilardaga, Catherine Majestic, Joseph Winger, Vicky Gandhi, Christine Nunez, Alyssa Van Denburg, Rebecca A. Shelby, Shelby D. Reed et al. "Optimizing delivery of a behavioral pain intervention in cancer patients using a sequential multiple assignment randomized trial SMART." Contemp Clin Trials 57 (2017): 51-57.

OTRselect: Variable selection for optimal treatment decision (R).

Lu, Wenbin, Hao Helen Zhang, Donglin Zeng, Yuan Geng, and Shannon T. Holloway. OTRselect: Variable selection for optimal treatment decision (R).. 1.0 ed. CRAN Repository, 2016.

Penalized Q-Learning for Dynamic Treatment Regimens.

Song, R, W Wang, D Zeng, and M R. Kosorok. "Penalized Q-Learning for Dynamic Treatment Regimens." Stat Sin 25, no. 3 (2015): 901-920.

Personalized Dose Finding Using Outcome Weighted Learning.

Chen, Guanhua, Donglin Zeng, and Michael R. Kosorok. "Personalized Dose Finding Using Outcome Weighted Learning." J Am Stat Assoc 111, no. 516 (2016): 1509-1521.

Personalized Evaluation of Biomarker Value: A Cost-Benefit Perspective.

Huang, Ying, and Eric Laber. "Personalized Evaluation of Biomarker Value: A Cost-Benefit Perspective." Stat Biosci 8, no. 1 (2016): 43-65.

Precision Medicine.

Kosorok, Michael R., and Eric B. Laber. "Precision Medicine." Annu Rev Stat Appl 6 (2019): 263-286.

Predicting Alzheimer's Disease Using Combined Imaging-Whole Genome SNP Data.

Kong, Dehan, Kelly S. Giovanello, Yalin Wang, Weili Lin, Eunjee Lee, Yong Fan, Murali P Doraiswamy, and Hongtu Zhu. "Predicting Alzheimer's Disease Using Combined Imaging-Whole Genome SNP Data." J Alzheimers Dis 46, no. 3 (2015): 695-702.

Probability-enhanced sufficient dimension reduction for binary classification.

Shin, Seung Jun, Yichao Wu, Hao Helen Zhang, and Yufeng Liu. "Probability-enhanced sufficient dimension reduction for binary classification." Biometrics 70, no. 3 (2014): 546-55.

Proper Inference for Value Function in High-Dimensional Q-Learning for Dynamic Treatment Regimes.

Zhu, Wensheng, Donglin Zeng, and Rui Song. "Proper Inference for Value Function in High-Dimensional Q-Learning for Dynamic Treatment Regimes." J Am Stat Assoc 114, no. 527 (2019): 1404-1417.

Prospects and challenges for clinical decision support in the era of big data.

Naqa, Issam El, Michael R. Kosorok, Judy Jin, Michelle Mierzwa, and Randall K. Ten Haken. "Prospects and challenges for clinical decision support in the era of big data." JCO Clin Cancer Inform 2 (2018).

Provider-based research networks and diffusion of surgical technologies among patients with early-stage kidney cancer.

Tan, Hung-Jui, Anne-Marie Meyer, Tzy-Mey Kuo, Angela B. Smith, Stephanie B. Wheeler, William R. Carpenter, and Matthew E. Nielsen. "Provider-based research networks and diffusion of surgical technologies among patients with early-stage kidney cancer." Cancer 121, no. 6 (2015): 836-43.

Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes.

Schulte, Phillip J., Anastasios A. Tsiatis, Eric B. Laber, and Marie Davidian. "Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes." Stat Sci 29, no. 4 (2014): 640-661.

Q-LEARNING WITH CENSORED DATA.

Goldberg, Yair, and Michael R. Kosorok. "Q-LEARNING WITH CENSORED DATA." Ann Stat 40, no. 1 (2012): 529-560.

Q-Learning.

Laber, Eric B., E Rose, Marie Davidian, and Anastasios A. Tsiatis. Q-Learning. In Wiley StatsRef: Statistics Reference Online. Wiley, 2018.

Quantifying center of pressure variability in chondrodystrophoid dogs.

Blau, S R., L M. Davis, A M. Gorney, C S. Dohse, K D. Williams, J-H Lim, W G. Pfitzner, E Laber, G S. Sawicki, and N J. Olby. "Quantifying center of pressure variability in chondrodystrophoid dogs." Vet J 226 (2017): 26-31.

Quantile-Optimal Treatment Regimes.

Wang, Lan, Yu Zhou, Rui Song, and Ben Sherwood. "Quantile-Optimal Treatment Regimes." J Am Stat Assoc 113, no. 523 (2018): 1243-1254.

Racial Differences in Diffusion of Intensity-Modulated Radiation Therapy for Localized Prostate Cancer.

Cobran, Ewan K., Ronald C. Chen, Robert Overman, Anne-Marie Meyer, Tzy-Mey Kuo, Jonathon O'Brien, Til Stürmer, Nathan C. Sheets, Gregg H. Goldin, Dolly C. Penn et al. "Racial Differences in Diffusion of Intensity-Modulated Radiation Therapy for Localized Prostate Cancer." Am J Mens Health 10, no. 5 (2016): 399-407.

RAMSVM: Reinforced angle-based multicategory support vector machines (R).

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Recursively Imputed Survival Trees.

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Reinforcement learning trees

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Residual Weighted Learning for Estimating Individualized Treatment Rules.

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Response to reader reaction.

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