qLearn swMATH ID: 11117 Software Authors: Chakraborty, Bibhas; Laber, Eric B.; Zhao, Yingqi Description: Inference for optimal dynamic treatment regimes using an adaptive m-out-of-n bootstrap scheme. A dynamic treatment regime consists of a set of decision rules that dictate how to individualize treatment to patients based on available treatment and covariate history. A common method for estimating an optimal dynamic treatment regime from data is Q-learning which involves nonsmooth operations of the data. This nonsmoothness causes standard asymptotic approaches for inference like the bootstrap or Taylor series arguments to breakdown if applied without correction. Here, we consider the m-out-of-n bootstrap for constructing confidence intervals for the parameters indexing the optimal dynamic regime. We propose an adaptive choice of m and show that it produces asymptotically correct confidence sets under fixed alternatives. Furthermore, the proposed method has the advantage of being conceptually and computationally much simple than competing methods possessing this same theoretical property. We provide an extensive simulation study to compare the proposed method with currently available inference procedures. The results suggest that the proposed method delivers nominal coverage while being less conservative than alternatives. The proposed methods are implemented in the qLearn R-package and have been made available on the Comprehensive R-Archive Network (http://cran.r-project.org/). Analysis of the Sequenced Treatment Alternatives to Relieve Depression (STAR * D) study is used as an illustrative example. Homepage: http://cran.r-project.org/web/packages/qLearn/index.html Source Code: https://github.com/cran/qLearn Dependencies: R Keywords: dynamic treatment regime; \(m\)-out-of-\(n\) bootstrap; nonregularity; \(Q\)-learning Related Software: DynTxRegime; R; DTRreg; iqLearn; bootlib; Matching; fda (R); IVEware; Approxrl; nhanes; Matlab; listdtr; qlaci; DTR; covTest; leaps; randomForest; SuperLearner; earth; e1071 Cited in: 19 Documents all top 5 Cited by 51 Authors 6 Chakraborty, Bibhas 5 Moodie, Erica E. M. 4 Laber, Eric B. 2 Cheung, Ying Kuen K. 2 Qian, Min 2 Wallace, Michael P. 2 Zhao, Yingqi 1 Almirall, Daniel 1 Brower, Roy 1 Daniels, Michael J. 1 Davidian, Marie 1 Davidson, Karina W. 1 Ertefaie, Ashkan 1 Ghosh, Palash 1 Hong, Han 1 Kasari, Connie 1 Kidwell, Kelley M. 1 Kramer, Michael S. 1 Li, Jessie 1 Li, Quefeng 1 Lizotte, Daniel J. 1 Maiti, Raju 1 McKay, James R. 1 Murphy, Susan A. 1 Murray, Thomas A. 1 Needham, Dale M. 1 Oslin, David W. 1 Pelham, William E. 1 Platt, Robert W. 1 Rush, A. John 1 Scharfstein, Daniel O. 1 Seewald, Nicholas J. 1 Shao, Jun 1 Simoneau, Gabrielle 1 Song, Rui 1 Staicu, Ana-Maria 1 Strawderman, Robert L. 1 Tao, Yebin 1 Thall, Peter F. 1 Tran, Qui 1 Tsiatis, Anastasios A. 1 Wang, Chenguang 1 Wang, Lu 1 Wang, Sijian 1 Wang, Weiwei 1 Xu, Yaoyao 1 Yu, Menggang 1 Yuan, Ying 1 Zeng, Donglin 1 Zhang, Baqun 1 Zhu, Wensheng all top 5 Cited in 11 Serials 6 Biometrics 4 Journal of the American Statistical Association 1 The Canadian Journal of Statistics 1 The Annals of Statistics 1 Biometrika 1 Journal of Statistical Computation and Simulation 1 Statistica Sinica 1 Journal of the Royal Statistical Society. Series C. Applied Statistics 1 Journal of Applied Statistics 1 Electronic Journal of Statistics 1 Statistics for Biology and Health Cited in 4 Fields 19 Statistics (62-XX) 3 Numerical analysis (65-XX) 3 Computer science (68-XX) 2 Biology and other natural sciences (92-XX) Citations by Year