DTRlearn (version 1.0)

DTRlearn-package: Dynamic Treatment Regimens Learning

Description

This package contains 3 main type of algorithms to find optimal DTR. Dynamic treatment regimens (DTRs) are sequential decision rules tailored at each stage by potentially time-varying patient features and intermediate outcomes observed in previous stages. For many complex chronic disorders, DTRs operationalize the sequential process of medical decision-making and closely reflect actual clinical practice. Sequential Multiple Assignment Randomized Trials (SMARTs) were proposed to best construct DTRs which offer a causal interpretation of their comparisons through randomization at each critical decision point. Machine learning methods such as Q-learning ,O-learning (outcome weighted learning) and P-learning have been proposed to find the optimal individualized treatment from SMART. They can also be used to analysis observational data provided estimation for the distribution of the treatment. Currently we allow two treatment choices for each stage coded by 1 and -1.

Details

ll{ Package: DTRlearn Type: Package Version: 1.0 Date: 2015-10-07 License: GPL-2 }

References

Use of personalized Dynamic Treatment Regimes (DTRs) and Sequential Multiple Assignment Randomized Trials (SMARTs) in mental health studies http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4311115/ The methodology paper for Plearning is under double blinded review.

Zhao, Y., Zeng, D., Rush, A. J., & Kosorok, M. R. (2012). Estimating individualized treatment rules using outcome weighted learning. Journal of the American Statistical Association, 107(499), 1106-1118. Zhao, Y. Q., Zeng, D., Laber, E. B., & Kosorok, M. R. (2014). New statistical learning methods for estimating optimal dynamic treatment regimes. Journal of the American Statistical Association, (just-accepted), 00-00. Watkins, C. J. C. H. (1989). Learning from delayed rewards (Doctoral dissertation, University of Cambridge). Murphy, S. A., Oslin, D. W., Rush, A. J., & Zhu, J. (2007). Methodological challenges in constructing effective treatment sequences for chronic psychiatric disorders. Neuropsychopharmacology, 32(2), 257-262. Zhao, Y., Kosorok, M. R., & Zeng, D. (2009). Reinforcement learning design for cancer clinical trials. Statistics in medicine, 28(26), 3294.