Dynamic treatment regimens (DTRs) are sequential decision rules tailored at each stage by time-varying subject-specific features and intermediate outcomes observed in previous stages. For many complex chronic disorders, DTRs operationalize the sequential process of medical decision-making. Sequential Multiple Assignment Randomized Trials (SMARTs) are 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 O-learning (Zhao et. al. 2012,2014), Q-learning (Murphy et. al. 2007, Zhao et.al. 2009) and P-learning (Liu et. al. 2014, 2015) have been proposed to estimate the optimal individualized treatment from a SMART.
This package implements these 3 main types of algorithms to estimate the optimal DTR. The algorithms consider a continuous outcome (the larger indicates better clinical results), and allow two treatment choices for each stage coded by 1 and -1.
| Package: | DTRlearn |
| Type: | Package |
| Version: | 1.1 |
| Date: | 2015-10-26 |
| License: | GPL-2 |
Liu, Y., Zeng, D., Wang, Y. (2014). Use of personalized Dynamic Treatment Regimes (DTRs) and Sequential Multiple Assignment Randomized Trials (SMARTs) in mental health studies. Shanghai archives of psychiatry, 26(6), 376. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4311115/
Liu et al. (2015). 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.