This application proposes to design and implement a series of simulation studies and secondary data analyses to compare different design and analysis approaches to sequential decision making in the context of alcohol treatment. In this proposal, we focus on selection of treatment regimes, using Bayesian and causal models. Our goal is to demonstrate that these approaches can be understood and implemented by clinical researchers.
This application proposes to design and implement a series of simulation studies and secondary data analyses to compare different design and analysis approaches to sequential decision making in the context of alcohol treatment. In this proposal, we focus on selection of treatment regimes, using Bayesian and causal models. Our goal is to demonstrate that these approaches can be understood and implemented by clinical researchers.
| Lu, Xi; Nahum-Shani, Inbal; Kasari, Connie et al. (2016) Comparing dynamic treatment regimes using repeated-measures outcomes: modeling considerations in SMART studies. Stat Med 35:1595-615 |
| Lu, Xi; Lynch, Kevin G; Oslin, David W et al. (2016) Comparing treatment policies with assistance from the structural nested mean model. Biometrics 72:10-9 |
| Ertefaie, Ashkan; Wu, Tianshuang; Lynch, Kevin G et al. (2016) Identifying a set that contains the best dynamic treatment regimes. Biostatistics 17:135-48 |
| Lei, H; Nahum-Shani, I; Lynch, K et al. (2012) A ""SMART"" design for building individualized treatment sequences. Annu Rev Clin Psychol 8:21-48 |
