Bayesian Nonparametric Estimation for Dynamic Treatment Regimes With Sequential Transition Times

• 成果类型: Article
• 署名作者: Xu, Yanxun; Muller, Peter; Wahed, Abdus S.; Thall, Peter F.
• 署名单位: University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; University of Texas System; UTMD Anderson Cancer Center
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2015.1086353
• 发表日期: 2016
• 页码: 921-935
• 关键词: estimating individualized treatment; 2-stage randomization designs; MARGINAL STRUCTURAL MODELS; survival distributions; treatment strategies; Causal Inference; sampling methods; SUBJECT; TRIAL

摘要

We analyze a dataset arising from a clinical trial involving multi-stage chemotherapy regimes for acute leukemia. The trial design was a 2 x 2 factorial for frontline therapies only. Motivated, by the idea that subsequent salvage treatments affect survival time, we model therapy as a dynamic treatment regime (DTR), that is, an alternating sequence of adaptive treatments or other actions and transition times between disease states. These sequences may vary substantially between patients, depending on how the regime plays out. To evaluate the regimes, mean overall survival time is expressed as a weighted average of the means of all possible sums of successive transitions times. We assume a Bayesian nonparametric survival regression model for each transition time, with a dependent Dirichlet process prior and Gaussian process base measure (DDP-GP). Posterior simulation is implemented by Markov chain Monte Carlo (MCMC) sampling. We provide general guidelines for constructing a prior using empirical Bayes methods. The proposed approach is compared with inverse probability of treatment weighting, including a doubly robust augmented version of this approach, for both single-stage and multi-stage regimes with treatment assignment depending on baseline covariates. The simulations show that the proposed nonparametric Bayesian approach can substantially improve inference compared to existing methods. An R program for implementing the DDP-GP-based Bayesian nonparametric analysis is freely available at www.ams.jhu.edu/yxu70. Supplementary materials for this article are available online.