ArticleJournal of the American Statistical Association2016
Bayesian Nonparametric Estimation for Dynamic Treatment Regimes with Sequential Transition Times.
Article in Journal of the American Statistical Association, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- A Bayesian latent class approach to causal inference with longitudinal data.Statistical methods in medical research · 2025Article
- Bayesian Nonparametric Model for Heterogeneous Treatment Effects With Zero-Inflated Data.Statistics in medicine · 2024Article
- Bayesian semiparametric model for sequential treatment decisions with informative timing.Biostatistics (Oxford, England) · 2024Article
- A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes.BMC medical research methodology · 2024Article
- Using joint models for longitudinal and time-to-event data to investigate the causal effect of salvage therapy after prostatectomy.Statistical methods in medical research · 2024Article
- A BAYESIAN MACHINE LEARNING APPROACH FOR ESTIMATING HETEROGENEOUS SURVIVOR CAUSAL EFFECTS: APPLICATIONS TO A CRITICAL CARE TRIAL.The annals of applied statistics · 2024Article
- Postprogression therapy and confounding for the estimated treatment effect on overall survival in phase III oncology trials.BMJ oncology · 2024Article
- A BAYESIAN DECISION FRAMEWORK FOR OPTIMIZING SEQUENTIAL COMBINATION ANTIRETROVIRAL THERAPY IN PEOPLE WITH HIV.The annals of applied statistics · 2023Article
- Interpreting Randomized Controlled Trials.Cancers · 2023Review
- Nonparametric Bayesian Q-learning for optimization of dynamic treatment regimes in the presence of partial compliance.Statistical methods in medical research · 2023Article
- Multi-stage optimal dynamic treatment regimes for survival outcomes with dependent censoring.Biometrika · 2023Article
- Less is More? First Impressions From COSMIC-313.Cancer investigation · 2023Article
- Review
- The Big Data Paradox in Clinical Practice.Cancer investigation · 2022Article
- A Bayesian nonparametric approach for evaluating the causal effect of treatment in randomized trials with semi-competing risks.Biostatistics (Oxford, England) · 2022Article
- A practical introduction to Bayesian estimation of causal effects: Parametric and nonparametric approaches.Statistics in medicine · 2021Article
- Ascertaining properties of weighting in the estimation of optimal treatment regimes under monotone missingness.Statistics in medicine · 2020Article
- Bayesian Nonparametric Policy Search with Application to Periodontal Recall Intervals.Journal of the American Statistical Association · 2020Article
- Article
- Bayesian non-parametric survival regression for optimizing precision dosing of intravenous busulfan in allogeneic stem cell transplantation.Journal of the Royal Statistical Society. Series C, Applied statistics · 2019Article
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4 authors.
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Abstract
We analyze a dataset arising from a clinical trial involving multi-stage chemotherapy regimes for acute leukemia. The trial design was a 2 × 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 https://www.ma.utexas.edu/users/yxu/.
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