Estimating Optimal Dynamic Treatment Regimes With Survival Outcomes
生存结局下最优动态治疗策略的估计AI 解读 ↓
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生存结局下最优动态治疗策略的估计
- 研究问题
- 对于存在右删失的生存结局，如何从观察性数据估计随患者信息调整的多阶段治疗策略，以获得最优预期结局？
- 方法与思路
- 作者提出动态加权生存建模，通过依次求解加权广义估计方程估计策略，并利用权重平衡性质论证一致性。
- 材料陈述的贡献
- 摘要称方法具有双重稳健性，给出一致性证明与渐近方差公式，并以类风湿关节炎队列示例及 DTRreg 软件实现说明应用。
- 阅读关注点
- 应核对观察数据下策略识别与删失处理条件、双重稳健性的准确含义、各阶段权重，以及最优结局目标和方差计算。
- 阅读边界
- OpenAlex 索引摘要版本未注明；摘要未说明双重稳健性的具体模型组合或识别条件，不能据此判断任意删失机制下的有效性。
原文摘要
Gabrielle Simoneaua* , Erica E. M. Moodiea , Jagtar S. Nijjarb, Robert W. Platta & the Scottish Early Rheumatoid Arthritis Inception Cohort Investigatorsa Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada; b Department of Medicine, University of Cambridge, Cambridge, UKColor versions of one or more of the figures in the article can be found online at www.tandfonline.com/r/JASA.Supplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.CONTACT Gabrielle Simoneau gabrielle.simoneau@mail.mcgill.ca Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Purvis Hall, 1020 Pine West, Montreal, QC, Canada, H3A 1A2.AbstractThe statistical study of precision medicine is concerned with dynamic treatment regimes (DTRs) in which treatment decisions are tailored to patient-level information. Individuals are followed through multiple stages of clinical intervention, and the goal is to perform inferences on the sequence of personalized treatment decision rules to be applied in practice. Of interest is the identification of an optimal DTR, that is, the sequence of treatment decisions that yields the best expected outcome. Statistical methods for identifying optimal DTRs from observational data are theoretically complex and not easily implementable by researchers, especially when the outcome of interest is survival time. We propose a doubly robust, easy to implement method for estimating optimal DTRs with survival endpoints subject to right-censoring which requires solving a series of weighted generalized estimating equations. We provide a proof of consistency that relies on the balancing property of the weights and derive a formula for the asymptotic variance of the resulting estimators. We illustrate our novel approach with an application to the treatment of rheumatoid arthritis using observational data from the Scottish Early Rheumatoid Arthritis Inception Cohort. Our method, called dynamic weighted survival modeling, has been implemented in the DTRreg R package. Supplementary materials for this article are available online.