Department of Population Health Sciences, Division of Biostatistics, University of Utah, Salt Lake City, UT, USA.
Department of Internal Medicine, Division of Oncology, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.
Stat Methods Med Res. 2024 May;33(5):794-806. doi: 10.1177/09622802241236954. Epub 2024 Mar 19.
Observational data (e.g. electronic health records) has become increasingly important in evidence-based research on dynamic treatment regimes, which tailor treatments over time to patients based on their characteristics and evolving clinical history. It is of great interest for clinicians and statisticians to identify an optimal dynamic treatment regime that can produce the best expected clinical outcome for each individual and thus maximize the treatment benefit over the population. Observational data impose various challenges for using statistical tools to estimate optimal dynamic treatment regimes. Notably, the task becomes more sophisticated when the clinical outcome of primary interest is time-to-event. Here, we propose a matching-based machine learning method to identify the optimal dynamic treatment regime with time-to-event outcomes subject to right-censoring using electronic health record data. In contrast to the established inverse probability weighting-based dynamic treatment regime methods, our proposed approach provides better protection against model misspecification and extreme weights in the context of treatment sequences, effectively addressing a prevalent challenge in the longitudinal analysis of electronic health record data. In simulations, the proposed method demonstrates robust performance across a range of scenarios. In addition, we illustrate the method with an application to estimate optimal dynamic treatment regimes for patients with advanced non-small cell lung cancer using a real-world, nationwide electronic health record database from Flatiron Health.
观察性数据（例如电子健康记录）在基于证据的动态治疗方案研究中变得越来越重要，这种治疗方案根据患者的特征和不断变化的临床病史，随着时间的推移为患者定制治疗方案。对于临床医生和统计学家来说，确定最佳的动态治疗方案以产生每个个体的最佳预期临床结果并最大限度地提高人群的治疗效果非常重要。观察性数据为使用统计工具估计最佳动态治疗方案带来了各种挑战。值得注意的是，当主要关注的临床结果是事件发生时间时，任务变得更加复杂。在这里，我们提出了一种基于匹配的机器学习方法，用于使用电子健康记录数据识别具有事件时间结局的最优动态治疗方案，同时考虑右删失。与基于逆概率加权的既定动态治疗方案方法不同，我们提出的方法在治疗序列方面提供了更好的模型误配和极端权重保护，有效地解决了电子健康记录数据纵向分析中的一个普遍挑战。在模拟中，该方法在一系列场景中表现出稳健的性能。此外，我们还通过使用来自 Flatiron Health 的真实全国性电子健康记录数据库为晚期非小细胞肺癌患者估计最优动态治疗方案的应用来说明该方法。
