论文标题
蒙特卡洛灵敏度分析用于动态治疗方案中未衡量的混杂
Monte Carlo Sensitivity Analysis for Unmeasured Confounding in Dynamic Treatment Regimes
论文作者
论文摘要
数据驱动的用于个性化治疗作业的方法吸引了临床医生和研究人员的关注。动态治疗方案通过一系列决策规则将其形式化，这些决策规则将个体患者特征映射到建议的治疗。观察性研究通常用于估计动态治疗方案，这是由于进行顺序多次分配随机试验的潜在成本。但是，通过观察数据估算动态治疗方案可能会导致由于无法衡量的混杂而导致的估计制度偏见。灵敏度分析对于评估研究的结论对潜在的无法测量的混杂因素有用。蒙特卡洛敏感性分析是一种概率方法，涉及从分布中提出和采样的参数，以控制偏见。我们提出了一种通过在动态治疗方案的估计中进行混淆，对偏差进行蒙特卡洛敏感性分析的方法。我们通过模拟研究证明了该程序的性能，并将其应用于一项观察性研究，该研究使用Kaiser Permanente Washington（KPWA）的数据来调整使用抗抑郁药来减轻抑郁症状的使用。
Data-driven methods for personalizing treatment assignment have garnered much attention from clinicians and researchers. Dynamic treatment regimes formalize this through a sequence of decision rules that map individual patient characteristics to a recommended treatment. Observational studies are commonly used for estimating dynamic treatment regimes due to the potentially prohibitive costs of conducting sequential multiple assignment randomized trials. However, estimating a dynamic treatment regime from observational data can lead to bias in the estimated regime due to unmeasured confounding. Sensitivity analyses are useful for assessing how robust the conclusions of the study are to a potential unmeasured confounder. A Monte Carlo sensitivity analysis is a probabilistic approach that involves positing and sampling from distributions for the parameters governing the bias. We propose a method for performing a Monte Carlo sensitivity analysis of the bias due to unmeasured confounding in the estimation of dynamic treatment regimes. We demonstrate the performance of the proposed procedure with a simulation study and apply it to an observational study examining tailoring the use of antidepressants for reducing symptoms of depression using data from Kaiser Permanente Washington (KPWA).
