A New Causal Approach to Account for Treatment Switching in Randomized Experiments under a Structural Cumulative Survival Model
结构累积生存模型下处理随机试验治疗转换的因果方法AI 解读 ↓
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结构累积生存模型下处理随机试验治疗转换的因果方法
- 研究问题
- 随机试验中患者选择性转换治疗后，如何估计治疗对事件时间的因果效应，并避免转换行为稀释组间比较？
- 方法与思路
- 作者在结构累积生存模型下利用随机分组作为工具变量，无需人工删失，构造效应估计、方差估计及重抽样一致置信带。
- 材料陈述的贡献
- 摘要称估计量一致且渐近高斯，并提供累积强度尺度的因果差异置信带、试验再分析、模拟和名为 ivsacim 的 R 包。
- 阅读关注点
- 应核对随机分组作为工具变量的有效性条件、治疗转换机制、模型限制，以及累积强度差异与临床事件风险的关系。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；仅凭摘要不能判断工具变量条件、正则条件与模拟覆盖率，也未核验软件。
原文摘要
Treatment switching in a randomized controlled trial is said to occur when a patient randomized to one treatment arm switches to another treatment arm during follow-up. This can occur at the point of disease progression, whereby patients in the control arm may be offered the experimental treatment. It is widely known that failure to account for treatment switching can seriously dilute the estimated effect of treatment on overall survival. In this paper, we aim to account for the potential impact of treatment switching in a re-analysis evaluating the treatment effect of NucleosideReverse Transcriptase Inhibitors (NRTIs) on a safety outcome (time to first severe or worse sign or symptom) in participants receiving a new antiretroviral regimen that either included or omitted NRTIs in the Optimized Treatment That Includes or OmitsNRTIs (OPTIONS) trial. We propose an estimator of a treatment causal effect under a structural cumulative survival model (SCSM) that leverages randomization as an instrumental variable to account for selective treatment switching. Unlike Robins' accelerated failure time model often used to address treatment switching, the proposed approach avoids the need for artificial censoring for estimation. We establish that the proposed estimator is uniformly consistent and asymptotically Gaussian under standard regularity conditions. A consistent variance estimator is also given and a simple resampling approach provides uniform confidence bands for the causal difference comparing treatment groups overtime on the cumulative intensity scale. We develop an R package named "ivsacim" implementing all proposed methods, freely available to download from R CRAN. We examine the finite performance of the estimator via extensive simulations.