Weighted Approach for Estimating Effects in Principal Strata with Missing Data for a Categorical Post-Baseline Variable in Randomized Controlled Trials
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随机试验中分类基线后变量缺失时主分层效应的加权估计
- 研究问题
- 当定义主分层的随机化后分类状态在对照组不可见、且治疗组部分缺失时，如何估计相应患者群体的治疗效应？
- 方法与思路
- 作者提出加权主分层方法，利用基线协变量与额外纵向信息对状态缺失患者重新加权，并讨论不同结局下的理论依据。
- 材料陈述的贡献
- 摘要称方法给出因果解释所需假设，并在模拟中获得有效推断及对某些假设违反的稳健性，同时应用于抗药抗体研究。
- 阅读关注点
- 应核对主分层的反事实定义、缺失机制与权重构造，以及哪些假设支持因果解释、模拟检验了哪些具体违反情形。
- 阅读边界
- 这是 arXiv 预印本，不能据此确认同行评审或期刊接收；摘要未列出具体假设和稳健范围，不能据此认定可处理任意缺失。
原文摘要
This research was motivated by studying anti-drug antibody (ADA) formation and its potential impact on long-term benefit of a biologic treatment in a randomized controlled trial, in which ADA status was not only unobserved in the control arm but also in a subset of patients from the experimental treatment arm. Recent literature considers the principal stratum estimand strategy to estimate treatment effect in groups of patients defined by an intercurrent status, i.e. in groups defined by a post-randomization variable only observed in one arm and potentially associated with the outcome. However, status information might be missing even for a non-negligible number of patients in the experimental arm. For this setting, a novel weighted principal stratum approach is presented: Data from patients with missing intercurrent event status were re-weighted based on baseline covariates and additional longitudinal information. A theoretical justification of the proposed approach is provided for different types of outcomes, and assumptions allowing for causal conclusions on treatment effect are specified and investigated. Simulations demonstrated that the proposed method yielded valid inference and was robust against certain violations of assumptions. The method was shown to perform well in a clinical study with ADA status as an intercurrent event.