Matched Design for Marginal Causal Effect on Restricted Mean Survival Time in Observational Studies
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观察性研究中限制平均生存时间边际因果效应的匹配设计
- 研究问题
- 在存在混杂的观察性生存数据中，如何用限制平均生存时间差衡量边际因果效应，并评估未测量混杂的影响？
- 方法与思路
- 作者结合倾向得分匹配与限制平均生存时间差估计，计算考虑配对相关性的方差，并将 E-value 方法改用于匹配数据。
- 材料陈述的贡献
- 摘要称估计量渐近无偏，模拟中表现充分且优于多种比较方法，并用 ARIC 数据研究吸烟对无卒中生存的影响。
- 阅读关注点
- 应核对因果识别及删失处理条件、时间窗口选择、匹配导致的相关性处理，以及敏感性分析对未测量混杂的刻画。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未经核实；摘要未列识别条件，敏感性分析也不能据此证明已消除未测量混杂。
原文摘要
Investigating the causal relationship between exposure and the time-to-event outcome is an important topic in biomedical research. Previous literature has discussed the potential issues of using the hazard ratio as a marginal causal effect measure due to its noncollapsibility property. In this paper, we advocate using the restricted mean survival time (RMST) difference as the marginal causal effect measure, which is collapsible and has a simple interpretation as the difference of area under survival curves over a certain time horizon. To address both measured and unmeasured confounding, a matched design with sensitivity analysis is proposed. Matching is used to pair similar treated and untreated subjects together, which is more robust to outcome model misspecification. Our propensity score matched RMST difference estimator is shown to be asymptotically unbiased and the corresponding variance estimator is calculated by accounting for the correlation due to matching. The simulation study also demonstrates that our method has adequate empirical performance and outperforms many competing methods used in practice. To assess the impact of unmeasured confounding, we develop a sensitivity analysis strategy by adapting the E-value approach to matched data. We apply the proposed method to the Atherosclerosis Risk in Communities Study (ARIC) to examine the causal effect of smoking on stroke-free survival.