Inference procedures in sequential trial emulation with survival outcomes: comparing confidence intervals based on the sandwich variance estimator, bootstrap and jackknife
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生存结局序贯试验模拟的推断：夹心方差、Bootstrap 与 Jackknife 比较
- 研究问题
- 研究观察数据中序贯目标试验模拟的边际风险差区间如何构造，尤其关注小样本、低事件率与低处理比例下的覆盖率和计算成本。
- 方法与思路
- 作者在逆概率加权和潜在结果结构模型框架下，通过模拟比较非参数及线性化估计函数 Bootstrap、Jackknife 和夹心方差区间。
- 材料陈述的贡献
- 摘要称，小中样本及低事件率情景下线性化 Bootstrap 覆盖率最佳；大样本及中高事件率下夹心方差区间覆盖率与速度更佳。
- 阅读关注点
- 应核对目标试验定义、重复入组造成的依赖、权重估计误差处理，以及各模拟情景的覆盖率、区间宽度和计算时间。
- 阅读边界
- 所给来源为 arXiv 预印本，不能据此确认同行评审或期刊接收；摘要未提供覆盖率数值，所述优势仅对应列明的模拟情景。
原文摘要
Sequential trial emulation (STE) is an approach to estimating causal treatment effects by emulating a sequence of target trials from observational data. In STE, inverse probability weighting is commonly utilised to address time-varying confounding and/or dependent censoring. Then structural models for potential outcomes are applied to the weighted data to estimate treatment effects. For inference, the simple sandwich variance estimator is popular but conservative, while nonparametric bootstrap is computationally expensive, and a more efficient alternative, linearised estimating function (LEF) bootstrap, has not been adapted to STE. We evaluated the performance of various methods for constructing confidence intervals (CIs) of marginal risk differences in STE with survival outcomes by comparing the coverage of CIs based on nonparametric/LEF bootstrap, jackknife, and the sandwich variance estimator through simulations. LEF bootstrap CIs demonstrated the best coverage with small/moderate sample sizes, low event rates and low treatment prevalence, which were the motivating scenarios for STE. They were less affected by treatment group imbalance and faster to compute than nonparametric bootstrap CIs. With large sample sizes and medium/high event rates, the sandwich-variance-estimator-based CIs had the best coverage and were the fastest to compute. These findings offer guidance in constructing CIs in causal survival analysis using STE.