AI 中文总结
该研究针对加性风险序贯试验模拟，指出行级稳健方差存在反保守性，提出受试者聚类方差和乘子自助法可改善置信区间覆盖率，并通过模拟和实际数据验证了方法有效性，相关代码已整合至steCI R包。
AI 中文摘要
序贯试验模拟（STE）通过逆概率加权堆叠嵌套模拟试验，以评估持续治疗的效果。加性风险STE用于估计边际风险差的估计量，推荐使用非参数自助法，但未评估其覆盖率。基于一个可正确指定的机制（存在少量已披露的残差），我们比较了两种估计量的解析标准误与自助法标准误。利用加性风险估计方程的闭式线性特性，我们推导了影响函数，并证明默认的行级稳健方差对于边际风险差曲线是不一致的，且在恒定风险差的情况下也经验性地表现出同样的失效：行级方差遗漏了受试者内跨试验协方差，该协方差在我们验证的符号条件下为正，且除第一个时间区间外，在所有时间区间均具有反保守性——仅在协方差为零的第一个时间区间，行级标准误未受损害。受试者聚类方差和乘子自助法对于固定权重线性化是一致的，且支持同时置信带，其测得的覆盖率为0.88。在我们的所有模拟中，基于模型和行级稳健的区间均具有反保守性，且随样本量增大而恶化，当n=5000时，覆盖率降至0.71；聚类使恒定风险差在n=5000时的覆盖率维持在0.86附近，乘子自助法则使风险差曲线的覆盖率维持在0.90附近（最长时间区间为0.86）。STE恒定风险差是随时间变化效应的设计加权汇总，依赖于试验结构。我们在斯坦福心脏移植数据上进行了说明，并将其提供在steCI R包中。
英文摘要
Sequential trial emulation (STE) estimates the effect of a sustained treatment by stacking nested emulated trials with inverse-probability weighting. Additive-hazards STE estimators of the marginal risk difference recommend the nonparametric bootstrap without evaluating its coverage. Using a correctly-specifiable mechanism (up to a small, disclosed residual), we compare analytic and bootstrap standard errors for both estimands. Exploiting the closed-form linearity of the additive-hazards estimating equation, we derive the influence functions and prove the default row-level robust variance inconsistent for the marginal risk-difference curve, and show the same failure empirically for the constant hazard difference: the row-level variance omits a within-subject cross-trial covariance that is positive under a sign condition we verify across our mechanisms, and is anticonservative at every horizon except the first - only there, where the covariance is zero, is the row-level standard error unimpaired. The subject-clustered variance and multiplier bootstrap are consistent for the fixed-weight linearisation and support simultaneous confidence bands, whose measured coverage is 0.88. Across our simulations the model-based and row-level robust intervals are anticonservative and worsen with sample size, coverage falling to 0.71 at n=5000; clustering leaves the constant-hazard-difference coverage near 0.86 at n=5000, and the multiplier bootstrap leaves the risk-difference-curve coverage near 0.90 (0.86 at the longest horizon). The STE constant hazard difference is a design-weighted summary of a time-varying effect, dependent on the trial structure. We illustrate on the Stanford heart transplant data and provide them in the steCI R package.
Comments28 pages, 3 figures (main text); 52-page supplementary appendix included as an ancillary file. Companion R package and full reproducibility compendium: https://github.com/ehsanx/steCI
