从累积权重到边际密度比：序贯目标试验模拟中的方案内估计
From Cumulative Weights to Marginal Density Ratios: Per-Protocol Estimation in Sequential Target Trial Emulation
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中文总结 AI 辅助
该研究针对序贯目标试验模拟中方案内效应估计的挑战，提出基于边际密度比的双重稳健方法，经模拟验证其性能优于传统累积权重法，是有前景的替代方案。
中文摘要 AI 辅助
序贯目标试验模拟通过在多个基线时间评估合格性，利用观察性数据模拟一系列随机试验。在此场景中估计方案内效应颇具挑战，因为治疗偏离和失访会在随时间保持可观察且依从的个体中引发选择偏差。传统逆概率方法使用由估计的依从性和删失概率构建的累积权重来解决该选择问题，但这些权重可能具有高变异性，导致效应估计不稳定且不精确。我们提出一种基于边际密度比（MDR）的不同方法：MDR直接比较目标治疗策略下将保持无事件的个体的状态分布，与观察到的依从个体的对应分布。我们使用纵向g-计算生成目标风险集，并利用概率分类器估计用于重新加权观察结果的密度比。在此方法基础上，我们还开发了一种双重稳健扩展。模拟研究显示其性能良好，表明当MDR的识别假设合理时，MDR加权是累积纵向权重的有前景替代方案。
英文摘要
Sequential target trial emulation evaluates eligibility at multiple baseline times to emulate a sequence of randomized trials using observational data. Estimating per-protocol effects in this setting is challenging because treatment deviations and loss to follow-up induce selection among individuals who remain observed and adherent over time. Conventional inverse-probability methods address this selection using cumulative weights constructed from estimated adherence and censoring probabilities, but these weights can be highly variable, leading to unstable and imprecise effect estimates. We propose a different approach based on marginal density ratios (MDRs). The MDR directly compares the state distribution among individuals who would remain event-free under a target treatment strategy with the corresponding distribution among observed-adherent individuals. We use longitudinal g-computation to generate the target risk sets and a probabilistic classifier to estimate density ratios for reweighting the observed outcomes. Building on this approach, we also develop a doubly robust extension. Favorable performance across the simulation study suggests that MDR weighting is a promising alternative to cumulative longitudinal weights when its identification assumptions are plausible.
