Targeted maximum likelihood estimation for longitudinal two-stage designs with outcome subsampling
结局子抽样纵向两阶段设计的目标最大似然估计AI 解读 ↓
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结局子抽样纵向两阶段设计的目标最大似然估计
- 研究问题
- 对失访者仅追踪其中一部分结局的纵向研究，如何利用完整协变量历史提高因果参数估计效率，并获得可靠置信区间？
- 方法与思路
- 作者扩展两阶段设计的 IPCW-TMLE 至纵向情形，另将第二阶段抽样指标作为干预节点纳入序贯回归，并采用交叉拟合方差估计。
- 材料陈述的贡献
- 摘要报告，在所研究模拟中，LTMLE 相对已知权重的加权 Kaplan–Meier 方差最高降低百分之七十三，交叉拟合改善区间覆盖。
- 阅读关注点
- 应核对失访与结局抽样的识别条件、已知抽样权重为何仍可估计和更新，以及方差比较与覆盖率结论对应哪些模拟设置。
- 阅读边界
- 所给版本为 arXiv 预印本，不能据此认定已同行评审或获期刊接收；摘要未说明完整识别条件，所报效率增益不能视为普遍保证。
原文摘要
We consider efficient estimation of causal parameters in longitudinal two-stage designs with outcome subsampling, motivated by resampling designs in HIV-related mortality studies. In these studies, many participants become lost to follow-up; resampling designs address this by tracing a subset of lost individuals to ascertain their outcomes. Analyses often use inverse-probability-weighted Kaplan-Meier (wKM) estimators that discard longitudinal covariate information and suffer from efficiency losses. We note that resampling designs are an instance of a broader class: two-stage designs with outcome subsampling, in which a first stage collects some data on all participants and a second stage collects outcome information on a selected subset. This connection motivates two estimators. First, drawing on inverse probability of censoring weighted targeted maximum likelihood estimation (IPCW-TMLE) for two-stage designs, we develop its longitudinal extension, IPCW longitudinal TMLE (IPCW-LTMLE) and show that estimating and targeting the known second-stage sampling weights yields variance reductions of up to 36% over the use of known sampling probabilities. Second, given that inverse weighting sacrifices efficiency, we propose an LTMLE that incorporates the second-stage sampling indicator as an intervention node in the sequential regression framework, returning to plug-in estimation and avoiding inverse weighting entirely. Simulations across sample sizes show that LTMLE achieves up to 73% lower variance than wKM with known sampling weights, with reductions of 30-50% common across settings, while IPCW-LTMLE achieves consistent gains of 20-35%. We further demonstrate that cross-fitted variance estimation is essential for valid inference: standard variance estimators yield confidence interval coverage as low as 76%, while our cross-fitted variants consistently restore coverage to nominal levels.