基于倾向得分的病例对照研究因果推断
Causal inference via propensity scores for case-control studies
浏览论文内容
中文总结 AI 辅助
该研究针对病例对照研究中倾向得分因果推断方法应用有限的问题，提出新型两步倾向得分卡尺匹配程序，结合模拟与真实数据验证了相关估计量的有效性，可用于估计因果相对效应。
中文摘要 AI 辅助
倾向得分因果推断方法在队列研究和实验设计中应用日益广泛，但在病例对照等依赖结局的抽样方案中的发展和应用仍有限。病例对照研究是对有无目标结局的个体进行抽样，以估计既往暴露的效应。当为观察性设计时，需对混杂偏倚进行统计调整。在病例对照研究中，可利用对照数据拟合倾向得分模型，假设对照在协变量条件下的暴露分布与源人群具有代表性（即“对照可交换性”）。本文首先证明，在三种不同的病例对照设计变体中，利用对照拟合的倾向得分可估计相对效应（如因果风险比）。我们采用两种现有估计量：逆概率处理加权法和一种高效的双稳健估计量，同时提出一种适用于病例对照设计的新型两步倾向得分卡尺匹配程序，并引入新的诊断工具以验证两种必要的重叠类型。随后，我们通过模拟数据对比这些估计量，并将其应用于探究规律服用阿司匹林与卵巢癌风险的关联。
英文摘要
Propensity score methods for causal inference are increasingly being used in cohort and experimental designs, but their development and uptake in outcome-dependent sampling schemes, such as case-control studies, remains limited. Case-control studies involve the sampling of individuals with and without an outcome of interest with the goal of estimating the effects of past exposures. When the design is observational, statistical adjustment for confounding bias is necessary. In case-control studies, propensity score models can be fit using control data under the assumption that the controls are representative of the source population with respect to their exposure distribution conditional on covariates ("control exchangeability"). In this paper, we first demonstrate that relative effects, such as causal risk ratios, are estimable under three different case-control design variants using control-fitted propensity scores. We appropriate two existing estimators for these designs: inverse probability of treatment weighting and an efficient and doubly robust estimator. We also introduce a novel two-step propensity score caliper-matching procedure for case-control designs. We introduce novel diagnostic tools to verify two necessary types of overlap. We then contrast our estimators using simulated data and apply them to examine the association between regular aspirin use and ovarian cancer risk.
