On the estimation of inclusion probabilities for weighted analyses of nested case control studies
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嵌套病例对照研究加权分析中的纳入概率估计
- 研究问题
- 嵌套病例对照研究中，两类常用权重何时会产生偏差，权重计算应如何随目标参数和变量间因果关系调整？
- 方法与思路
- 作者比较 Kaplan–Meier 与广义加性模型权重，借助因果图选择加权变量，并用合成数据及 EPIC 研究数据说明。
- 材料陈述的贡献
- 摘要指出，队列成员无法被抽中可导致前者偏差，遗漏匹配因素交互可导致后者偏差，并将后者扩展到仅纳入部分病例的设计。
- 阅读关注点
- 核对三类目标参数对应的加权变量、不可纳入成员的判定、匹配因素交互处理，以及真实数据分析如何支持偏差诊断。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核验；摘要未给出偏差大小、具体识别条件或权重估计误差的推断处理。
原文摘要
Nested case-control (NCC) studies are a widely adopted design in epidemiology to investigate exposure-disease relationships. This paper examines weighted analyses in NCC studies, focusing on two prominent weighting methods: Kaplan-Meier (KM) weights and Generalized Additive Model (GAM) weights. We consider three target estimands: log-hazard ratios, conditional survival, and associations between exposures. While KM- and GAM-weights are generally robust, we identify specific scenarios where they can lead to biased estimates. We demonstrate that KM-weights can lead to biased estimates when a proportion of the originating cohort is effectively ineligible for NCC selection, particularly with small case proportions or numerous matching factors. Instead, GAM-weights can yield biased results if interactions between matching factors influence disease risk and are not adequately incorporated into weight calculation. Using Directed Acyclic Graphs (DAGs), we develop a framework to systematically determine which variables should be included in weight calculations. We show that the optimal set of variables depends on the target estimand and the causal relationships between matching factors, exposures, and disease risk. We illustrate our findings with both synthetic and real data from the European Prospective Investigation into Cancer and nutrition (EPIC) study. Additionally, we extend the application of GAM-weights to "untypical" NCC studies, where only a subset of cases are included. Our work provides crucial insights for conducting accurate and robust weighted analyses in NCC studies.