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.

翻译：嵌套病例对照研究是流行病学中广泛采用的一种研究设计，用于探究暴露-疾病关系。本文考察了嵌套病例对照研究中的加权分析，重点关注两种主要的加权方法：Kaplan-Meier权重和广义可加模型权重。我们考虑了三个目标估计量：对数风险比、条件生存率以及暴露之间的关联。虽然KM权重和GAM权重通常具有稳健性，但我们识别了它们可能导致估计偏差的特定场景。我们证明，当原始队列中有一部分个体实际上不符合NCC选择条件时，尤其是在病例比例较小或匹配因素众多的情况下，KM权重可能导致有偏估计。相反，如果匹配因素之间的交互作用影响疾病风险，且未充分纳入权重计算，则GAM权重可能产生有偏结果。利用有向无环图，我们开发了一个框架，用于系统性地确定权重计算中应包含哪些变量。我们证明，最优变量集取决于目标估计量以及匹配因素、暴露和疾病风险之间的因果关系。我们使用来自欧洲癌症与营养前瞻性调查研究的合成数据和真实数据阐明了我们的发现。此外，我们将GAM权重的应用扩展到"非典型"嵌套病例对照研究，即仅纳入部分病例的情况。我们的工作为在嵌套病例对照研究中开展准确且稳健的加权分析提供了关键见解。