Equivalence Between Conditional and Random-Effects Likelihoods for Pair-Matched Case-Control Studies
配对病例对照研究中条件似然与随机效应似然的等价性AI 解读 ↓
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配对病例对照研究中条件似然与随机效应似然的等价性
- 研究问题
- 在含多个分类协变量的配对病例对照研究中，条件似然与随机效应模型能否精确对应，并共享条件方法的稳健性质？
- 方法与思路
- 作者构造与条件方法等价的特定随机效应模型，在全似然框架中无需指定随机效应分布的参数形式，并设置病例对照先验可交换性。
- 材料陈述的贡献
- 摘要称证明两类方法的精确对应，解释相应随机效应模型的稳健性质，并据此讨论条件似然扩展及基因分型误分类应用。
- 阅读关注点
- 应核对配对设计和分类协变量限制、随机效应分布的约束、等价性针对的似然对象，以及可交换性与稳健性的具体含义。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要；不能将配对病例对照的等价性外推至任意分层模型，扩展条件未说明，不能推断刊本变化。
原文摘要
Two approaches dominate the analysis of highly stratified data sets: use of conditional likelihood and random-effects models. Conditioning is more traditional and has attractive robustness properties. Contemporary random-effects approaches rely more on accurate assumptions but can be applied to a larger class of problems. For pair-matched studies with arbitrary numbers of categorical covariates, we reconcile these two approaches, showing that the conditional approach has an exact interpretation as a specific type of random-effects model. The random-effects models that provide equivalence with conditioning naturally inherit all the attractive properties of that approach, and we argue they are a pragmatic model choice. We also discuss desirable characteristics of the random-effects model that are entirely novel and that have foundational implications. For example, although our model is specified entirely within a full-likelihood framework, its assumed random-effects distribution does not need a parametric form; we make explicit the robustness that this provides. In addition, our model forces a priori exchangeability of case and control status, an attractive property for objective analyses. The equivalence of the two approaches justifies extensions of conditional likelihood methods to new situations. We give a motivating example from a real problem concerning misclassification error in a genotyping process, and discuss refinements specific to genetic applications.