A nested semiparametric method for case‐control study with missingness
提出一种嵌套半参数模型，用于分析部分个体真实病例状态缺失的病例对照研究，通过引入非病例概念进行插补，并估计真实病例与对照的比值比参数。
Abstract We propose a nested semiparametric model to analyze a case‐control study where genuine case status is missing for some individuals. The concept of a noncase is introduced to allow for the imputation of the missing genuine cases. The odds ratio parameter of the genuine cases compared to controls is of interest. The imputation procedure predicts the probability of being a genuine case compared to a noncase semiparametrically in a dimension reduction fashion. This procedure is flexible, and vastly generalizes the existing methods. We establish the root‐ asymptotic normality of the odds ratio parameter estimator. Our method yields stable odds ratio parameter estimation owing to the application of an efficient semiparametric sufficient dimension reduction estimator. We conduct finite sample numerical simulations to illustrate the performance of our approach, and apply it to a dilated cardiomyopathy study.