A Nested Semiparametric Method for Case-Control Study with Missingness

We propose a nested semiparametric model to analyze a case-control study with missingness on the genuine cases. The concept of non-case is introduced to allow imputing the missing genuine cases. The odds ratio parameter of the genuine cases is of interest. The imputation predicts the probability of genuine case over non-case semiparametrically in a dimension reduction fashion. This procedure is flexible, and vastly generalizes the existing methods. We establish the root-n asymptotic normality of the odds ratio parameter estimator. Our method yields a 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.

Case-control study

Missingness

Semiparametrics