A nested semiparametric method for case-control study with missingness

Ge Zhao, Yanyuan Ma, Jill Schnall Hasler, Scott Damrauer, Michael Levin, Jinbo Chen

Research output: Contribution to journalArticlepeer-review


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- (Formula presented.) 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.

Original languageEnglish
Pages (from-to)201-219
Number of pages19
JournalScandinavian Journal of Statistics
Issue number1
Early online dateAug 9 2023
StatePublished - Mar 2024


  • case-control study
  • missingness
  • semiparametrics


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