A Nested Semiparametric Method for Case-control study with missingness

Authors

Ge Zhao, Portland State UniversityFollow
Yanyuan Ma, Penn State University
Jill Schnall Hasler, University of Pennsylvania Perelman School of Medicine, Philadelphia
Scott Damrauer, University of Pennsylvania Perelman School of Medicine, Philadelphia
Michael Levin, University of Pennsylvania Perelman School of Medicine, Philadelphia
Jinbo Chen, University of Pennsylvania Perelman School of Medicine, Philadelphia

Published In

Scandinavian Journal of Statistics

Document Type

Article

Publication Date

8-2023

Subjects

Semiparametrics -- case-control study

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-n 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.

Rights

Copyright (c) 2023 The Authors

This work is licensed under a Creative Commons Attribution 4.0 International License.

Locate the Document

https://doi-org/10.1111/sjos.12673

DOI

10.1111/sjos.12673

Persistent Identifier

https://archives.pdx.edu/ds/psu/40745

Citation Details

Zhao, G., Ma, Y., Hasler, J. S., Damrauer, S., Levin, M., & Chen, J. A Nested Semiparametric Method for Case‐Control Study with Missingness. Scandinavian Journal of Statistics.