Missing exposure data in stereotype regression model: application to matched case-control study with disease subclassification

Abstract

With advances in modern medicine and clinical diagnosis, case-control data with characterization of finer subtypes of cases are often available. In matched case-control studies, missingness in exposure values often leads to deletion of entire stratum, and thus entails a significant loss in information. When subtypes of cases are treated as categorical outcomes, the data are further stratified and deletion of observations becomes even more expensive in terms of precision of the category-specific odds-ratio parameters, especially using the multinomial logit model. The stereotype regression model for categorical responses lies intermediate between the proportional odds and the multinomial or baseline category logit model. The use of this class of models has been limited as the structure of the model implies certain inferential challenges with nonidentifiability and nonlinearity in the parameters. We illustrate how to handle missing data in matched case-control studies with finer disease subclassification within the cases under a stereotype regression model. We present both Monte Carlo based full Bayesian approach and expectation/conditional maximization algorithm for the estimation of model parameters in the presence of a completely general missingness mechanism. We illustrate our methods by using data from an ongoing matched case-control study of colorectal cancer. Simulation results are presented under various missing data mechanisms and departures from modeling assumptions.

Figure 1

Posterior density plot corresponding to the log odds ratio parameters in 1:1 matched MECC study data with numerical summaries and estimates as presented in Table 1. The left plot corresponds to participation in sports (X) and the right plot corresponds to statin use (Z₁). The results are based on 10,000 samples generated from the posterior distribution of each parameter.