Analysis of case-control data with interacting misclassified covariates

• Main Authors: Grace Y. Yi, Wenqing He
• Format: Article
• Language: English
• Published: SpringerOpen 2017-10-01
• Series: Journal of Statistical Distributions and Applications
• Subjects: Case-control study; Interaction term; Misclassification; Prospective logistic regression; Replicated measurements
• Online Access: http://link.springer.com/article/10.1186/s40488-017-0069-0

Abstract Case-control studies are important and useful methods for studying health outcomes and many methods have been developed for analyzing case-control data. Those methods, however, are vulnerable to mismeasurement of variables; biased results are often produced if such a feature is ignored. In this paper, we develop an inference method for handling case-control data with interacting misclassified covariates. We use the prospective logistic regression model to feature the development of the disease. To characterize the misclassification process, we consider a practical situation where replicated measurements of error-prone covariates are available. Our work is motivated in part by a breast cancer case-control study where two binary covariates are subject to misclassification. Extensions to other settings are outlined.