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.
Highlights
Case-control studies are important and useful methods for studying rare health outcomes, such as rare diseases
The primary purpose of a case-control study is to investigate how risk factors are associated with the disease incidence, and the study typically involves the comparison of cases with controls
We report the bias (Bias), the model-based standard error (SEM), and the 95% confidence interval coverage rate (CR%), and the results are reported in Tables 6, 7 and 8, each corresponding to a size scenario
Summary
Case-control studies are important and useful methods for studying rare health outcomes, such as rare diseases. When a misclassification probability, say P Xa∗ = 0|Xa = 1 , is bigger than 1/2, the observed measurements Xa∗ carry useless information of Xa; using such observations to estimate the model parameter, no matter how an estimation method is developed, is even worse than using artificial data generated from flipping a fair coin. Repeated measurements of Xa and Xs were collected for those women on two occasions, and the measurements are given in Table 4 where one subject has missing observations of Xs. We analyze the data using the proposed method described in Section 4 and the naive method with misclassification in Xa and Xs ignored, called Analysis 1 and Analysis 2, respectively.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Statistical Distributions and Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
