Analysis of case-control data with interacting misclassified covariates

Grace Y. Yi () and Wenqing He
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Grace Y. Yi: University of Waterloo
Wenqing He: University of Western Ontario

Journal of Statistical Distributions and Applications, 2017, vol. 4, issue 1, 1-16

Abstract: 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.

Keywords: Case-control study; Interaction term; Misclassification; Prospective logistic regression; Replicated measurements (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1186/s40488-017-0069-0

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