An Approximate Quasi-Likelihood Approach for Error-Prone Failure Time Outcomes and Exposures
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Measurement error arises commonly in clinical research settings that rely on data from electronic health records or large observational cohorts. In particular, self-reported outcomes are typical in cohort studies for chronic diseases such as diabetes in order to avoid the burden of expensive diagnostic tests. Dietary intake, which is also commonly collected by self-report and subject to measurement error, is a major factor linked to diabetes and other chronic diseases. These errors can bias exposure-disease associations that ultimately can mislead clinical decision-making. We have extended an existing semiparametric likelihood-based method for handling error-prone, discrete failure time outcomes to also address covariate measurement error. We conduct an extensive numerical study to evaluate the proposed method in terms of bias and efficiency in the estimation of the regression parameter of interest. This method is applied to data from the Women's Health Initiative. Upon implementing the proposed method, we are able to assess the association between energy and protein intake and the risk of incident diabetes mellitus, correcting for the errors in both the self-reported outcome and dietary exposures.
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