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Measurement error arises commonly in clinical research settings that rely ondata from electronic health records or large observational cohorts. Inparticular, self-reported outcomes are typical in cohort studies for chronicdiseases such as diabetes in order to avoid the burden of expensive diagnostictests. Dietary intake, which is also commonly collected by self-report andsubject to measurement error, is a major factor linked to diabetes and otherchronic diseases. These errors can bias exposure-disease associations thatultimately can mislead clinical decision-making. We have extended an existingsemiparametric likelihood-based method for handling error-prone, discretefailure time outcomes to also address covariate error. We conduct an extensivenumerical study to compare the proposed method to the naive approach thatignores measurement error in terms of bias and efficiency in the estimation ofthe regression parameter of interest. In all settings considered, the proposedmethod showed minimal bias and maintained coverage probability, thusoutperforming the naive analysis which showed extreme bias and low coverage.This method is applied to data from the Women's Health Initiative to assess theassociation between energy and protein intake and the risk of incident diabetesmellitus. Our results show that correcting for errors in both the self-reportedoutcome and dietary exposures leads to considerably different hazard ratioestimates than those from analyses that ignore measurement error, whichdemonstrates the importance of correcting for both outcome and covariate error.Computational details and R code for implementing the proposed method arepresented in Section S1 of the Supplementary Materials.

An Approximate Quasi-Likelihood Approach for Error-Prone Failure Time Outcomes and Exposures