  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 error. We conduct an extensive
numerical study to compare the proposed method to the naive approach that
ignores measurement error in terms of bias and efficiency in the estimation of
the regression parameter of interest. In all settings considered, the proposed
method showed minimal bias and maintained coverage probability, thus
outperforming the naive analysis which showed extreme bias and low coverage.
This method is applied to data from the Women's Health Initiative to assess the
association between energy and protein intake and the risk of incident diabetes
mellitus. Our results show that correcting for errors in both the self-reported
outcome and dietary exposures leads to considerably different hazard ratio
estimates than those from analyses that ignore measurement error, which
demonstrates the importance of correcting for both outcome and covariate error.
Computational details and R code for implementing the proposed method are
presented in Section S1 of the Supplementary Materials.
