  The onset of several silent, chronic diseases such as diabetes can be
detected only through diagnostic tests. Due to cost considerations,
self-reported outcomes are routinely collected in lieu of expensive diagnostic
tests in large-scale prospective investigations such as the Women's Health
Initiative. However, self-reported outcomes are subject to imperfect
sensitivity and specificity. Using a semiparametric likelihood-based approach,
we present time to event models to estimate the association of one or more
covariates with a error-prone, self-reported outcome. We present simulation
studies to assess the effect of error in self-reported outcomes with regard to
bias in the estimation of the regression parameter of interest. We apply the
proposed methods to prospective data from 152,830 women enrolled in the Women's
Health Initiative to evaluate the effect of statin use with the risk of
incident diabetes mellitus among postmenopausal women. The current analysis is
based on follow-up through 2010, with a median duration of follow-up of 12.1
years. The methods proposed in this paper are readily implemented using our
freely available R software package icensmis, which is available at the
Comprehensive R Archive Network (CRAN) website.
