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

Semiparametric time to event models in the presence of error-prone, self-reported outcomes - With application to the women's health initiative