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

TL;DR

This paper develops semiparametric models to analyze time-to-event data with error-prone self-reported outcomes, demonstrating their effectiveness through simulations and application to women's health data on statin use and diabetes risk.

Contribution

It introduces a novel likelihood-based approach for modeling error-prone self-reported outcomes in time-to-event analysis, with an accompanying R package for implementation.

Findings

Simulation studies show reduced bias in estimates.

Application to Women's Health Initiative data reveals associations between statin use and diabetes risk.

Method performs well under various error scenarios.

Abstract

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.