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In large epidemiologic studies, it is typical for an inexpensive,non-invasive procedure to be used to record disease status during regularfollow-up visits, with less frequent assessment by a gold standard test.Inexpensive outcome measures like self-reported disease status are practical toobtain, but can be error-prone. Association analysis reliant on error-proneoutcomes may lead to biased results; however, restricting analyses to only datafrom the less frequently observed error-free outcome could be inefficient. Wehave developed an augmented likelihood that incorporates data from botherror-prone outcomes and a gold standard assessment. We conduct a numericalstudy to show how we can improve statistical efficiency by using the proposedmethod over standard approaches for interval-censored survival data that do notleverage auxiliary data. We extend this method for the complex survey designsetting so that it can be applied in our motivating data example. Our method isapplied to data from the Hispanic Community Health Study/Study of Latinos toassess the association between energy and protein intake and the risk ofincident diabetes. In our application, we demonstrate how our method can beused in combination with regression calibration to additionally address thecovariate measurement error in self-reported diet.

An Augmented Likelihood Approach for the Discrete Proportional Hazards  Model Using Auxiliary and Validated Outcome Data -- with Application to the  HCHS/SOL Study