Abstract
In this big data era, we can readily access extensive clinical data from large observational studies or electronic health records (EHR). Data accuracy can vary according to the measurement method. For example, clinical variables extracted by automated computer algorithms or obtained from participant self-reported medical history can be error-prone. Precise data, such as those obtained from a chart review or a gold standard diagnostic test, may only be available on a subset of individuals due to cost or participant burden. We propose a method to augment a regression analysis of a gold standard time-to-event outcome with available error-prone disease diagnoses for the setting where the gold standard is observed on a subset. The proposed model addresses left-truncation and interval-censoring in time-to-event outcomes while leveraging information from the self-reported disease diagnosis in a joint likelihood for the gold standard and error-prone outcomes. The proposed model is applied to the Hispanic Community Health Study/Study of Latinos data to quantify risk factors associated with diabetes onset.