A Joint Model of Longitudinal and Interval-censored Post-Diagnosis Time to Event Data

Oregon Convention Center 

This work presents a novel joint model of a longitudinal biomarker and interval-censored post-diagnosis time to event outcome in the presence of interval-censored covariates due to the unknown initial event diagnosis time. By treating interval-censored initial event as missing data, we develop an expectation-maximization algorithm for semi-parametric maximum likelihood estimation, where the distribution of interval-censored initial event is modeled. A simulation framework is constructed to demonstrate the performance of our novel approach across a variety of scenarios. We applied this joint model to large-scale UK-Biobank data and found that (1) the age at diagnosis of diabetes was positively associated with the systolic blood pressure; (2) a smoker had a significantly increased risk of cardiovascular disease (CVD) event, but midpoint analysis detected no significance in these two covariates. Lastly, using Brier Score as a calibration measure for dynamic prediction, our proposed model yielded a higher accuracy of CVD event prediction than midpoint analysis. In summary, both our simulation and application results showed that our proposed model outperformed midpoint analysis.

Joint model

Unknown initial diagnosis

Interval-censored data

Longitudinal biomarker

Time to event outcome 

Biometrics Section