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

Abstract Number:

2012 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Speed 

Participants:

Shanpeng Li (1), Gang Li (2)

Institutions:

(1) City of Hope, N/A, (2) University of California-Los Angeles, N/A

Co-Author:

Gang Li  
University of California-Los Angeles

First Author:

Shanpeng Li  
City of Hope

Presenting Author:

Shanpeng Li  
City of Hope

Abstract Text:

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.

Keywords:

Joint model|Unknown initial diagnosis|Interval-censored data|Longitudinal biomarker|Time to event outcome|

Sponsors:

Biometrics Section

Tracks:

Survival Analysis

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