Joint modeling of two longitudinal biomarkers and clustered survival data with application to periodontal disease

Music City Center 

Joint modeling of longitudinal data and survival data has been extended to accommodate multilevel data structures. In dental studies, data often exhibit a multilevel hierarchy: each patient has multiple teeth, and one or more biomarkers are measured repeatedly over time for each tooth. In addition to biomarker measurements, the time to tooth loss may vary differently between patients as some people are more susceptible to tooth loss, conditional on other risk factors. In this work, we account for intra-patient and intra-tooth correlations in the longitudinal measurement of a continuous biomarkers, probing pocket depth (PPD), and a binary biomarker, mobility. We also account for the correlation in time to tooth loss between teeth within the same patient. We jointly model the two biomarker measurements and the risk of tooth loss using a Bayesian estimation approach. Our simulation study shows that the proposed joint model produced more desirable estimates with better coverage compared to the standard bivariate joint model that ignores the multilevel data structure. We applied our model to electronic periodontal data obtained from the Canadian Armed Forces (CAF). The results of both the simulation and the real data application suggest that our model accurately estimates the parameters and standard errors, whereas the standard joint model tends to underestimate the standard errors.

Multilevel data

Joint model

Longitudinal data analysis

Survival analysis

Periodontal disease