Modelling time‐varying risk factors of tooth loss: Results from joint model compared with extended Cox regression model

Abstract

AbstractAimTo illustrate the use of joint models (JMs) for longitudinal and survival data in estimating risk factors of tooth loss as a function of time‐varying endogenous periodontal biomarkers (probing pocket depth [PPD], alveolar bone loss [ABL] and mobility [MOB]).Materials and MethodsWe used data from the Veterans Affairs Dental Longitudinal Study, a longitudinal cohort study of over 30 years of follow‐up. We compared the results from the JM with those from the extended Cox regression model which assumes that the time‐varying covariates are exogenous.ResultsOur results showed that PPD is an important risk factor of tooth loss, but each model produced different estimates of the hazard. In the tooth‐level analysis, based on the JM, the hazard of tooth loss increased by 4.57 (95% confidence interval [CI]: 2.13–8.50) times for a 1‐mm increase in maximum PPD, whereas based on the extended Cox model, the hazard of tooth loss increased by 1.60 (95% CI: 1.37–1.87) times.ConclusionsJMs can incorporate time‐varying periodontal biomarkers to estimate the hazard of tooth loss. As JMs are not commonly used in oral health research, we provide a comprehensive set of R codes and an example dataset to implement the method.