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To 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]). We 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. Our 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. JMs 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. © 2023 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.


Aya A Mitani, Xinyang Feng, Elizabeth K Kaye. Modelling time-varying risk factors of tooth loss: Results from joint model compared with extended Cox regression model. Journal of clinical periodontology. 2024 Feb;51(2):110-117

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PMID: 37846605

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