A joint model of the individual mean and within-subject variability of a longitudinal outcome with a competing risks time-to-event outcome

Motivated by a growing body of research emphasizing the importance of modeling within-subject (WS) variability in longitudinal biomarkers and its association with health outcomes, this paper proposes a semiparametric joint model for both the mean and WS variability of a longitudinal biomarker, jointly with competing-risk time-to-event outcomes.

We derive an expectation-maximization algorithm for parameter estimation and a profile-likelihood method for standard error estimation and inference, which allows time-dependent covariates and general forms of the latent association structure.

Furthermore, we optimize the implementation of our joint model when the survival submodel includes only time-independent baseline covariates and shared random effects, allowing it to scale effectively to biobank-scale data involving tens of thousands of subjects.

Our method demonstrates satisfactory performance in simulations, whereas classical joint models that assume homogeneous WS variability may suffer from substantial estimation bias, invalid inference, and inferior prediction when confronted with heterogeneous WS variability.

We illustrate the utility of our method using the Multi-Ethnic Study of Atherosclerosis (MESA) cohort.

Our analysis demonstrates that associations between WS blood pressure variability and cardiovascular outcomes, previously observed in clinical trials involving relatively homogeneous populations, extend to a more ethnically diverse and generally healthier cohort, and that explicitly modeling heterogeneous WS variability substantially enhances risk discrimination.

A user-friendly R package, \textbf{JMH}, has been developed for the proposed shared random effects model with efficient implementation and is publicly available on the Comprehensive R Archive Network this https URL.