  Joint models for longitudinal and survival data have become a popular
framework for studying the association between repeatedly measured biomarkers
and clinical events. Nevertheless, addressing complex survival data structures,
especially handling both recurrent and competing event times within a single
model, remains a challenge. This causes important information to be
disregarded. Moreover, existing frameworks rely on a Gaussian distribution for
continuous markers, which may be unsuitable for bounded biomarkers, resulting
in biased estimates of associations. To address these limitations, we propose a
Bayesian shared-parameter joint model that simultaneously accommodates multiple
(possibly bounded) longitudinal markers, a recurrent event process, and
competing risks. We use the beta distribution to model responses bounded within
any interval (a,b) without sacrificing the interpretability of the association.
The model offers various forms of association, discontinuous risk intervals,
and both gap and calendar timescales. A simulation study shows that it
outperforms simpler joint models. We utilize the US Cystic Fibrosis Foundation
Patient Registry to study the associations between changes in lung function and
body mass index, and the risk of recurrent pulmonary exacerbations, while
accounting for the competing risks of death and lung transplantation. Our
efficient implementation allows fast fitting of the model despite its
complexity and the large sample size from this patient registry. Our
comprehensive approach provides new insights into cystic fibrosis disease
progression by quantifying the relationship between the most important clinical
markers and events more precisely than has been possible before. The model
implementation is available in the R package JMbayes2.
