A longitudinal, observational study with many repeated measures demonstrated improved precision of individual survival curves using bayesian joint modeling of disability and survival

Background/Study Context: It has not been previously demonstrated whether Bayesian joint modeling (BJM) of disability and survival can, under certain conditions, improve precision of individual survival curves.Methods: A longitudinal, observational study wherein 754 initially nondisabled community-dwelling adults in greater New Haven, Connecticut, were observed on a monthly basis for over 10 years.Results: In this study, BJM exploited many monthly observations to demonstrate, relative to a separate survival model with adjustment, improved precision of individual survival curves, permitting detection of significant differences between survival curves of two similar individuals. The gain in precision was lost when using only those observations from intervals of 6, 9, or 12 months.Conclusion: When there are many repeated measures, BJM of longitudinal functional disability and interval-censored survival can potentially increase the precision of individual survival curves relative to those from a separate survival model. This may facilitate the identification of significant differences between individual survival curves, a useful result usually precluded by the large variability inherent to individual-level estimates from stand-alone survival models.