Bayesian joint modeling of longitudinal patient-reported outcomes and survival: an application to chronic obstructive pulmonary disease
Year: 2026
Type: preprint
Abstract: Questionnaire-based patient-reported outcomes (PROs) are discrete, bounded and overdispersed, yet joint models relating them to survival may ignore these features or estimate both processes sequentially. We propose a Bayesian joint model combining a beta-binomial mixed-effects submodel with a Weibull proportional hazards submodel, linked through the subject-specific response probability. Simulations show that simultaneous estimation reduces bias in the longitudinal slope and yields practically unbiased association estimates, unlike two-stage estimation. In a cohort of 543 patients with chronic obstructive pulmonary disease, the model identified associations for all eight SF-36 dimensions and for two of three SGRQ dimensions, including several associations not detected by the two-stage approach, and provided dynamic survival predictions.
Source: arXiv (Cornell University)
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Awards: G1250084973, G1272900390, G1280739393, G1480809515, G1612158077, G1633674249, G1747643187, G2089874362, G223601010, G2309174360, G2457414450, G2785478817, G290395390, G3077296717, G3518538256, G4033406476, G4289627271, G430777504, G5149722201, G542055026, G5421974903, G5635532810, G5805502524, G5852410385, G6294024929, G6481147803, G6731524417, G7436716198, G745419941, G7805626310, G7951220496, G8497050386, G883993239, G8939903727
OpenAlex ID: W7214330437