What question did this study set out to answer?

The aim is to improve understanding of disease progression in cystic fibrosis by modeling how visit patterns affect outcomes.

Modeling Disease Progression in the Presence of an Outcome-Dependent Visiting Process with Application to Cystic Fibrosis Clinical Data

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This modeling study examines disease progression in cystic fibrosis, suggesting visiting patterns influence clinical outcomes.

The aim is to improve understanding of disease progression in cystic fibrosis by modeling how visit patterns affect outcomes.
Developed a Bayesian joint model linking mixed-effects and Weibull proportional hazards models.
Included covariates for both longitudinal markers and event processes.
Evaluated performance of different random effect structures.
Lower lung function correlates with more frequent clinical visits.
Incorporating a time-dependent Gaussian process improves model fit significantly.
Traditional random intercepts models displayed maximum bias with outcome-dependent visits.

Su et al. (2026) studied this question.

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