Estimating Cystic Fibrosis Lung Function Decline: An Empirical Study

Abstract

Various statistical approaches have been used to estimate lung function decline over age among individuals with cystic fibrosis (CF). We investigated the impact of different statistical models and real-world scenarios on results and conclusions regarding rate-of-decline.

The sample included individuals with CF aged >6 years in the CF Foundation Patient Registry (2003-2016). Marginal, mixed-effects and joint longitudinal-survival models for estimating rate-of-FEV1 decline were implemented under scenarios that mimic attributes of different available types of data varied by sample size, duration and frequency of follow-up. We assessed the impact of linear and nonlinear trajectories and different correlation structures.

Overall, all models indicated an approximately linear rate of decline until age 30 (-1.4% predicted/year) with minimal difference between marginal and mixed models; nonlinear models fit better than linear models. Joint models suggested more severe FEV1 decline over time. Mixed model estimates had more variability between scenarios than marginal models. Duration of follow-up was the only scenario that impacted estimates. Rate of FEV1 decline was associated with mortality across scenarios (estimated hazard ratio, HR and 95% CI for death/lung transplant, HR: 0.67, 95% CI: 0.66-0.68).

Choosing an appropriate modeling strategy depends on the research question and data structure. Longer follow-up is best characterized with nonlinear terms. Association of rate-of-decline and survivorship should be assessed even in younger cohorts.