Nevin Manimala Statistics

Lung Function Decline in Cystic Fibrosis: Impact of Data Availability and Modeling Strategies on Clinical Interpretations

Ann Am Thorac Soc. 2023 Mar 8. doi: 10.1513/AnnalsATS.202209-829OC. Online ahead of print.


RATIONALE: Studies estimating rate of lung function decline in cystic fibrosis (CF) have been inconsistent regarding methods used. How the methodology used impacts validity of the results and comparability between studies is unknown.

OBJECTIVES: The Cystic Fibrosis Foundation established a workgroup whose tasks were to examine the impact of differing approaches to estimating rate of decline in lung function and to provide analysis guidelines.

METHODS: We utilized a natural history cohort of 35,252 individuals with CF aged > 6 years of the Cystic Fibrosis Foundation Patient Registry (CFFPR), 2003-2016. Modeling strategies using linear and nonlinear forms of marginal and mixed-effects models, which have previously quantified rate of forced expiratory volume in 1 second (FEV1) decline (% predicted/year), were evaluated under clinically relevant scenarios of available lung function data. Scenarios varied by sample size (overall CFFPR, medium-sized cohort of 3,000 subjects, and small-sized cohort of 150), data collection/reporting frequency (encounter, quarterly, and annual), inclusion of FEV1 during pulmonary exacerbation, and follow-up length (<2 years, 2-5 years, entire duration).

RESULTS: Rate-of-FEV1-decline estimates (% predicted/year) differed between linear marginal and mixed-effects models; overall cohort estimates (95% confidence interval) were 1.26 (1.24-1.29) and 1.40 (1.38-1.42), respectively. Marginal models consistently estimated less rapid lung function decline than mixed-effects models across scenarios except for short-term follow-up (both were ~1.4). Rate-of-decline estimates from nonlinear models diverged by age 30. Among mixed-effects models, nonlinear and stochastic terms fit best except for short-term follow-up (< 2 years). Overall CFFPR analysis from a joint longitudinal-survival model implied that an increase in rate of decline of 1% predicted/year in FEV1 associated with a 1.52-fold (52%) increase in the hazard of death/lung transplantation, but results exhibited immortal cohort bias.

CONCLUSIONS: Differences were as high as 0.5% predicted/year between rate-of-decline estimates, but we found estimates were robust to lung function data availability scenarios except short-term follow-up and older age ranges. Inconsistencies among previous study results may be attributable to inherent differences in study design, inclusion criteria, or covariate adjustment. Results-based decision points reported herein will support researchers in selecting a strategy to model lung function decline most reflective of nuanced, study-specific goals.

PMID:36884219 | DOI:10.1513/AnnalsATS.202209-829OC

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