BACKGROUND: Forced expiratory volume in 1 s as a percentage of predicted (%FEV(1)) is a key outcome in cystic fibrosis (CF) and other lung diseases. As people with CF survive for longer periods, new methods are required to understand the way %FEV(1) changes over time. An up to date approach for longitudinal modelling of %FEV(1) is presented and applied to a unique CF dataset to demonstrate its utility at the clinical and population level. METHODS AND FINDINGS: The Danish CF register contains 70¿448 %FEV(1) measures on 479 patients seen monthly between 1969 and 2010. The variability in the data is partitioned into three components (between patient, within patient and measurement error) using the empirical variogram. Then a linear mixed effects model is developed to explore factors influencing %FEV(1) in this population. Lung function measures are correlated for over 15 years. A baseline %FEV(1) value explains 63% of the variability in %FEV(1) at 1 year, 40% at 3 years, and about 30% at 5 years. The model output smooths out the short-term variability in %FEV(1) (SD 6.3%), aiding clinical interpretation of changes in %FEV(1). At the population level significant effects of birth cohort, pancreatic status and Pseudomonas aeruginosa infection status on %FEV(1) are shown over time. CONCLUSIONS: This approach provides a more realistic estimate of the %FEV(1) trajectory of people with chronic lung disease by acknowledging the imprecision in individual measurements and the correlation structure of repeated measurements on the same individual over time. This method has applications for clinicians in assessing prognosis and the need for treatment intensification, and for use in clinical trials.