Validation and use of a parametric model for projecting cystic fibrosis survivorship beyond observed data: a birth cohort analysis

Background The current lifetable approach to survival estimation is favoured by CF registries. Recognising the limitation of this approach, we examined the utility of a parametric survival model to project birth cohort survival estimates beyond the follow-up period, where short duration of follow-up meant median survival estimates were indeterminable. Methods Parametric models were fitted to observed survivorship data from the US CF Foundation (CFF) Patient Registry 1980–1994 birth cohort. Model-predicted median survival was estimated. The best fitting model was applied to a Cystic Fibrosis Registry of Ireland dataset to allow an evaluation of the model's ability to estimate predicted median survival. This involved a comparison of birth cohort lifetable predicted and observed (Kaplan–Meier) median survival estimates. Results A Weibull model with main effects of gender and birth cohort was developed using a US CFF dataset (n=13 115) for which median survival was not directly estimable. Birth cohort lifetable predicted median survival for male and female patients born between 1985 and 1994 and surviving their first birthday was 50.9 and 42.4 years respectively. To evaluate the accuracy of a Weibull model in predicting median survival, a model was developed for the 1980–1984 Cystic Fibrosis Registry of Ireland birth cohort (n=243), which had an observed (Kaplan–Meier) median survival of 27.7 years. Model-predicted median survival estimates were calculated using data censored at different follow-up periods. The estimates converged to the true value as length of follow-up increased. Conclusions Accurate prognostic information that is clinically critical for care of patients affected by rare, life-limiting disorders can be provided by parametric survival models. Problems associated with short duration of follow-up for recent birth cohorts can be overcome using this approach, providing better opportunities to monitor survival and plan services locally.