Abstract
Background: With the availability of effective modulators for people living with Cystic Fibrosis (CF) there is a need to re-design prospective studies. The Lung Clearance Index (LCI) is a sensitive measure of lung function and is increasingly used in clinical trials. Crude sample size estimates are based on the expected treatment effect and variance of response. However, there is often discordance between the assumptions made to calculate sample size and the LCI treatment effects observed in trials.
Aim: We aimed to investigate sample size considerations for using the LCI in clinical trials in the era of modulators.
Methods: Monte Carlo simulations were used to estimate the required sample size to detect a range of treatment effects. Unadjusted/adjusted and relative/absolute analytical approaches were modelled based on LCI treatment effects observed in published data.
Results: The distribution of LCI treatment effects is often skewed (a few individuals with large treatment effects). Sample size estimates under an assumed normal distribution of treatment effects are nearly two-times higher than observed in published studies (Figure 1). Incorporating baseline measures into analysis reduces the required sample size by up to 50%.
Conclusions: Sample size estimates for prospective trials should consider the heterogeneity and skew of treatment effects, and study designs should include baseline measurements to improve efficiency.
Footnotes
Cite this article as Eur Respir J 2022; 60: Suppl. 66, 78.
This article was presented at the 2022 ERS International Congress, in session “-”.
This is an ERS International Congress abstract. No full-text version is available. Further material to accompany this abstract may be available at www.ers-education.org (ERS member access only).
- Copyright ©the authors 2022
