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Estimating the minimum sample size for interventional and observational studies using the lung clearance index as an endpoint

  • Cole Bowerman
    Affiliations
    Michael G. DeGroote School of Medicine, McMaster University, 1280 Main St W, Michael G. DeGroote Centre for Learning and Discovery, Rm 3104, Hamilton, ON, L8S 4K1

    Department of Community Health and Epidemiology, Dalhousie University, 5790 University Avenue, Halifax, NS, B3H 1V7
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  • Felix Ratjen
    Affiliations
    Division of Respiratory Medicine, Department of Pediatrics, Hospital for Sick Children, Translational Medicine, Research Institute, University of Toronto, 555 University Avenue, Room 4534, 4th Floor, Hill Wing, Toronto, ON, M5G 1 × 8
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  • Sanja Stanojevic
    Correspondence
    Corresponding author at: Department of Community Health and Epidemiology, Dalhousie University, 5790 University Avenue, Halifax, NS, B3H 1V7.
    Affiliations
    Department of Community Health and Epidemiology, Dalhousie University, 5790 University Avenue, Halifax, NS, B3H 1V7
    Search for articles by this author
Published:November 16, 2022DOI:https://doi.org/10.1016/j.jcf.2022.11.003

      Highlights

      • In randomized controlled trials that used the Lung Clearance Index as an outcome, there is a discordance between planned sample size required to observe statistically significant treatment effects and the magnitude of the treatment effects that are observed.
      • Large improvements in Lung Clearance Index for a few individuals can influence the interpretation of the effect size and may bias prospective sample size calculations.
      • Lung clearance index requires as little as 1/3 of the total sample size to detect equivalent changes with spirometry endpoints such as forced expiratory volume in 1 second (FEV1).

      Abstract

      Background

      With the increasing availability of highly effective modulators for people living with cystic fibrosis (CF), there is a need to re-design research studies to reflect the changing epidemiology of the CF population. The lung clearance index (LCI), a sensitive physiological measure of lung function, may be ideally suited as an endpoint in the era of CF modulator therapies. In this study we describe study design considerations for implementing LCI into interventional and observational research.

      Methods

      Simulations were used to estimate the required sample size to detect a range of treatment effects for interventional studies (including cross-over trials) and to track lung disease progression in observational studies.

      Results

      Using published treatment effects to inform the design of prospective studies can lead to inefficient study designs. Large improvements in LCI for a few individuals can skew results and can influence interpretations of treatment effects. Adjusting for baseline LCI can help to improve the efficiency of a study. Compared to the forced expiratory volume in 1 second (FEV1), analysis using LCI as an endpoint requires as little as one third of the total sample size.

      Conclusions

      Planning of prospective studies that include LCI as an endpoint need to consider baseline LCI and disease severity of the study population; whereas interpretation of results needs to consider whether a few individuals skew the overall treatment effect.

      Keywords

      Abbreviations:

      LCI (Lung Clearance Index)
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