Bayesian modelling of lung function data from multiple‐breath washout tests. (26th March 2018)

• Record Type: Journal Article
• Authors: Mahar, Robert K.; Carlin, John B.; Ranganathan, Sarath; Ponsonby, Anne‐Louise; Vuillermin, Peter; Vukcevic, Damjan
• Is Part Of: Statistics in medicine. Volume 37:Number 12(2018)
• Page Start: 2016
• Page End: 2033
• Publication Date: 2018-03-26
• Subjects: incomplete data -- lung clearance index -- multiple‐breath washout -- Stan -- variance components; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.7650
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : Paediatric respiratory researchers have widely adopted the multiple‐breath washout (MBW) test because it allows assessment of lung function in unsedated infants and is well suited to longitudinal studies of lung development and disease. However, a substantial proportion of MBW tests in infants fail current acceptability criteria. We hypothesised that a model‐based approach to analysing the data, in place of traditional simple empirical summaries, would enable more efficient use of these tests. We therefore developed a novel statistical model for infant MBW data and applied it to 1197 tests from 432 individuals from a large birth cohort study. We focus on Bayesian estimation of the lung clearance index, the most commonly used summary of lung function from MBW tests. Our results show that the model provides an excellent fit to the data and shed further light on statistical properties of the standard empirical approach. Furthermore, the modelling approach enables the lung clearance index to be estimated by using tests with different degrees of completeness, something not possible with the standard approach. Our model therefore allows previously unused data to be used rather than discarded, as well as routine use of shorter tests without significant loss of precision. Beyond our specific application, our work illustrates a number of important aspects of Bayesian modelling in practice, such as the importance of hierarchical specifications to account for repeated measurements and the value of model checking via posterior predictive distributions.