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Bayesian modelling of lung function data from multiple-breath washout tests

journal contribution
posted on 2018-05-30, 00:00 authored by Robert K Mahar, John B Carlin, Sarath Ranganathan, Anne-Louise Ponsonby, Peter VuillerminPeter Vuillermin, Damjan Vukcevic
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.

History

Journal

Statistics in medicine

Volume

37

Issue

12

Pagination

2016 - 2033

Publisher

John Wiley & Sons

Location

Chichester, Eng.

ISSN

0277-6715

eISSN

1097-0258

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2018, John Wiley & Sons, Ltd.