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

TL;DR

This paper introduces a Bayesian statistical model for infant lung function data from multiple-breath washout tests, improving data utilization and enabling analysis of incomplete or shorter tests, thus enhancing research and clinical assessments.

Contribution

The study develops a novel Bayesian model for infant MBW data that allows for analysis of incomplete tests and improves data efficiency over traditional methods.

Findings

Model fits data well and clarifies statistical properties of empirical summaries.

Enables estimation of lung clearance index from incomplete or shorter tests.

Facilitates use of previously discarded data, improving research and clinical practice.

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 1,197 tests from 432 individuals from a large birth cohort study. We focus on Bayesian estimation of the lung clearance index (LCI), 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 LCI to be estimated 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.