  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.
