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

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

Linear mixed-effect models for saliva hormones.

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Number (%) of waves with available hormone measurements for each hormone for females and males.

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Descriptive statistics for age at saliva collection, time of saliva collection, saliva flow rate, and hormone measurements at waves 1 to 4 for females (n = 667) and males (n = 572).

Descriptive statistics for age at saliva collection, time of saliva collection, saliva flow rate, and hormone measurements at waves 1 to 4 for females (n = 667) and males (n = 572).</p

Number (%) of females and males in cross-classifications of individuals by hormone level at age 9 years and hormone progression rate over time.

L1, L2, and L3 respectively indicate low, normal, and high predicted hormone levels compared to population; R1 and R2 indicate slow/normal and fast predicted progression rate over time compared to population.</p

Estimates of standard deviation and correlation parameters in the mixed-effects models for adrenal hormone measured at age 9 years old.

Estimates of standard deviation and correlation parameters in the mixed-effects models for adrenal hormone measured at age 9 years old.</p

Percentage of individuals who changed class after excluding hormone measurements collected after 11am (n = 51).

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Estimates of fixed-effect coefficients, with 95% confidence intervals, in the mixed-effects models.

Estimates of fixed-effect coefficients, with 95% confidence intervals, in the mixed-effects models.</p

Histogram of log detectable values for DHEA-S overlaid with a normal distribution curve.

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Percentage of individuals who changed class after excluding hormone measurements not collected at school setting (n = 149).

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