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

Robert K Mahar1,2, John B Carlin1,2,3, Sarath Ranganathan2,4,5

  • 1Data Science, Murdoch Children's Research Institute, Parkville, Victoria, Australia.

Statistics in Medicine
|March 28, 2018
PubMed
Summary
This summary is machine-generated.

A new statistical model improves infant lung function assessment using multiple-breath washout (MBW) tests. This approach allows more data to be used, enhancing the analysis of lung development and disease in children.

Keywords:
Stanincomplete datalung clearance indexmultiple-breath washoutvariance components

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Area of Science:

  • Pediatric Pulmonology
  • Biostatistics
  • Computational Biology

Background:

  • Multiple-breath washout (MBW) tests are crucial for assessing infant lung function, particularly in longitudinal studies.
  • A significant number of infant MBW tests do not meet current acceptability criteria, leading to data loss.
  • Traditional analysis methods for MBW data may not fully utilize available information.

Purpose of the Study:

  • To develop and validate a novel statistical model for analyzing infant MBW data.
  • To improve the efficiency and utility of MBW tests in pediatric respiratory research.
  • To enable the estimation of the lung clearance index (LCI) from incomplete or shorter MBW tests.

Main Methods:

  • Development of a novel Bayesian statistical model tailored for infant MBW data.
  • Application of the model to a large dataset of 1197 MBW tests from 432 infants in a birth cohort.
  • Focus on Bayesian estimation of the lung clearance index (LCI).
  • Model checking using posterior predictive distributions.

Main Results:

  • The developed Bayesian model demonstrated an excellent fit to the infant MBW data.
  • The model provides insights into the statistical properties of standard empirical analysis methods.
  • The novel approach allows LCI estimation from tests of varying completeness, unlike standard methods.
  • Previously unusable data from incomplete or shorter tests can now be utilized without significant loss of precision.

Conclusions:

  • A model-based approach offers a more efficient and comprehensive analysis of infant MBW data compared to traditional methods.
  • The Bayesian model enhances data utilization by accommodating tests with different degrees of completeness.
  • This methodology supports the routine use of shorter MBW tests, improving data acquisition in pediatric studies.
  • The study highlights the practical benefits of Bayesian hierarchical modeling in analyzing repeated measures data in developmental research.