Robert Mahar, John Carlin, Sarath Ranganathan, Anne-Louise Ponsonby, Peter Vuillermin, and Damjan Vukcevic write:
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.
Yesssss! This reminds me of our work on serial dilution assays, where we squeezed information out of data that had traditionally been declared “below detection limit.”
Mahar, Carlin, et al. continue:
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.
Wow—all my favorite things! And check this out:
Keywords: lung clearance index, multiple-breath washout, variance components, Stan, incomplete data.
That’s right. Stan.
There’s only one thing that bugs me. From their Stan program:
alpha ~ normal(0, 10000);
Ummmmm . . . no.
But basically I love this paper. It makes me so happy to think that the research my colleagues and I have been doing for the past thirty years is making a difference.
Bob also points out this R package, “breathteststan: Stan-Based Fit to Gastric Emptying Curves,” from Dieter Menne et al.
There’s so much great stuff out there. And this is what Stan’s all about: enabling people to construct good models, spending less time on figuring how to fit the damn things and more time on model building, model checking, and design of data collection. Onward!