Bayesian Hierarchical Methods to Interpret the (13)C-Octanoic Acid Breath Test for Gastric Emptying

BACKGROUND: The (13)C-octanoic acid breath test is a convenient method for assessing gastric emptying (GE). Success depends on obtaining a well-characterized time profile of the excretion of label in breath, which may not be the case if GE is delayed. AIMS: To use Bayesian techniques in conjunction with hierarchical modelling as a method to increase the success of the modelling process. METHODS: Retrospective analysis of 164 individual breath tests using the WinBUGS program. The approach was tested by analysing the complete dataset simultaneously, and also as individual studies. RESULTS: The time required for Bayesian modelling was comparable with that needed for the usual methods. The results obtained were almost identical to those obtained from conventional modelling for well-behaved breath tests, but much more realistic in cases where the experimental data was poor, or when GE was delayed. CONCLUSIONS: The use of Bayesian estimation of the parameters of the (13)C-octanoic acid breath test is demonstrated. By adopting a hierarchical model, realistic values for the lag phase and half-emptying time were obtained in situations when conventional parameter estimation failed. This is particularly relevant when GE is unexpectedly delayed. We recommend that WinBUGS become the method of choice for analysing breath test data.