A Bayesian Hierarchical 13 COBT to Correct Estimates Associated with a Delayed Gastric Emptying

The use of a Bayesian Hierarchical Model (BHM) to interpret breath measurements obtained during a C Octanoic Breath Test (COBT) is demonstrated. The statistical analysis was implemented using WinBUGS, a commercially available computer package for Bayesian inference. A hierarchical setting was adopted where poorly defined parameters associated with a delayed Gastric Emptying (GE) were able to "borrow" strength from global distributions. This is proved to be a sufficient tool to correct model's failures and data inconsistencies apparent in conventional analyses employing a Non-linear least squares technique (NLS). Direct comparison of two parameters describing gastric emptying ( lag t -lag phase, 2 / 1 t -half emptying time) revealed a strong correlation between the two methods. Despite our large dataset ( 164 = n ), Bayesian modeling was fast and provided a successful fitting for all subjects. On the contrary, NLS failed to return acceptable estimates in cases where GE was delayed. Keywords—Bayesian hierarchical analysis, COBT, Gastric emptying, WinBUGS.