Probiotic responder identification in cross-over trials for constipation using a Bayesian statistical model considering lags between intake and effect periods.

Recent advances in microbiome research have led to the further development of microbial interventions, such as probiotics and prebiotics, which are potential treatments for constipation. However, the effects of probiotics vary from person to person; therefore, the effectiveness of probiotics needs to be verified for each individual. Individuals showing significant effects of the target probiotic are called responders. A statistical model for the evaluation of responders was proposed in a previous study. However, the previous model does not consider the lag between intake and effect periods of the probiotic. It is expected that the lag exists when probiotics are administered and when they are effective. In this study, we propose a Bayesian statistical model to estimate the probability that a subject is a responder, by considering the lag between intake and effect periods. In synthetic dataset experiments, the proposed model was found to outperform the base model, which did not factor in the lag. Further, we found that the proposed model could distinguish responders showing large uncertainty in terms of the lag between intake and effect periods.