A fully objective Bayesian approach for the Behrens-Fisher problem using historical studies

TL;DR

This paper introduces a fully objective Bayesian sequential method for the Behrens-Fisher problem that effectively incorporates historical data to improve inference in small sample in vivo studies.

Contribution

It develops a novel Bayesian approach with calibrated priors and model comparison, integrating multiple historical datasets for small sample inference.

Findings

The method controls type I and II error rates effectively.

It outperforms traditional methods in small sample scenarios.

Applicable to real in vivo research data.

Abstract

For in vivo research experiments with small sample sizes and available historical data, we propose a sequential Bayesian method for the Behrens-Fisher problem. We consider it as a model choice question with two models in competition: one for which the two expectations are equal and one for which they are different. The choice between the two models is performed through a Bayesian analysis, based on a robust choice of combined objective and subjective priors, set on the parameters space and on the models space. Three steps are necessary to evaluate the posterior probability of each model using two historical datasets similar to the one of interest. Starting from the Jeffreys prior, a posterior using a first historical dataset is deduced and allows to calibrate the Normal-Gamma informative priors for the second historical dataset analysis, in addition to a uniform prior on the model space. From this second step, a new posterior on the parameter space and the models space can be used as the objective informative prior for the last Bayesian analysis. Bayesian and frequentist methods have been compared on simulated and real data. In accordance with FDA recommendations, control of type I and type II error rates has been evaluated. The proposed method controls them even if the historical experiments are not completely similar to the one of interest.