Including historical control data in simultaneous inference for pre-clinical multi-arm studies

TL;DR

This paper introduces Bayesian methods for incorporating historical control data into pre-clinical studies with binary outcomes, aiming to reduce animal use while maintaining statistical validity.

Contribution

It develops dynamic Bayesian borrowing techniques and credible intervals for risk ratios applicable to long-term carcinogenicity studies, filling a methodological gap.

Findings

Bayesian approaches reduce control group size significantly.

Methods control familywise error rate effectively.

Approaches show robustness against data drift.

Abstract

In pre- and non-clinical toxicology, the reduction of animal use is highly desireable. Although approaches for possible sample size reduction in the concurrent control group were suggested previously under the virtual control groups framework for continuous endpoints, methodology that is applicable to binary outcomes that occur in long-term carcinogenicity studies is currently missing. In order to augment animals in the current control group with historical control data, we propose approaches that rely on dynamic Bayesian borrowing and simultaneous credible intervals for risk ratios. Several operation characteristics such as familywise error rate (FWER) and power are assessed via Monte-Carlo simulations and compared to the ones of approaches that rely on pooling of historical and current observations. It turned out that under optimal conditions, Bayesian approaches based on robustified prior distributions enable a substantial reduction of the control groups sample size, while still controlling the FWER up to a satisfactory level. Furthermore, at least to some extend, these approaches were able to protect against possible drift. This hightlights the potential of Bayesian study designs to reduce animal use in toxicology through re-use of the large pool of existing control data.