Sequential Bayes Factors for Sample Size Reduction in Preclinical Experiments with Binary Outcomes

Abstract

Preclinical studies are an integral part of pharmaceutical drug development, yet traditional methods for designing and analyzing these types of studies can be inefficient and wasteful. Even worse, when the units of study are animals, ethical concerns can arise. The 3Rs initiative was established for the ethical treatment of animals through the replacement, reduction, and refinement of animal experiments. In this article, we focus on the reduction aspect of the 3Rs initiative through the use of sequential Bayes factors. The use of sequential Bayes factors has the potential to help design more efficient experiments, that can be analyzed sequentially, in order to reduce the average number of animals needed in preclinical studies. An added bonus, sequential Bayes factors provide a means of quantifying evidence both for and against the null hypothesis, a characteristic not common to traditional preclinical trial analysis methods. Illustrations highlighting the success of sequential Bayes factors are provided for two real seven-day preclinical experiments in rats, as well as extensive simulation studies.

Keywords:

• 3Rs
• Bayesian
• Optional stopping
• Sequential designs

Supplementary

Further Operating Characteristics Assessment: This file contains the summary of operating characteristics for the sequential Bayes factor method (appendix S1) when using a sampling prior as described in Gelfand and Wang (2002). These results are compared to the results of Section 4.3. Likewise, tables containing the distributional summaries of the simulations when analyzing the real datasets of Section 5 are included as well (appendix S2). (.pdf file)

Acknowledgments

We are grateful for the helpful comments and suggestions from three anonymous reviewers and the associate editor.

Disclosure Statement

The authors report there are no competing interests to declare.

Funding

The author(s) reported there is no funding associated with the work featured in this article.