Breaking the Bayesian Ice with Preclinical Discovery Biologists by Predicting Inadequate Animal Enrolment

Abstract

An initial proposal was made to start 30 monkeys in the run-in period of a preclinical translational research study, to have 24 or more animals qualify for randomization in the subsequent treatment period. Based upon data from previous studies, Bayesian posterior prediction indicated that successful enrolment was highly unlikely. At least 67 animals were required to achieve an acceptable posterior predictive probability of success. Importantly, we leveraged these feasibility analyses to introduce our preclinical scientist collaborators to a Bayesian strategy for probability-based decision making. We provided them with a generous helping of graphics to effectively and efficiently illustrate Bayesian concepts and methods. We present our 4P strategy for collaboration with preclinical scientists: patience, persistence, positioning, and privilege. We discuss the alignment of the Bayesian and 4P strategies with goals common to pharmaceutical researchers: scientific innovation; stochastic intelligence and statistical literacy of team members; team collaboration and collegial partnerships; ethical acuity; and fiscal stewardship. Our article is as much about successfully reaching out to preclinical scientists, and introducing them to the Bayesian strategy, as it is about that strategy successfully addressing the animal enrolment question. This article is written at a statistical level accessible to both preclinical scientists and statisticians.