Decision-Making in Drug Development: Application of a Model Based Framework for Assessing Trial Performance

Abstract

This chapter proposes a general framework for the formal integration of model-based predictions and their uncertainty in the planning of prospective trials and in quantitative decision-making. Standard operating characteristics such as statistical power, which are conditional on a chosen effect size, quantify the performance of the design. Optimising trials based solely on power does not fully address the needs of drug development teams interested in understanding the performance of the compound as well as the performance of the proposed study design. Many Phase 3 trials fail due to lack of significant efficacy despite being adequately powered. Power does not take into consideration the likelihood of achieving the assumed treatment effect. Metrics such as probability of a correct decision, probability of a Go decision, and probability of reaching a target value are proposed to evaluate the performance of the compound and trial. A conceptual clinical trial simulation (CTS) approach is outlined for calculating these trial performance metrics and to evaluate the ‘false positive’ and ‘false negative’ error rates for the proposed metrics. An example is presented to illustrate the CTS procedure and show how different choices of trial design, analytic technique and trial metric influence the probability of making correct decisions.