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.
[1]
L B Sheiner,et al.
Learning versus confirming in clinical drug development
,
1997,
Clinical pharmacology and therapeutics.
[2]
G. Box.
Use and Abuse of Regression
,
1966
.
[3]
Scott Marshall,et al.
A Bayesian Design and Analysis for Dose-Response Using Informative Prior Information
,
2006,
Journal of biopharmaceutical statistics.
[4]
W Ewy,et al.
Model‐based Drug Development
,
2007,
Clinical pharmacology and therapeutics.
[5]
Modeling and Simulation to Support Dose Selection and Clinical Development of SC‐75416, a Selective COX‐2 Inhibitor for the Treatment of Acute and Chronic Pain
,
2008,
Clinical pharmacology and therapeutics.
[6]
Andrea Marshall,et al.
Importance of protocols for simulation studies in clinical drug development
,
2011,
Statistical methods in medical research.