Sample size and the probability of a successful trial.
This paper describes the distinction between the concept of statistical power and the probability of getting a successful trial. While one can choose a very high statistical power to detect a certain treatment effect, the high statistical power does not necessarily translate to a high success probability if the treatment effect to detect is based on the perceived ability of the drug candidate. The crucial factor hinges on our knowledge of the drug's ability to deliver the effect used to power the study. The paper discusses a framework to calculate the 'average success probability' and demonstrates how uncertainty about the treatment effect could affect the average success probability for a confirmatory trial. It complements an earlier work by O'Hagan et al. (Pharmaceutical Statistics 2005; 4:187-201) published in this journal. Computer codes to calculate the average success probability are included.
Chuang-Stein
Christy
C
Global Statistics, Global Research and Development, Pfizer Inc., Ann Arbor, MI, USA. christy.j.chuang-stein@pfizer.com
D000465:Algorithms; D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D011336:Probability; D016032:Randomized Controlled Trials as Topic; D015203:Reproducibility of Results; D012107:Research Design; D018401:Sample Size; D012984:Software; D013810:Therapeutic Equivalency; D013997:Time Factors