Open AccessUse of Bayesian statistics in drug development: Advantages and challenges
Author(s) -
Sandeep Kumar Gupta
Publication year - 2012
Publication title -
international journal of applied and basic medical research/international journal of applied and basic medical research
Language(s) - English
Resource type - Journals
eISSN - 2248-9606
pISSN - 2229-516X
DOI - 10.4103/2229-516x.96789
Subject(s) - frequentist inference , bayesian probability , bayesian statistics , computer science , statistics , frequentist probability , econometrics , bayesian inference , artificial intelligence , mathematics
MAINLY, TWO STATISTICAL METHODOLOGIES ARE APPLICABLE TO THE DESIGN AND ANALYSIS OF CLINICAL TRIALS: frequentist and Bayesian. Most traditional clinical trial designs are based on frequentist statistics. In frequentist statistics prior information is utilized formally only in the design of a clinical trial but not in the analysis of the data. On the other hand, Bayesian statistics provide a formal mathematical method for combining prior information with current information at the design stage, during the conduct of the trial, and at the analysis stage. It is easier to implement adaptive trial designs using Bayesian methods than frequentist methods. The Bayesian approach can also be applied for post-marketing surveillance purposes and in meta-analysis. The basic tenets of good trial design are same for both Bayesian and frequentist trials. It has been recommended that the type of analysis to be used (Bayesian or frequentist) should be chosen beforehand. Switching to an analysis method that produces a more favorable outcome after observing the data is not recommended.