Gupta Sandeep K
Department of Medical Affairs and Clinical Research, Ranbaxy Laboratories Ltd., Gurgaon, Haryana, India.
Int J Appl Basic Med Res. 2012 Jan;2(1):3-6. doi: 10.4103/2229-516X.96789.
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.
频率学派方法和贝叶斯方法。大多数传统的临床试验设计基于频率学派统计学。在频率学派统计学中，先验信息仅在临床试验设计中正式使用，而不在数据分析中使用。另一方面，贝叶斯统计学提供了一种正式的数学方法，用于在试验设计阶段、试验进行期间以及分析阶段将先验信息与当前信息相结合。使用贝叶斯方法比频率学派方法更容易实施适应性试验设计。贝叶斯方法也可用于上市后监测目的和荟萃分析。贝叶斯试验和频率学派试验的良好试验设计的基本准则是相同的。建议事先选择要使用的分析类型（贝叶斯或频率学派）。不建议在观察数据后切换到能产生更有利结果的分析方法。
