Shao Jun, Chow Shein-Chung
Department of Statistics, University of Wisconsin, Madison, WI 53706, USA.
Stat Med. 2002 Jun 30;21(12):1727-42. doi: 10.1002/sim.1177.
For marketing approval of a new drug product, the United States Food and Drug Administration (FDA) requires that substantial evidence of the effectiveness of the drug product be provided through the conduct of at least two adequate and well-controlled clinical trials. The purpose of conducting the second clinical trial is to study whether the clinical result from the first trial is reproducible in the second trial with the same study protocol. Under certain circumstance, the FDA Modernization Act of 1997 includes a provision to allow data from one adequate and well-controlled clinical trial investigation and confirmatory evidence to establish effectiveness for risk/benefit assessment of drug and biological candidates for approval. In this paper, we introduce the concept of reproducibility probability for a given clinical trial, which is useful in providing important information for regulatory agencies in deciding whether a single clinical trial is sufficient and for pharmaceutical companies in adjusting the sample size in a future clinical trial. Three approaches, the estimated power approach, the method of confidence bounds and the Bayesian approach, are studied in evaluating reproducibility probabilities under several study designs commonly used in clinical trials.
对于一种新药产品的上市批准，美国食品药品监督管理局（FDA）要求通过开展至少两项充分且对照良好的临床试验来提供该药品有效性的充分证据。开展第二项临床试验的目的是研究在相同研究方案下，第一项试验的临床结果在第二项试验中是否可重现。在某些情况下，1997年的《FDA现代化法案》包含一项条款，允许来自一项充分且对照良好的临床试验调查的数据以及确证性证据用于确立药物和生物制品候选物批准的风险/效益评估的有效性。在本文中，我们引入了给定临床试验的可重现概率的概念，这对于监管机构决定单一临床试验是否足够以及制药公司在未来临床试验中调整样本量时提供重要信息很有用。在评估临床试验中常用的几种研究设计下的可重现概率时，研究了三种方法，即估计效能法、置信区间法和贝叶斯法。
