Bayesian clinical trials

被引:0
作者
Donald A. Berry
机构
[1] The University of Texas M. D. Anderson Cancer Center,Department of Biostatistics and Applied Mathematics
来源
Nature Reviews Drug Discovery | 2006年 / 5卷
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摘要
The greatest virtue of traditional frequentist statistical approaches may be the extreme rigour and narrowness of focus, but a side effect of this virtue is inflexibility, which in turn limits innovation in the design and analysis of clinical trials.Because of this, clinical trials tend to be overly large, which increases the cost of developing new therapeutic approaches, and some patients are unnecessarily exposed to inferior experimental therapies. Bayesian approaches have the potential to address these issues.The defining characteristic of any statistical approach is how it deals with uncertainty. Unlike the frequentist approach, in the Bayesian approach, all uncertainty is measured by probability.The continuous learning that is possible in the Bayesian approach enables investigators to modify trials in midcourse. Modifications include stopping the trial, adaptively assigning patients to therapies that are performing better or that will give more information about the scientific question of interest, and adding and dropping treatment arms.In analysing the results of a clinical trial, the Bayesian attitude is to bring all available information to bear on the scientific question being addressed. Outside of a Bayesian perspective, such potentially important information is usually overlooked because the methodology used cannot incorporate it.The Bayesian approach has several advantages in drug development, such as the process of updating knowledge gradually rather than restricting to large discrete steps measured in trials or phases. Another advantage is that it is specifically tied to decision making, within a particular trial, within a drug development programme, and within establishing a company's portfolio of drugs under development.Therapeutic areas in which the clinical endpoints are observed early stand to benefit most from an increase in the use of Bayesian approaches. Similiarly, diseases such as cancer in which there is a burgeoning number of biomarkers available for assessing the disease's progress will also benefit.
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页码:27 / 36
页数:9
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