INSTRUCTIONAL BAYESIAN ANALYSIS FOR RANDOMIZED TRIALS

医疗保健研究的进展在很大程度上取决于通过临床试验证明的有效性和安全性。贝叶斯方法为临床试验提供了一种有价值的替代分析模式。与标准的“频率”方法相比，贝叶斯方法提供了更多可解释的输出，使用了所有可用的证据，并适合于复杂的医疗研究所要求的更复杂的分析。不幸的是，很少有生物统计学家接受过贝叶斯方法的培训。我们建议生产用户友好的软件，以实现临床试验数据的贝叶斯分析。拟议的商业应用：临床试验代表着一个价值70亿美元的行业。该行业的统计软件本身就是一项重要的业务。我们将把iBART授权给制药公司的试验者和联邦资助的临床研究人员。我们预计iBART的教程和总体上的贝叶斯方法将特别吸引许多没有专业统计资格的试验者。

Progress in healthcare research depends heavily on demonstrations of efficacy and safety via clinical trials. Bayesian methods offer a valuable alternative mode of analysis for clinical trials. By comparison with standard "frequentist" methods, Bayesian methods provide more interpretable outputs, use all the available evidence, and lend themselves to the more complex analyses demanded by complex healthcare research. Unfortunately, few biostatisticians have received training in Bayesian methods. We propose to produce user-friendly software to enable Bayesian analysis of data from clinical trials. PROPOSED COMMERCIAL APPLICATIONS: Clinical trials represent a $7 billion industry. Statistical software for this industry is itself a significant business. We will license iBART both to drug company trialists and to federally sponsored clinical researchers. We expect iBART's tutorial and the Bayesian approach in general to appeal especially to the many trialists without specialized statistical qualifications.