Jerome Cornfield and the methodology of clinical trials

Bayesian clinical trials in action

Although the frequentist paradigm has been the predominant approach to clinical trial design since the 1940s, it has several notable limitations. Advancements in computational algorithms and computer hardware have greatly enhanced the alternative Bayesian paradigm. Compared with its frequentist counterpart, the Bayesian framework has several unique advantages, and its incorporation into clinical trial design is occurring more frequently. Using an extensive literature review to assess how Bayesian methods are used in clinical trials, we find them most commonly used for dose finding, efficacy monitoring, toxicity monitoring, diagnosis/decision making, and studying pharmacokinetics/pharmacodynamics. The additional infrastructure required for implementing Bayesian methods in clinical trials may include specialized software programs to run the study design, simulation and analysis, and web-based applications, all of which are particularly useful for timely data entry and analysis. Trial success requires not only the development of proper tools but also timely and accurate execution of data entry, quality control, adaptive randomization, and Bayesian computation. The relative merit of the Bayesian and frequentist approaches continues to be the subject of debate in statistics. However, more evidence can be found showing the convergence of the two camps, at least at the practical level. Ultimately, better clinical trial methods lead to more efficient designs, lower sample sizes, more accurate conclusions, and better outcomes for patients enrolled in the trials. Bayesian methods offer attractive alternatives for better trials. More Bayesian trials should be designed and conducted to refine the approach and demonstrate their real benefit in action.

An introduction to the Bayesian analysis of clinical trials

Although most clinical trials comparing therapies are analyzed using classical hypothesis testing and P values, such methods do not yield the information most useful to the clinician, that is, the probability that one treatment is more efficacious than another. Bayesian inference can yield this probability but only if we quantify our prior beliefs about the possible efficacies of the treatments studied. This article gives a brief introduction to Bayesian methods and contrasts them with classical hypothesis testing. It shows that the quantification of prior beliefs is a common and necessary part of the interpretation of clinical information, whether from a laboratory test or published clinical trial. Advantages of Bayesian analysis over classical analysis of clinical trials include the ability to incorporate prior information regarding treatment efficacies into the analysis; the ability to make multiple unscheduled inspections of accumulating data without increasing the error rate of the study; and the ability to calculate the probability that one treatment is more effective than another. Because it is likely that Bayesian methods will be used more often in the analysis of future clinical trials, investigators and readers should be aware of the two schools of statistical thought and the strengths and weaknesses of each.

Controlled clinical trials in emergency medicine

In this first article of a series on controlled clinical trials in emergency medicine, an overview of the structure of such studies and the relevant issues related to study design, data generation, statistical analysis, and reporting are presented. The importance of precise patient selection, randomization and stratification, blinding, and equivalent therapeutic intervention in study design has been discussed. In addition, precise outcome and measurement criteria, assessor qualifications, deficiencies in data, appropriate sample-size selection, and reporting are also presented. Although an idealized framework for the conduct and analysis of a controlled clinical trial is provided, it should be appreciated that design and analytical compromises may at times be difficult to avoid in clinical research. Future articles in this series will discuss randomization in clinical trials, alternatives to randomization, beta error and sample-size determination, the statistical and analytical issues related to imperfections in the execution of a trial, and issues related to the reporting of clinical trials.