Bayesian models offer a flexible way of incorporating historical controls in the analysis of trial data (whether single arm and randomized), and with increased access to the necessary computational power, they are transforming today’s clinical research. In a recently published article, Cytel’s scientific community members review the main Bayesian methods used in clinical trial design. Continue reading this blog for a brief overview.
Bayesian statistics have a much older history than Frequentist methods, having first been developed by the English minister Thomas Bayes in the 1700s; but their use within the clinical development setting has taken time to establish. Even today, the number of simulation and modeling tools necessary to perform Bayesian computations requires statisticians to be well-resourced technologically.
Bayesian approaches provide a posterior probability distribution for some parameter (e.g., treatment effect), derived from the observed data and a prior probability distribution for the parameter. The posterior distribution is then used as the basis for statistical inference. In contrast, Frequentist methods such as significance tests and confidence intervals, can be interpreted in terms of the frequency of certain outcomes occurring in hypothetical repeated realizations of the same experimental situation.
Frequentist designs can often require higher sample sizes than Bayesian methods, and are considered by some to be less flexible and less intuitive. Others point out that the decision to use Frequentist or Bayesian designs is a matter of context.
The Cytel article offers insights on:
Early-phase Bayesian designs
Bayesian methods in early-phase Frequentist designs
Late-phase Bayesian designs
The authors have highlighted the need for more Bayesian methods employed in pharmaceutical development, both for design and analysis. The array of Bayesian methods is vast, and with computing power now more easily accessible to a wide range of statisticians, these methods are perpetuating the trial design space. Click the button to learn more.
About Pantelis Vlachos
Pantelis is Principal/Strategic Consultant for Cytel, Inc. based in Geneva. He joined the company in January 2013. Before that, he was a Principal Biostatistician at Merck Serono as well as a Professor of Statistics at Carnegie Mellon University for 12 years. His research interests lie in the area of adaptive designs, mainly from a Bayesian perspective, as well as hierarchical model testing and checking although his secret passion is Text Mining. He has served as Managing Editor of the journal “Bayesian Analysis” as well as editorial boards of several other journals and online statistical data and software archives.