_References_berry_bayes3_ct2005

_References_berry_bayes3_ct2005 - Introduction to Bayesian...

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Unformatted text preview: Introduction to Bayesian methods III: use and interpretation of Bayesian tools in design and analysis Donald A Berry The Bayesian approach and several of its advantages in drug and medical device development are described. One advantage from the perspective of analysis is that it provides a methodology for synthesizing information. However, taking a Bayesian approach to designing clinical trials is potentially more valuable than using this approach in analyzing trial results. Bayesian methodology provides a mechanism for updating what is known as results accumulate during a trial. Such updating can be incorporated completely explicitly and prospectively. An important way in which the Bayesian approach can be used is in calculating the predictive probability distribution of future results on the basis of current results. I show how to exploit predictive distributions in adapting to results that accumulate during the course of a trial. Possible adaptations including decreasing or increasing sample size, dropping treatment arms, and modifying the randomization proportions to the various arms depending on the interim results. Consequences of taking a Bayesian approach to clinical trial design are efficiency, better treatment of patients in the trial, and greater precision regarding the primary endpoints. An example of the last of these is Bayesian modeling of the relationship between early and longer term endpoints. Such modeling also enables earlier decision making. Case studies 2 and 3 deal with trials that were shorter and smaller, respectively, because of such modeling. Clinical Trials 2005; 2 : 295300. www.SCTjournal.com Introduction Researchers at M. D. Anderson Cancer Center are increasingly applying Bayesian statistical methods in laboratory experiments and clinical trials. Over 50 current trials at M. D. Anderson have been designed from the Bayesian perspective. In addition, the pharmaceutical and medical device industries are becoming more interested in and are using the Bayesian approach. Many applications in both venues use adaptive methods, which is a primary focus of this presentation. The fully Bayesian approach There are two approaches to implementing Bayesian statistics in drug and medical device development: a fully Bayesian approach, and using Bayes as a tool to expand the frequentist envelope. Choosing the appropriate approach depends on the context in which it will be used. Is the context that of company decision making, or does it involve the design and analysis of registration studies? Pharmaceutical company decisions involve questions such as whether to move on to phase III, and if so, how many doses and which doses to include, whether to incorporate a pilot aspect of phase III, how many phase III trials should be conducted, and how many centers should be involved. These questions beg for a decision analysis of what can be called a fully Bayesian approach, using the likelihood function, the posterior distribution and a utility structure to arrive at a...
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This note was uploaded on 12/30/2010 for the course BST 252 taught by Professor Tsodikov during the Winter '06 term at UC Davis.

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_References_berry_bayes3_ct2005 - Introduction to Bayesian...

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