A Bayesian approach to utilizing prior data in new drug development.
In this paper we propose a Bayesian method to combine safety data collected from two separate drug development programs using the same active drug substance but for different indications, formulations, or patient populations. The objective of combining the data across the programs is to better define the level of safety risk associated with the new indication or target population. There may be adverse events (AEs) observed in the new program that represent new safety signals. Our method is to explore the AEs using data from both development programs. Our approach utilizes data collected previously to assist in analyzing safety data from the new program. It is assumed that the frequency of a certain AE follows a distribution with a parameter that characterizes the safety risk level. The parameter is assumed to follow a distribution function. In the Bayesian framework, this distribution function is called a prior distribution in the absence of data and posterior distribution when updated by real data. The key concept behind our method is to use data from the previous program to construct a posterior distribution that will in turn serve as a prior distribution for the new program. The construction of this updated prior down weights data from the previous program to emphasize the new program and thus avoids simple pooling of the data across programs. Such "soft use" of previous information minimizes the potential for undue influence of previous data on the analysis. Data from the new program are used to update the prior distribution and compute the posterior distribution for the new program. Key statistics are then calculated from the posterior distribution to quantify the risk level for the new program. We have tested the proposed approach using data from a real Phase 2 study that was conducted as part of a clinical development program for a new indication of an approved drug. The results indicate that the estimated risk level was affected both by the observed event rates and the extents of exposure across the two development programs. This approach appropriately characterizes the safety profile across the two development programs and properly contextualizes new safety signals from the new program.
Shen
Larry Z
LZ
Amylin Pharmaceuticals, Inc., San Diego, California 92121, USA. lshen@amylin.com
Coffey
Todd
T
Deng
Wei
W
D001499:Bayes Theorem; D015195:Drug Design; D064420:Drug-Related Side Effects and Adverse Reactions; D008962:Models, Theoretical; D004364:Pharmaceutical Preparations / Q000737:chemistry; D018570:Risk Assessment; D012449:Safety
D004364:Pharmaceutical Preparations