PMID- 18327718
OWN - NLM
STAT- MEDLINE
DCOM- 20080331
LR  - 20131121
IS  - 1520-5711 (Electronic)
IS  - 1054-3406 (Linking)
VI  - 18
IP  - 2
DP  - 2008
TI  - A Bayesian approach to utilizing prior data in new drug development.
PG  - 227-43
LID - 10.1080/10543400701697133 [doi]
AB  - 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.
FAU - Shen, Larry Z
AU  - Shen LZ
AD  - Amylin Pharmaceuticals, Inc., San Diego, California 92121, USA. lshen@amylin.com
FAU - Coffey, Todd
AU  - Coffey T
FAU - Deng, Wei
AU  - Deng W
LA  - eng
PT  - Journal Article
PL  - England
TA  - J Biopharm Stat
JT  - Journal of biopharmaceutical statistics
JID - 9200436
RN  - 0 (Pharmaceutical Preparations)
SB  - IM
MH  - Bayes Theorem
MH  - *Drug Design
MH  - Drug-Related Side Effects and Adverse Reactions
MH  - *Models, Theoretical
MH  - *Pharmaceutical Preparations/chemistry
MH  - Risk Assessment
MH  - Safety
EDAT- 2008/03/11 09:00
MHDA- 2008/04/01 09:00
CRDT- 2008/03/11 09:00
PHST- 2008/03/11 09:00 [pubmed]
PHST- 2008/04/01 09:00 [medline]
PHST- 2008/03/11 09:00 [entrez]
AID - 791343990 [pii]
AID - 10.1080/10543400701697133 [doi]
PST - ppublish
SO  - J Biopharm Stat. 2008;18(2):227-43. doi: 10.1080/10543400701697133.