TY - JOUR
T1 - A two-stage Bayesian design with sample size reestimation and subgroup analysis for phase II binary response trials
AU - Zhong, Wei
AU - Koopmeiners, Joseph S.
AU - Carlin, Bradley P.
PY - 2013/11
Y1 - 2013/11
N2 - Frequentist sample size determination for binary outcome data in a two-arm clinical trial requires initial guesses of the event probabilities for the two treatments. Misspecification of these event rates may lead to a poor estimate of the necessary sample size. In contrast, the Bayesian approach that considers the treatment effect to be random variable having some distribution may offer a better, more flexible approach. The Bayesian sample size proposed by (Whitehead et al., 2008 [27]) for exploratory studies on efficacy justifies the acceptable minimum sample size by a "conclusiveness" condition. In this work, we introduce a new two-stage Bayesian design with sample size reestimation at the interim stage. Our design inherits the properties of good interpretation and easy implementation from Whitehead et al. (2008) [27], generalizes their method to a two-sample setting, and uses a fully Bayesian predictive approach to reduce an overly large initial sample size when necessary. Moreover, our design can be extended to allow patient level covariates via logistic regression, now adjusting sample size within each subgroup based on interim analyses. We illustrate the benefits of our approach with a design in non-Hodgkin lymphoma with a simple binary covariate (patient gender), offering an initial step toward within-trial personalized medicine.
AB - Frequentist sample size determination for binary outcome data in a two-arm clinical trial requires initial guesses of the event probabilities for the two treatments. Misspecification of these event rates may lead to a poor estimate of the necessary sample size. In contrast, the Bayesian approach that considers the treatment effect to be random variable having some distribution may offer a better, more flexible approach. The Bayesian sample size proposed by (Whitehead et al., 2008 [27]) for exploratory studies on efficacy justifies the acceptable minimum sample size by a "conclusiveness" condition. In this work, we introduce a new two-stage Bayesian design with sample size reestimation at the interim stage. Our design inherits the properties of good interpretation and easy implementation from Whitehead et al. (2008) [27], generalizes their method to a two-sample setting, and uses a fully Bayesian predictive approach to reduce an overly large initial sample size when necessary. Moreover, our design can be extended to allow patient level covariates via logistic regression, now adjusting sample size within each subgroup based on interim analyses. We illustrate the benefits of our approach with a design in non-Hodgkin lymphoma with a simple binary covariate (patient gender), offering an initial step toward within-trial personalized medicine.
KW - Bayesian design
KW - Clinical trial
KW - Personalized medicine
KW - Predictive approach
KW - Sample size reestimation
KW - Subgroup analysis
UR - http://www.scopus.com/inward/record.url?scp=84888130583&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84888130583&partnerID=8YFLogxK
U2 - 10.1016/j.cct.2013.03.011
DO - 10.1016/j.cct.2013.03.011
M3 - Article
C2 - 23583925
AN - SCOPUS:84888130583
SN - 1551-7144
VL - 36
SP - 587
EP - 596
JO - Contemporary Clinical Trials
JF - Contemporary Clinical Trials
IS - 2
ER -