Bayesian interim analysis in clinical trials.
We propose a Bayesian approach to monitor clinical trials with clustered binary outcomes using multivariate probit models. Our monitoring is based on the calculated probability of the reduced incidence rate using a new treatment compared with the standard treatment greater than a target improvement under different prior scenarios for the treatment effect. We develop a Bayesian sampling algorithm for posterior inference allowing missing values in the outcomes. We illustrate our method using a published early trail of inhaled nitric oxide therapy in premature infants.
Zhang
Xiao
X
Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, 35294, USA.
Cutter
Gary
G
D000465:Algorithms; D001499:Bayes Theorem; D026661:Clinical Trials Data Monitoring Committees; D002986:Clinical Trials as Topic / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D015999:Multivariate Analysis; D012106:Research