Power and Sample Size Estimation in Clinical Trials with Multiple Co-Primary Endpoints

• Activity Number: 522
• Type: Contributed
• Date/Time: Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Zuoshun Zhang*+
• Companies: Celgene Corporation
• Keywords: co-primary endpoints ; power ; sample size ; correlation matrix ; positive definite matrix ; Cholesky decomposition

Abstract

For some diseases, there needs to show the significance of a treatment in multiple co-primary endpoints. In the design stage, it will be helpful to have easily accessible tools for power and sample size estimation. Sozu et al. (J Biopharm Stat 2011, 650-668) provided formulas and gave examples for 2 and 3 co-primary endpoints using integration. For any number of co-primary endpoints, we give an alternative way for accurate and quick estimation for power and sample size. We factor the positive definite correlation matrix into a product using Cholesky decomposition and generate independent standard normal random numbers. The random numbers are transformed into correlated vectors corresponding to the sufficient test statistics of simulated trials, from which the power and sample size are estimated. For 2 or 3 continuous co-primary endpoints, we estimated the sample sizes and compared with t