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ISSN: 2155-6180

Journal of Biometrics & Biostatistics
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Research Article

Adjustment on the Type I Error Rate for a Clinical Trial Monitoring for both Intermediate and Primary Endpoints

Susan Halabi*

Department of Biostatistics and Bioinformatics, Duke University Medical Center, USA

*Corresponding Author:
Susan Halabi, Ph.D.
Department of Biostatistics and Bioinformatics
Duke University Medical Center
2424 Erwin Road, Box 2721
Durham, NC 27710, USA
E-mail: [email protected]

Received date: April 10, 2012; Accepted date: May 18, 2012; Published date: May 19, 2012

Citation: Halabi S (2012) Adjustment on the Type I Error Rate for a Clinical Trial Monitoring for both Intermediate and Primary Endpoints. J Biomet Biostat S7:015. doi:10.4172/2155-6180.S7-015

Copyright: © 2012 Halabi S. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


 In many clinical trials, a single endpoint is used to answer the primary question and forms the basis for monitoring the experimental therapy. Many trials are lengthy in duration and investigators are interested in using an intermediate endpoint for an accelerated approval, but will rely on the primary endpoint (such as, overall survival) for the full approval of the drug by the Food and Drug Adminstration. We have designed a clinical trial where both intermediate (progression free survival, (PFS)) and primary endpoints (overall survival, (OS)) are used for monitoring the trial so the overall type I error rate is preserved at the pre-specified alpha level of 0.05. A two-stage procedure is used. In the first stage, the

Bonferroni correction was used where the global type I error rate was allocated to each of the endpoints. In the next stage, the O'Brien-Fleming approach was used to design the boundary for the interim and final analysis for each endpoint. Data were generated assuming several parametric copulas with exponential marginals. Different degrees of dependence, as measured by Kendall's τ , between OS and PFS were assumed: 0 (independence), 0.1, 0.3, 0.5 and 0.7. The results of the simulations were robust regardless of the copula that were assumed. We controlled for the global type I error and marginal type I rates for both of the endpoints under the null hypothesis. In addition, the global power and individual power for each endpoint were attained at the desired level under the alternative hypotheses. This approach is applied to an example in a prostate cancer trial.


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