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ISSN: 2155-6180

Journal of Biometrics & Biostatistics
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Research Article

Finding Critical Values to Control Type I Error for a Biomarker Informed Two-Stage Winner Design

Jing Wang1,2*, Sandeep Menon1,4 and Mark Chang1,3

1Department of Biostatistics, Boston University, 801 Massachusetts Avenue, Boston, MA 02118, USA

2Gilead Sciences, Inc. 303 Velocity Way, Foster City, CA 94404, USA

3AMAG Pharmaceuticals, Inc., 100 Hayden Avenue, Lexington, MA 02420, USA

4Pfizer, Inc., 35 Cambridge Park Drive, Cambridge, MA 02140, USA

*Corresponding Author:
Jing Wang
Gilead Sciences, Inc. 303 Velocity Way
Foster City, CA 94404, USA
Tel: 650-522-1167
E-mail: [email protected]

Received date: May 06, 2014; Accepted date: July 04, 2014; Published date: July 10, 2014

Citation: Wang J, Menon S, Chang M (2014) Finding Critical Values to Control Type I Error for a Biomarker Informed Two-Stage Winner Design. J Biomet Biostat 5:207. doi: 10.4172/2155-6180.1000207

Copyright: © 2014 Wang J, et al. 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 are credited.

Abstract

Adaptive clinical trial designs have been getting very popular in recent years. The PhRMA Working Group defines an adaptive design as a clinical study design that uses accumulating data to direct modification of aspects of the study as it continues, without undermining the validity and integrity of the [1]. These designs can assist in potentially accelerating clinical development and improving efficiency. However, the multiple interim looks and adaptive adjustments with the design can lead to inflation of type I error. Over the past decade, several statistical approaches have been proposed to control the inflation, some of which have been widely applied in practice. Some of these approaches include: error spending approach for classical group sequential plans [2-4]; Combination of p-values, such as Fisher’s combination test [5,6], Inverse Normal Method [7], sum of p-values approach [8]; conditional error function [9-11]; fixed weighting method [12]; variance spending method [13,14]; and multiple testing methodology such as closed test procedures [15-17].

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