P-Values for Group Sequential Testing

Biometrika ◽  
1995 ◽  
Vol 82 (3) ◽  
pp. 650
Author(s):  
Myron N. Chang ◽  
A. Lawrence Gould ◽  
Steven M. Snapinn
Keyword(s):  
Sequential Testing ◽  
Group Sequential ◽  
P Values ◽  
Group Sequential Testing

P-values for group sequential testing

Biometrika ◽  
1995 ◽  
Vol 82 (3) ◽  
pp. 650-654 ◽  
Author(s):  
MYRON N. CHANG ◽  
A. LAWRENCE GOULD ◽  
STEVEN M. SNAPINN
Keyword(s):  
Sequential Testing ◽  
Group Sequential ◽  
P Values ◽  
Group Sequential Testing

scholarly journals Conditional Inference Following Group Sequential Testing

2003 ◽  
Vol 45 (5) ◽  
pp. 515-526 ◽  
Author(s):  
Pamela A. Ohman Strickland ◽  
George Casella
Keyword(s):  
Conditional Inference ◽  
Sequential Testing ◽  
Group Sequential ◽  
Group Sequential Testing

scholarly journals Strategies for validating biomarkers using data from a reference set

Biostatistics ◽  
2019 ◽  
Author(s):  
Lu Wang ◽  
Ying Huang ◽  
Ziding Feng
Keyword(s):  
Type I Error ◽  
Characteristic Curve ◽  
Stopping Rules ◽  
Research Network ◽  
Sequential Testing ◽  
Type I ◽  
Group Sequential ◽  
Testing Method ◽  
Reference Set ◽  
Group Sequential Testing

Summary Candidate biomarkers discovered in the laboratory need to be rigorously validated before advancing to clinical application. However, it is often expensive and time-consuming to collect the high quality specimens needed for validation; moreover, such specimens are often limited in volume. The Early Detection Research Network has developed valuable specimen reference sets that can be used by multiple labs for biomarker validation. To optimize the chance of successful validation, it is critical to efficiently utilize the limited specimens in these reference sets on promising candidate biomarkers. Towards this end, we propose a novel two-stage validation strategy that partitions the samples in the reference set into two groups for sequential validation. The proposed strategy adopts the group sequential testing method to control for the type I error rate and rotates group membership to maximize the usage of available samples. We develop analytical formulas for performance parameters of this strategy in terms of the expected numbers of biomarkers that can be evaluated and the truly useful biomarkers that can be successfully validated, which can provide valuable guidance for future study design. The performance of our proposed strategy for validating biomarkers with respect to the points on the receiver operating characteristic curve are evaluated via extensive simulation studies and compared with the default strategy of validating each biomarker using all samples in the reference set. Different types of early stopping rules and boundary shapes in the group sequential testing method are considered. Compared with the default strategy, our proposed strategy makes more efficient use of the limited resources in the reference set by allowing more candidate biomarkers to be evaluated, giving a better chance of having truly useful biomarkers successfully validated.

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