Sequential Designs and Methods—Part III: Classical Group Sequential Trials

2021 ◽  
pp. 203-230
Author(s):  
Weichung Joe Shih ◽  
Joseph Aisner
Keyword(s):  
Classical Group ◽  
Sequential Designs ◽  
Group Sequential ◽  
Group Sequential Trials ◽  
Sequential Trials

Group sequential trials revisited: Simple implementation using SAS

2010 ◽  
Vol 20 (6) ◽  
pp. 635-656 ◽  
Author(s):  
John Whitehead
Keyword(s):  
Final Analysis ◽  
Design Stage ◽  
Sequential Designs ◽  
Group Sequential ◽  
Sequential Approach ◽  
Final Sample ◽  
Wide Range ◽  
Group Sequential Trials ◽  
Simple Implementation ◽  
Sequential Trials

The methodology of group sequential trials is now well established and widely implemented. The benefits of the group sequential approach are generally acknowledged, and its use, when applied properly, is accepted by researchers and regulators. This article describes how a wide range of group sequential designs can easily be implemented using two accessible SAS functions. One of these, PROBBNRM is a standard function, while the other, SEQ, is part of the interactive matrix language of SAS, PROC IML. The account focuses on the essentials of the approach and reveals how straightforward it can be. The design of studies is described, including their evaluation in terms of the distribution of final sample size. The conduct of the interim analyses is discussed, with emphasis on the consequences of inevitable departures from the planned schedule of information accrual. The computations required for the final analysis, allowing for the sequential design, are closely related to those conducted at the design stage. Illustrative examples are given and listings of suitable of SAS code are provided.

Approximately Optimal One-Parameter Boundaries for Group Sequential Trials

Biometrics ◽  
1987 ◽  
Vol 43 (1) ◽  
pp. 193 ◽  
Author(s):  
Samuel K. Wang ◽  
Anastasios A. Tsiatis
Keyword(s):  
Group Sequential ◽  
Group Sequential Trials ◽  
Sequential Trials

Exact Statistical Inference for Group Sequential Trials

Biometrics ◽  
1991 ◽  
Vol 47 (4) ◽  
pp. 1399 ◽  
Author(s):  
D. Y. Lin ◽  
L. J. Wei ◽  
D. L. DeMets
Keyword(s):  
Statistical Inference ◽  
Group Sequential ◽  
Group Sequential Trials ◽  
Sequential Trials ◽  
Exact Statistical Inference

Incorporating Surrogate Endpoints into Group Sequential Trials

1996 ◽  
Vol 38 (1) ◽  
pp. 119-130 ◽  
Author(s):  
Richard J. Cook ◽  
Vern T. Farewell
Keyword(s):  
Surrogate Endpoints ◽  
Group Sequential ◽  
Group Sequential Trials ◽  
Sequential Trials

scholarly journals Robustness of testing procedures for confirmatory subpopulation analyses based on a continuous biomarker

2018 ◽  
Vol 28 (6) ◽  
pp. 1879-1892 ◽  
Author(s):  
Alexandra Christine Graf ◽  
Gernot Wassmer ◽  
Tim Friede ◽  
Roland Gerard Gera ◽  
Martin Posch
Keyword(s):  
Error Rate ◽  
Dependence Structure ◽  
Sequential Designs ◽  
Group Sequential ◽  
Testing Procedures ◽  
Prognostic Effect ◽  
Type 1 Error ◽  
The Family ◽  
Sequential Trials

With the advent of personalized medicine, clinical trials studying treatment effects in subpopulations are receiving increasing attention. The objectives of such studies are, besides demonstrating a treatment effect in the overall population, to identify subpopulations, based on biomarkers, where the treatment has a beneficial effect. Continuous biomarkers are often dichotomized using a threshold to define two subpopulations with low and high biomarker levels. If there is insufficient information on the dependence structure of the outcome on the biomarker, several thresholds may be investigated. The nested structure of such subpopulations is similar to the structure in group sequential trials. Therefore, it has been proposed to use the corresponding critical boundaries to test such nested subpopulations. We show that for biomarkers with a prognostic effect that is not adjusted for in the statistical model, the variability of the outcome may vary across subpopulations which may lead to an inflation of the family-wise type 1 error rate. Using simulations we quantify the potential inflation of testing procedures based on group sequential designs. Furthermore, alternative hypotheses tests that control the family-wise type 1 error rate under minimal assumptions are proposed. The methodological approaches are illustrated by a trial in depression.

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