Common threads between sample size recalculation and group sequential procedures

2003 ◽  
Vol 2 (4) ◽  
pp. 263-271 ◽  
Author(s):  
Todd A. Schwartz ◽  
Jonathan S. Denne
Keyword(s):  
Sample Size ◽  
Group Sequential ◽  
Sequential Procedures

Sample size re-estimation in group-sequential response-adaptive clinical trials

2003 ◽  
Vol 22 (24) ◽  
pp. 3843-3857 ◽  
Author(s):  
Caroline C. Morgan
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Group Sequential ◽  
Adaptive Clinical Trials

A group sequential design and sample size estimation for an immunotherapy trial with a delayed treatment effect

2020 ◽  
pp. 096228022098078
Author(s):  
Bosheng Li ◽  
Liwen Su ◽  
Jun Gao ◽  
Liyun Jiang ◽  
Fangrong Yan
Keyword(s):  
Sample Size ◽  
Treatment Effect ◽  
Search Algorithm ◽  
Rank Test ◽  
Group Sequential Design ◽  
Group Sequential ◽  
Delayed Treatment ◽  
Maximum Sample Size ◽  
Analytical Estimation ◽  
Immunotherapy Trial

A delayed treatment effect is often observed in the confirmatory trials for immunotherapies and is reflected by a delayed separation of the survival curves of the immunotherapy groups versus the control groups. This phenomenon makes the design based on the log-rank test not applicable because this design would violate the proportional hazard assumption and cause loss of power. Thus, we propose a group sequential design allowing early termination on the basis of efficacy based on a more powerful piecewise weighted log-rank test for an immunotherapy trial with a delayed treatment effect. We present an approach on the group sequential monitoring, in which the information time is defined based on the number of events occurring after the delay time. Furthermore, we developed a one-dimensional search algorithm to determine the required maximum sample size for the proposed design, which uses an analytical estimation obtained by the inflation factor as an initial value and an empirical power function calculated by a simulation-based procedure as an objective function. In the simulation, we tested the unstable accuracy of the analytical estimation, the consistent accuracy of the maximum sample size determined by the search algorithm and the advantages of the proposed design on saving sample size.

Group sequential designs with robust semiparametric recurrent event models

2018 ◽  
Vol 28 (8) ◽  
pp. 2385-2403 ◽  
Author(s):  
Tobias Mütze ◽  
Ekkehard Glimm ◽  
Heinz Schmidli ◽  
Tim Friede
Keyword(s):  
Error Rate ◽  
Joint Distribution ◽  
Recurrent Events ◽  
Type I Error ◽  
Type I ◽  
Sequential Designs ◽  
Group Sequential ◽  
Type I Error Rate ◽  
Sequential Procedures ◽  
Group Sequential Designs

Robust semiparametric models for recurrent events have received increasing attention in the analysis of clinical trials in a variety of diseases including chronic heart failure. In comparison to parametric recurrent event models, robust semiparametric models are more flexible in that neither the baseline event rate nor the process inducing between-patient heterogeneity needs to be specified in terms of a specific parametric statistical model. However, implementing group sequential designs in the robust semiparametric model is complicated by the fact that the sequence of Wald statistics does not follow asymptotically the canonical joint distribution. In this manuscript, we propose two types of group sequential procedures for a robust semiparametric analysis of recurrent events. The first group sequential procedure is based on the asymptotic covariance of the sequence of Wald statistics and it guarantees asymptotic control of the type I error rate. The second procedure is based on the canonical joint distribution and does not guarantee asymptotic type I error rate control but is easy to implement and corresponds to the well-known standard approach for group sequential designs. Moreover, we describe how to determine the maximum information when planning a clinical trial with a group sequential design and a robust semiparametric analysis of recurrent events. We contrast the operating characteristics of the proposed group sequential procedures in a simulation study motivated by the ongoing phase 3 PARAGON-HF trial (ClinicalTrials.gov identifier: NCT01920711) in more than 4600 patients with chronic heart failure and a preserved ejection fraction. We found that both group sequential procedures have similar operating characteristics and that for some practically relevant scenarios, the group sequential procedure based on the canonical joint distribution has advantages with respect to the control of the type I error rate. The proposed method for calculating the maximum information results in appropriately powered trials for both procedures.

Some group sequential procedures for comparing several treatments with a control

1996 ◽  
Vol 23 (4) ◽  
pp. 371-383 ◽  
Author(s):  
W. Liu
Keyword(s):  
Group Sequential ◽  
Sequential Procedures

ADAPTIVE GROUP SEQUENTIAL PROCEDURES FOR MULTIPLE COMPARISON OF TREATMENTS

2002 ◽  
Vol 31 (5) ◽  
pp. 781-793
Author(s):  
Bernard Cerutti ◽  
Toufik Zoubeidi ◽  
David M. Reboussin
Keyword(s):  
Multiple Comparison ◽  
Group Sequential ◽  
Sequential Procedures

Allocating recycled significance levels in group sequential procedures for multiple endpoints

2014 ◽  
Vol 57 (1) ◽  
pp. 90-107 ◽  
Author(s):  
Dong Xi ◽  
Ajit C. Tamhane
Keyword(s):  
Group Sequential ◽  
Multiple Endpoints ◽  
Sequential Procedures ◽  
Significance Levels

Adaptive Sample Size Calculations in Group Sequential Trials

Biometrics ◽  
1999 ◽  
Vol 55 (4) ◽  
pp. 1286-1290 ◽  
Author(s):  
Walter Lehmacher ◽  
Gernot Wassmer
Keyword(s):  
Sample Size ◽  
Group Sequential ◽  
Sample Size Calculations ◽  
Group Sequential Trials ◽  
Sequential Trials
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