Case study: Unblinded sample size re-estimation in a bioequivalence study that has a group sequential design

2014 ◽  
pp. 397-402
Keyword(s):  
Sample Size ◽  
Bioequivalence Study ◽  
Sequential Design ◽  
Group Sequential Design ◽  
Group Sequential

Group sequential design for time-to-event data using the concept of proportional time

2019 ◽  
Vol 29 (7) ◽  
pp. 1867-1890
Author(s):  
Milind A Phadnis ◽  
Matthew S Mayo
Keyword(s):  
Sample Size ◽  
Sequential Design ◽  
Event Data ◽  
Efficacy And Safety ◽  
Group Sequential Design ◽  
Time To Event ◽  
Group Sequential ◽  
Generalized Gamma ◽  
Interim Monitoring ◽  
Time To Event Data

Sequential monitoring of efficacy and safety is an important part of clinical trials. A Group Sequential design allows researchers to perform interim monitoring after groups of patients have completed the study. Statistical literature is well developed for continuous and binary outcomes and relies on asymptotic normality of the test statistic. However, in the case of time-to-event data, existing methods of sample size calculation are done either assuming proportional hazards or assuming exponentially distributed lifetimes. In scenarios where these assumptions are not true, as evidenced from historical data, these traditional methods are restrictive and cannot always be used. As interim monitoring is driven by ethical, financial, and administrative considerations, it is imperative that sample size calculations be done in an efficient manner keeping in mind the specific needs of a clinical trial with a time-to-event outcome. To address these issues, a novel group sequential design is proposed using the concept of Proportional Time. This method utilizes the generalized gamma ratio distribution to calculate the efficacy and safety boundaries and can be used for all distributions that are members of the generalized gamma family using an error spending approach. The design incorporates features specific to survival data such as loss to follow-up, administrative censoring, varying accrual times and patterns, binding or non-binding futility rules with or without skips, and flexible alpha and beta spending mechanisms. Three practical examples are discussed, followed by discussion of the important aspects of the proposed design.

Information-based sample size re-estimation in group sequential design for longitudinal trials

2014 ◽  
Vol 33 (22) ◽  
pp. 3801-3814 ◽  
Author(s):  
Jing Zhou ◽  
Adeniyi Adewale ◽  
Yue Shentu ◽  
Jiajun Liu ◽  
Keaven Anderson
Keyword(s):  
Sample Size ◽  
Sequential Design ◽  
Group Sequential Design ◽  
Group Sequential

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.

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