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Journal ArticleDOI

A note on P-values under group sequential testing and nonproportional hazards.

TLDR
Two of the most commonly used methods for computing proper P-values following a group sequential test, based upon the analysis time (AT) and Z-statistic orderings, are compared with respect to resulting power functions when treatment effects on survival are delayed.
Abstract
Summary. Group sequential designs are often used for periodically assessing treatment efficacy during the course of a clinical trial. Following a group sequential test, P-values computed under the assumption that the data were gathered according to a fixed sample design are no longer uniformly distributed under the null hypothesis of no treatment effect. Various sample space orderings have been proposed for computing proper P-values following a group sequential test. Although many of the proposed orderings have been compared in the setting of time-invariant treatment effects, little attention has been given to their performance when the effect of treatment within an individual varies over time. Our interest here is to compare two of the most commonly used methods for computing proper P-values following a group sequential test, based upon the analysis time (AT) and Z-statistic orderings, with respect to resulting power functions when treatment effects on survival are delayed. Power under the AT ordering is shown to be heavily influenced by the presence of a delayed treatment effect, while power functions corresponding to the Z-statistic ordering remain robust under time-varying treatment effects.

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Citations
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Journal ArticleDOI

Frequentist evaluation of group sequential clinical trial designs

TL;DR: This paper describes how the frequentist operating characteristics of a particular stopping rule might be evaluated to ensure that the selected clinical trial design satisfies the constraints imposed by the many different disciplines represented by the clinical trial collaborators.
Journal ArticleDOI

Adaptive clinical trial designs with pre-specified rules for modifying the sample size: understanding efficient types of adaptation

TL;DR: Insight is provided into what are good and bad choices of adaptive sampling plans when the added flexibility of adaptive designs is desired, demonstrating in realistic settings that simple and easily implemented pre-specified adaptive designs provide only very small efficiency gains over group sequential designs with the same number of analyses.
Journal ArticleDOI

An evaluation of inferential procedures for adaptive clinical trial designs with pre-specified rules for modifying the sample size.

TL;DR: The cost of failing to plan ahead in settings where adaptations could realistically be pre-specified at the design stage is found to be meaningful for all designs and treatment effects considered, and to be substantial for designs frequently proposed in the literature.
Book ChapterDOI

Designing, Monitoring, and Analyzing Group Sequential Clinical Trials Using the RCTdesign Package for R

TL;DR: This manuscript demonstrates how the RCTdesign package in R can be used to select, implement, and analyze a group sequential stopping rule and illustrates trial design and monitoring in the context of an experimental monoclonal antibody in patients with relapsed chronic lymphocytic leukemia.
Journal ArticleDOI

A random walk approach for quantifying uncertainty in group sequential survival trials

TL;DR: A method of imputation of future alternatives using a random walk approach that incorporates a Bayesian conditional hazards model and splits the prior distribution for model parameters across regions of sampled and unsampled support is proposed and applied to survival data stemming from trial 002 of the PCRA study.
References
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Journal ArticleDOI

A multiple testing procedure for clinical trials.

TL;DR: The overall size of the procedure is shown to be controlled with virtually the same accuracy as the single sample chi-square test based on N(m1 + m2) observations and the power is found to bevirtually the same.
Book

Counting Processes and Survival Analysis

TL;DR: The Martingale Central Limit Theorem as mentioned in this paper is a generalization of the central limit theorem of the Counting Process and the Local Square Integrable Martingales (LSIM) framework.
Journal ArticleDOI

Group sequential methods in the design and analysis of clinical trials

TL;DR: In this article, a group sequential design is proposed to divide patient entry into a number of equal-sized groups so that the decision to stop the trial or continue is based on repeated significance tests of the accumulated data after each group is evaluated.
Book

Group Sequential Methods with Applications to Clinical Trials

TL;DR: A short history of sequential and group sequential methods can be found in this paper, where the authors present a road map for the application of two-sided tests for comparing two treatments with normal response of known variance.