A Mixture Gatekeeping Procedure Based on the Hommel Test for Clinical Trial Applications

D’Agostino

2013

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121

This tutorial discusses important statistical problems arising in clinical trials with multiple clinical objectives based on different clinical variables, evaluation of several doses or regiments of a new treatment, analysis of multiple patient subgroups, etc. Simultaneous assessment of several objectives in a single trial gives rise to multiplicity. If unaddressed, problems of multiplicity can undermine integrity of statistical inferences. The tutorial reviews key concepts in multiple hypothesis testing and introduces main classes of methods for addressing multiplicity in a clinical trial setting. General guidelines for the development of relevant and efficient multiple testing procedures are presented on the basis of application-specific clinical and statistical information. Case studies with common multiplicity problems are used to motivate and illustrate the statistical methods presented in the tutorial, and software implementation of the multiplicity adjustment methods is discussed.

Section: Success Criteriamentioning

confidence: 99%

Traditional multiplicity adjustment methods in clinical trials

D’Agostino

2013

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121

“…The Hommel procedure is also based on a step‐up testing algorithm : Step i = 1. The null hypothesis H ( m ) is accepted if p ( m ) > α .…”

Section: Semiparametric Procedures In Single‐family Problemsmentioning

confidence: 99%

“…For example, Brechenmacher et al . applied this method to derive powerful Hommel‐based gatekeeping procedures that were successfully utilized in the lurasidone Phase III trials .…”

Section: Multiplicity Problems With Multiple Families Of Null Hypothesesmentioning

confidence: 99%

Key multiplicity issues in clinical drug development

D’Agostino

Huque

2012

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Much progress has been made over the past decade with the development of novel methods for addressing increasingly more complex multiplicity problems arising in confirmatory Phase III clinical trials. This includes traditional problems with a single source of multiplicity, for example, analysis of multiple endpoints or dose-placebo contrasts. In addition, more advanced problems with several sources of multiplicity have attracted attention in clinical drug development. These problems include two or more families of objectives such as multiple endpoints evaluated at multiple dose levels or in multiple patient populations. This paper provides a review of concepts that play a central role in defining and solving multiplicity problems (error rate definitions) and introduces main classes of multiple testing procedures widely used in clinical trials (nonparametric, semiparametric, and parametric procedures). The paper also presents recent advances in multiplicity research, including gatekeeping procedures for clinical trials with multiple sets of objectives. The concepts and methods introduced in the paper are illustrated using several case studies on the basis of real clinical trials. Software implementation of commonly used multiple testing and gatekeeping procedures is discussed.

“…The upper bound on the error fraction functions of the truncated Holm, Hochberg, and Hommel procedures, which will be used throughout this paper instead of the actual error fraction functions, is given by

f_{i} (I_{i} MathClass-rel| γ_{i}) MathClass-rel= γ_{i} MathClass-bin+ \frac{(1 MathClass-bin- γ_{i}) MathClass-rel| I_{i} MathClass-rel|}{n_{i}} MathClass-punc,

if I i is non‐empty and 0 otherwise, where | I i | is the number of elements in the index set I i . It is easy to see that the error fraction functions of these truncated procedures do not depend on α .…”

Section: Superchain Procedures In Two‐family Problemsmentioning

confidence: 99%

“…Further, it is critical to maximize the probability of technical success by fully utilizing all available information on the joint distribution of the test statistics associated with the individual objectives. This is accomplished by using semiparametric or parametric procedures that are more powerful than the basic Bonferroni procedure . Another important feature is the availability of multi‐step algorithms to streamline the process of building a procedure and also to facilitate interpretation of the results.…”

Section: Introductionmentioning

confidence: 99%

Superchain procedures in clinical trials with multiple objectives

Kordzakhia

2012

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This paper introduces a new class of multiple testing procedures for addressing multiplicity problems arising in clinical trials with multiple objectives grouped into families. The families may correspond to equally important sets of objectives (co-primary endpoints) or ordered sets of objectives (primary and secondary endpoints). The procedures, termed superchain procedures, serve as an extension of several classes of other multiple testing procedures, including chain procedures and parallel gatekeeping procedures. Superchain procedures exhibit several desirable features, including flexible decision rules that can be tailored to a broad class of win criteria in confirmatory clinical trials. Additionally, superchain procedures enable the trial's sponsor to efficiently incorporate available distributional information to improve overall power. Finally, superchain procedures rely on straightforward testing algorithms, which facilitates their development. We illustrate the new methodology by using clinical trial examples with two and three families of objectives.