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Graphical approaches for multiple comparison procedures using weighted Bonferroni, Simes, or parametric tests.

Frank Bretz¹, Martin Posch, Ekkehard Glimm

¹Statistical Methodology, Novartis Pharma AG, Basel, Switzerland. frank.bretz@novartis.com

Biometrical Journal. Biometrische Zeitschrift

|August 13, 2011

Summary

This study introduces extended graphical approaches for multiple hypothesis testing in clinical trials. These methods separate weighting strategies from test procedures, enhancing flexibility and application for complex study objectives.

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Area of Science:

Biostatistics
Clinical Trial Design
Statistical Methods

Background:

Confirmatory analysis of pre-specified multiple hypotheses is standard in pivotal clinical trials.
Existing multiple test procedures (e.g., fixed sequence, fallback, gatekeeping) and graphical approaches exist for common problems.
Current graphical methods often integrate weighting strategies with specific test procedures.

Purpose of the Study:

To present extended graphical approaches for multiple hypothesis testing.
To decouple weighting strategies from test procedures, allowing greater flexibility.
To facilitate the application of various statistical tests based on study objectives.

Main Methods:

Developed extended graphical approaches by separating weighting strategies from test procedures.
Applied these approaches to derive suitable weighting strategies reflecting study objectives.
Utilized appropriate test procedures including weighted Bonferroni, weighted parametric, and weighted Simes tests.
Illustrated methods with practical examples and briefly described the R package gMCP.

Main Results:

Extended graphical approaches offer a flexible framework for multiple hypothesis testing.
The decoupling of weighting and testing allows for tailored statistical strategies.
Demonstrated applicability across various clinical trial scenarios, including dose-response and multiple endpoints.
The gMCP package provides an implementation of these advanced methods.

Conclusions:

The proposed extended graphical approaches enhance the flexibility and applicability of multiple hypothesis testing in clinical trials.
Separating weighting strategies from test procedures allows for more precise alignment with study objectives.
These methods aid in the visualization and communication of complex statistical testing procedures.
The gMCP package supports the practical implementation of these advanced biostatistical techniques.