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Multiple Comparison Tests

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...

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Multiple comparisons permutation test for image based data mining in radiotherapy.

Chun Chen¹, Marnix Witte, Wilma Heemsbergen

¹Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX, Amsterdam, The Netherlands. c.chen@nki.nl.

Radiation Oncology (London, England)

|December 25, 2013

Summary

This summary is machine-generated.

This study introduces a permutation test to compare radiotherapy dose distributions, identifying regions linked to patient outcomes. This method aids in understanding dose-response relationships and improving cancer treatment strategies.

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Area of Science:

Radiation Oncology
Medical Physics
Biostatistics

Background:

Comparing radiotherapy dose distributions is crucial for exploring dose-response relationships.
The challenge lies in addressing the multiple comparisons problem inherent in image data analysis.

Purpose of the Study:

To introduce a novel permutation test for comparing image data, specifically radiotherapy dose distributions.
To tackle the multiple comparisons problem in analyzing dose distributions and patient outcomes.

Main Methods:

A permutation test was developed using a Tmax statistic to summarize image differences into a single value.
The method was validated retrospectively on prostate cancer (3D dose distributions) and esophageal cancer (2D surface dose distributions) datasets.
Adjusted p-values were computed using a permutation procedure.

Main Results:

The permutation test successfully identified specific regions in dose distributions significantly associated with treatment failure in prostate cancer patients.
Significant regions linked to acute esophageal toxicity were identified in the esophagus study.
The method demonstrated its utility in data mining radiotherapy images.

Conclusions:

Permutation testing provides a robust method for directly comparing image data across different patient outcome groups.
This approach is valuable for identifying critical areas in radiotherapy dose distributions related to treatment efficacy and toxicity.
The developed method enhances the exploration of dose-response hypotheses in radiotherapy research.