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Related Concept Videos

Multiple Comparison Tests01:13

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.
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The Mantel-Cox Log-Rank Test

The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of interest.
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Overview of Minitab

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Identifying Statistically Significant Differences: The F-Test01:14

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Related Experiment Video

Updated: May 13, 2026

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
07:11

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

A graphical tool for locating inconsistency in network meta-analyses.

Ulrike Krahn1, Harald Binder, Jochem König

  • 1Division Medical Biometry, Institute of Medical Biostatistics, Epidemiology and Informatics-IMBEI, University Medical Center Johannes Gutenberg University Mainz, Obere Zahlbacher Str, 69, 55131 Mainz, Germany. ulrike.krahn@unimedizin-mainz.de

BMC Medical Research Methodology
|March 19, 2013
PubMed
Summary

Network meta-analyses can identify inconsistent study results using the net heat plot. This tool visualizes direct comparisons, highlighting potential sources of inconsistency for more reliable treatment effect estimates.

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Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)

Published on: November 27, 2019

Area of Science:

  • Biostatistics
  • Health Research Methodology

Background:

  • Network meta-analysis synthesizes evidence from multiple clinical trials.
  • Assessing consistency across different evidence pathways is crucial for valid analyses.
  • Deviations in direct comparisons can introduce inconsistencies, impacting network estimates.

Purpose of the Study:

  • To introduce the net heat plot, a novel tool for visualizing inconsistency in network meta-analyses.
  • To identify specific direct comparisons that drive network estimates and create inconsistencies.
  • To facilitate the detection of 'hot spots' of inconsistency within trial networks.

Main Methods:

  • The net heat plot visualizes the contribution of each direct comparison to network estimates.
  • Methods are based on fixed-effects models and regression diagnostics.
  • Heat colors indicate changes in agreement between direct and indirect estimates when consistency assumptions are relaxed, with clustering to identify inconsistency hot spots.

Main Results:

  • The net heat plot effectively identifies sources of inconsistency in constructed and published network meta-analyses.
  • The method pinpoints which network estimates are affected by specific inconsistent direct comparisons.
  • Examples demonstrate the tool's utility in revealing drivers of inconsistency.

Conclusions:

  • The net heat plot is valuable for identifying inconsistency sources and their interrelationships in network meta-analyses.
  • This visualization aids in understanding the impact of individual studies on overall network conclusions.
  • The tool supports further subject-matter-based investigations into network inconsistencies.