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Related Concept Videos

Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...
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Bonferroni Test01:10

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The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
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Ranks

Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
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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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Related Experiment Video

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The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies
08:24

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Published on: August 25, 2023

Rank reversal in indirect comparisons.

Edward C Norton1, Morgen M Miller, Jason J Wang

  • 1Department of Health Management and Policy, University of Michigan, Ann Arbor, MI 48109, USA. ecnorton@umich.edu

Value in Health : the Journal of the International Society for Pharmacoeconomics and Outcomes Research
|December 19, 2012
PubMed
Summary

Rank reversal can cause inconsistent results in indirect treatment comparisons. Choosing the right risk measure based on study design is crucial for accurate meta-analysis rankings.

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Area of Science:

  • Biostatistics
  • Clinical Epidemiology
  • Health Research Methods

Background:

  • Indirect comparisons in meta-analysis are valuable for evaluating treatments when direct trial data is unavailable.
  • Rank reversal, where treatment rankings change based on the effect measure used, poses a challenge to consistent interpretation.

Purpose of the Study:

  • To elucidate rank reversal as a source of inconsistency in indirect treatment comparisons.
  • To propose best practices for selecting appropriate risk measures in meta-analysis.

Main Methods:

  • Utilizing intuition, illustrative examples, graphical representations, and mathematical proofs.
  • Providing supporting software and discussing implications for research and policy.

Main Results:

  • Different effect measures (risk ratio, risk difference, odds ratio) can yield substantially different treatment rankings in indirect comparisons.
  • Rank reversal is demonstrated across common statistical measures.

Conclusions:

  • The selection of a risk measure is critical for accurate indirect treatment comparisons.
  • The choice of measure should be guided by the specific study design and its underlying conceptual framework.