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Related Concept Videos

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Multiple Comparison Tests

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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.
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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...
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The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
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McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
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On optimal two-stage testing of multiple mediators.

Vera Djordjilović1, Jesse Hemerik2, Magne Thoresen3

  • 1Department of Economics, Ca' Foscari University of Venice, Dorsoduro, Venice, Italy.

Biometrical Journal. Biometrische Zeitschrift
|April 15, 2022
PubMed
Summary
This summary is machine-generated.

This study optimizes the ScreenMin method for high-dimensional mediation analysis by deriving a new threshold. This improves the familywise error rate in finite samples, enhancing mediator identification in complex datasets.

Keywords:
familywise error ratehigh-dimensional mediationmultiple testingpartial conjunction hypothesisscreening

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Area of Science:

  • Statistics
  • Genetics
  • Epidemiology

Background:

  • High-dimensional mediation analysis requires efficient methods to identify mediators from numerous variables.
  • The ScreenMin procedure offers a two-step approach to control familywise error rate (FWER) in such settings.
  • Existing ScreenMin methods use a data-independent threshold, guaranteeing asymptotic FWER control.

Purpose of the Study:

  • To investigate the impact of the selection threshold on the finite-sample familywise error rate in ScreenMin.
  • To derive a novel threshold that maximizes statistical power while controlling FWER.
  • To evaluate the performance of the proposed threshold against existing methods.

Main Methods:

  • Derivation of a power-maximizing threshold for the ScreenMin procedure.
  • Approximation of the derived threshold using an adaptive threshold from existing literature (Wang et al., 2016).
  • Application and illustration of the optimized procedures in real-world datasets, including a case-control study and a replicability analysis of crop yield SNPs.

Main Results:

  • The derived power-maximizing threshold is well-approximated by an adaptive threshold.
  • The investigation provides insights into optimizing threshold selection for finite-sample FWER control.
  • The proposed methods are demonstrated effectively in both epidemiological and genetic contexts.

Conclusions:

  • The optimized threshold selection enhances the performance of the ScreenMin procedure in high-dimensional mediation analysis.
  • This work contributes to more accurate and powerful identification of mediators in complex biological and environmental studies.
  • The findings support the use of adaptive thresholds for improved FWER control and statistical power in mediation analysis.