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We mention this only for completeness because, in the multiple testing scenarios we consider herein, the P values for the individual comparisons are so small that adjusting for multiple comparisons will not affect the conclusions presented.
Correction for multiple comparisons will be applied.
Adjustments for multiple comparisons will be used for secondary analysis.
Going forward, adjustment for multiple comparisons will be important to avoid spurious associations.
Corrections for multiple comparisons will be performed with Bonferroni or Dunn's test.
Issues of the relevance and the need for formal adjustment for multiple comparisons will clearly be very problem specific [ 16].
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To protect against multiple comparisons, we will use Fisher's protected least significant difference approach.
To accommodate for the possibility of inflated Type I error rate resulting from multiple comparisons, significance will be set at 0.01 (i.e., 99% confidence intervals).
In select cases, formal multiple comparisons procedures will be designated in analysis plans when statistical power is sufficient and it is not plausible to define a single primary analysis for a particular research question.
To determine comparability between the conditions at baseline, multiple planned comparisons will be conducted for continuous variables and chi-squared tests for categorical ones.
Example(s): In the study in Box 2, 10 cytokines will be tested, and multiple comparisons of treatments will be made for each cytokine.
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