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b P: P value for heterogeneity between subgroups with a meta-regression analysis.
c P: adjusted P value for heterogeneity between subgroups with a permutation test.
Few significant differences in physical status between subgroups with a stronger versus a weaker SOC were found in hip fracture patients [ 17].
This sample size will allow us to detect differences of approximately 3% in participation rate between subgroups, with a continuity corrected chi-square test and a 0.05 two-sided significance level, requesting a power of 80%.
For subgroup analysis to compare whether treatment effects differed by patient characteristics, hypothesis testing will be based on the comparison of treatment effects between subgroups, with a two-tailed α of 0.10 used to offset the decreased precision available for estimating interaction effects (ie, differences in differences).
Calculations showed that with this sample set and assuming that at least 30 to 35 genes are truly differentially expressed between subgroups with a fold change of 2.0 or more, at least 10 differentially expressed genes would be detected with a false discovery rate of 0.1.
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Conduct problems proved to be useful for distinguishing subgroups with more-severe ADHD symptoms from the subgroup with less-severe symptoms at study start, but this variable was less suited to differentiating between subgroups with and without a good treatment response after switching medication.
Comparisons were made between subgroups with different BRCA1/2 status, to a series of normal fallopian tube samples, as well to a previously reported series of normal tissue samples and tumors of mesenchymal origin.
We explored age differences between subgroups with box plots and performed a Kruskal-Wallis test to test the statistical difference.
SOCS 1 - 3 Mean mRNA expression levels in a cohort of 128 breast cancer patients; a comparison between subgroups with different tumour grade, NPI score, and TNM stage.
We then performed all pairwise comparisons between subgroups with use of log-rank tests with a Bonferroni adjustment to correct for multiple testing.
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