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Moreover, the study recruited approximately equal proportions of patients categorised as GOLD B or D. Other strengths include the large sample size, very high compliance with the study medications, and application of statistical hierarchy methodology to avoid multiple comparisons and multiplicity issues.
Due to the observational nature of the study, we did not perform adjustments for multiplicity issues within endpoints or for multiplicity caused by multiple endpoints.
To assess this association requires specifying an appropriate measure of association, a suitable test statistic and, as the number of genes is large, proper handling of multiplicity issues.
Three approaches were adopted to mitigate the multiplicity issues.
Most trials are affected to some extent by multiplicity issues.
Multiplicity issues were addressed using the Bonferroni adjustment.
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We used FDR method to account for multiplicity issue and we used corrected p value from SAS 9.1.
Multivariate analysis may give an alternative approach to the multiplicity issue.
Numerous subgroup analyses of different endpoints, dose groups, and subgroup populations may lead to the multiplicity issue.
Established adjustment techniques (such as the Bonferroni, Hochberg, and related methods) can help to adjust for the multiplicity issue.
Hence, it would be unwise to try only one classification method: the multiplicity issue investigated in this study is relevant.
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