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Both gene- and pathway-based analysis deal with the challenge of multiple testing error control (i.e. multiplicity correction ).
Statistical analysis of the data was performed by GeneSpring software (Agilent, Palo Alto, CA) using two-tailed t-tests with the FDR multiple testing error correction set at the p cut-off value of 0.05.
Where there were significant results in tests split by year and method, we checked them further with a Bonferroni simultaneous inference adjustment to control for multiple testing error.
The well-known Bonferroni correction and Benjamini Hochbergg false discovery rate (FDR) test is also included to reduce the effect of multiple testing error.
A staged analytic design applying a priori criteria to the results of conditional logistic regressions was employed to exclude associations likely due to multiple testing error.
Multiple testing error (false-positive results) was estimated by the false discovery rate (FDR = expected/observed) (71) procedure, and median FDRs are reported.
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SAM is a statistically rigorous test that incorporates a FDR calculation to correct for multiple testing errors.
Analyses employed both per-gene and common variance and utilized the Holm and False Discovery Rate methods to control for multiple testing errors [45], [46].
To account for multiple testing errors, a False Discovery Rate adjusted (FDR-adjusted) procedure was employed [ 25].
Resulting p-value was adjusted for multiple testing errors using false discovery rate (FDR) [ 66].
Correction for potential multiple testing errors was performed using the Bonferroni method.
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