Sentence examples for multiple testing bias from inspiring English sources
To correct for multiple testing bias, association results were subjected to 100,000 permutation tests by pathway using Haploview.
Allele and haplotype counts were compared between patients and controls by chi-square test, and permutation testing (10 000 iterations) as implemented into Haploview was performed in order to obtain a measure of significance corrected for multiple testing bias.
Stratified bootstrapping method was used for stratified ethnicity and for multiple testing bias corrections due to deviation of normality.
Because of the large number of statistical tests conducted in Q-statistics run across complete residential histories, multiple testing bias is a concern.
MANCOVA test with 1,000 stratified bootstrap samples, and bias corrected and accelerated (BCa) 95% CI was used for multiple testing bias corrections due to deviation of normality.
To date, very few studies have been sufficiently large to overcome issues of multiple testing bias in screening for gene gene interactions (39, 56, 104).
This was done to account for multiple testing biases given the large number of events that were investigated as interruptions in four separate suicide time series.
Other models may have been used for serum markers, in order to take into account the relative changes of elevated marker (for example, 50% decrease between baseline at cycle 2...), but testing all the possibilities for serum marker analyses would have led to multiple testing biases favoring serum markers over CTC.
Although we adjusted for confounding by several factors, the possibility of chance associations resulting from multiple testing or bias due to uncontrolled confounding cannot be ruled out.
If further analyzes are conducted (e.g., by cell type) then the issues of multiple testing, including bias and chance, become material concerns.
When comparing outcomes between the treatment arms within the two groups of patients identified by bio-marker expression; P-values were not calculated for sub-groups to avoid multiple testing and bias, where one group was much smaller than the other.
