corrected for multiple comparisons OR = Odds ratio; Theme A = the cell; Theme B = the man; Theme C = the society; Theme D = the doctor.
On the basis of logistic regression analysis with adjustment for sex, age, status of smoking and drinking, smoking level, family history and nationality, only one SNP, i.e. rs12542354, was significantly associated with the susceptibility to NPC in the Guangxi population after correction for multiple comparisons (OR = 1.32, 95 % CI = 1.13-1.54, P = 0.00040; Additional file 1: Table S4).
There was insufficient power to correct for multiple comparisons or to covary for potential confounding variables.
Post hoc tests were performed using Bonferroni correction for multiple comparisons or Dunett's test for comparison with a control group.
Statistics were analyzed using either ANOVA (for multiple comparisons) or Student's 2-tailed t test for comparing two means.
When multiple comparisons or more than two groups were analyzed, Bonferroni's correction to the significance level α was invoked.
The statistical significance was determined by ANOVA followed by Bonferroni post-hoc test for multiple comparisons or the Student's t-test.
Comparisons of the microarray data used Kruskall-Wallis test with post-hoc Dunn's test for multiple comparisons or Mann Whitney tests.
Data were analyzed using a one-way ANOVA and Student-Newman-Keuls tests for multiple comparisons or Student's test for unpaired data.
A one-way ANOVA followed by Newman Keuls' multiple comparisons or unpaired Student's t-tests were used to compare control and treated groups, with p values <0.05 being considered as statistically significant.
Comparisons of counts per minute (for radioactivity) or counts per second (for TRF), either as absolute numbers, percentage of total counts, or ratios of bound to unbound were done using a one way (Kruskal-Wallis test with Dunn's test for multiple comparisons) or two-way ANOVA (Bonferroni post-test for multiple comparisons).
