2.3. Statistical analysis

For the control group of each study, the observed genotype frequencies of MS A2756G polymorphism were assessed for HWE. The strength of association between MS A2756G polymorphism and hematologic neoplasm risk was assessed by calculating ORs with the corresponding 95% CIs for homozygote (GA vs AA), heterozygote (GG vs AA), dominant (AG+GG vs AA), and recessive (GG vs AG+AA) models, respectively.^[39,40] Heterogeneity was assessed by a chi-square-based Q-statistic test (P < .10 was considered significant). Heterogeneity was quantified using the I² metric (I² < 25% no heterogeneity; I² = 25–50% moderate heterogeneity; I² > 50% large or extreme heterogeneity).^[41,42] When heterogeneity was present, the random effects model (the DerSimonian and Laird method) was used to calculate the pooled ORs, whereas the fixed effects model (the Mantel-Haenszel method) was used. The main source of heterogeneity was determined by Galbraith plot.^[41] Subgroup analysis was controlled by cancer type, race, and source of controls. To assess the effect of individual studies on the overall risk of cancers, sensitivity analyses were performed by excluding each study individually and recalculating the ORs and the 95% CIs.

We carried out a cumulative meta-analysis of the effect of the MS A2756G polymorphism on hematologic neoplasm risk based on the date of publication. Analysis of publication bias was shown with the funnel plot and Egger's linear regression asymmetry test; P < .05 suggested statistically significant publication bias.^[42,43] All statistical analyses were performed using STATA statistical software (version 12.0; STATA Corporation, College Station, TX), and all tests were 2 tailed.