Data synthesis and analysis
1. Summary effect estimates and 95% CI for each association between
environmental factors and childhood cancer risk were calculated through
fixed and random effects models .
2. Heterogeneity was assessed with the Cochran’s Q test and the
I2 statistic (ranging from 0% to 100%), defined as
the ratio of between-study variance over the sum of the within-study and
between-study variances . We further calculated the 95% CIs to assess
the uncertainty around heterogeneity estimates .
3. Ninety-five percent prediction intervals for the summary random
effect estimates were calculated to further assess heterogeneity and to
estimate the effect that would be expected in future studies
investigating the same association .
4. Small study effects, namely whether smaller studies tend to
contribute higher effect estimates compared to larger studies were also
examined; such differences between small and large studies may indicate
publication bias or other reporting biases, genuine heterogeneity or
chance . To account for small study effects, we used the Egger’s
regression asymmetry test (p ≤0.10) and we also assessed whether
the random effects summary estimate was larger than the point estimate
of the largest-most precise study, namely the study with the smallest
standard error included in each meta-analysis.
5. Excess significance bias (set for individual meta-analyses atp ≤0.10) were assessed exploring whether the observed number of
studies with nominally statistically significant results (“positive”
studies, p <0.05) within each meta-analysis was greater
than the expected number of studies with statistically significant
results. Specifically, we calculated the expected number of
statistically significant studies in each meta-analysis from the sum of
the statistical power estimates for each component study using an
algorithm from a non-central t distribution . The power estimates of
each component study depend on the plausible effect size for the tested
association, which was assumed to be the smallest standard error, namely
the effect of the largest study in each meta-analysis .