Statistical Analysis
Meta-analyses using random-effects models were conducted for each
outcome. Adjusted mean differences (MD) and odds ratios (OR) were pooled
for continuous and categorical outcomes, respectively. Different scales
of reporting diet quality across studies, including one absolute unit
increase, one SD increase, tertiles, quartiles, quintiles were
transformed to calculate the effect size in the top tertile of diet
quality scores compared with the bottom tertile using methodology
reported in previous studies19,30,31. There are two
assumptions for this method: 1) diet quality scores are normally
distributed and 2) the associations with the outcomes are log-linear. In
a normal distribution, the means of the top and bottom tertiles,
quartiles, quintiles of are 2.18, 2.54, 2.80 SD apart, respectively. log
ORs and SEs were multiplied by 2.18/SD, 2.18, 2.18/2.54, 2.18/2.80 for
the transformation from 1 unit, 1 SD, quartile, quntile, respectively,
to estimates in tertile. Heterogeneity was assessed using the
I2 statistic, Cochran’s Q test (P-heterogeneity) and
by visual inspection of the forest plots. Sources of heterogeneity were
assessed by conducting subgroup analyses when there was a substantial
amount of heterogeneity (I2≥75% or
P-heterogeneity<0.05).
Sensitivity analyses were performed by excluding one study at a time to
evaluate the influence of any individual study on the pooled estimate.
The robustness of the estimates was also examined with sensitivity
analyses excluding moderate- and low-quality studies. Publication bias
was assessed by visual inspection of a funnel plot and Egger’s test when
at least 10 studies were included in the
meta-analyses32.
Data analyses were performed using R version 4.0.3 (R Project for
Statistical Computing). Two-tailed P values were used and P <
.05 was considered statistically significant.