Data extraction and meta-analysis of data from all studies
Data were obtained from each study included in the systematic review and
documented in contingency tables. We extracted the necessary data to
calculate the incidence of PE in white women and in each other racial
group. Whenever possible, we extracted the reported relative risk (RR)
or odds ratio (OR) and 95% confidence intervals (CIs) from each study.
Where available we extracted separate RR estimates with different
degrees of confounder adjustment for the following prespecified
conventional risk factors (age, weight and height or body mass index,
smoking status and parity). First, we used raw data to adjust random
effect models for meta-analyses using inverse variance method for
pooling and DerSimonian-Laird to estimate the between-study variance
(τ2). Second, we used adjusted OR from the included studies to also
adjust the random effect model for meta-analysis with inverse variance
for pooling but, in this case, we used restricted maximum-likelihood
estimator (REML) for the between-study variance estimation. The pooled
RR and/or pooled OR with 95% confidence intervals were estimated for
race as a predictor for PE, using adjusted analysis as reported in the
studies and a random effects model that considers both within- and
between study variation30. Statistical heterogeneity
among studies was evaluated using the I2,
τ2 statistics and the p value of the Chi-Square test
of Q31.
Publication bias, when the minimum number of included studies was 10,
was assessed by plotting the RR estimate against precision (funnel
plots) by Begg’s adjusted rank correlation test, and by Egger’s
regression asymmetry test32,33.
Risk of bias assessment was made with quality in prognostic studies
(QUIPS) tool34 presented and adjusted for this review.
The following six domains were used: representativeness of study
population, adequateness of follow-up period and attrition,
appropriateness of racial origin classification, appropriateness of the
definition of the outcome (PE), adequateness of statistical analysis and
reporting. Each element was classified as low, moderate or high risk of
bias. If two of the domains were assessed as having high risk of bias or
four of the domains were assessed as having moderate risk of bias, then
the overall risk of bias for a study was graded as high risk of bias. If
three of the domains were assessed as having moderate risk of bias, or
one domain was at high risk of bias and one was at moderate risk then
the overall risk of bias was graded as moderate risk of bias. If all the
domains within a study were graded as low risk of bias, or less than
three were moderate and none was high, then the overall judgement for
the study was low risk of bias.
Statistical software R 35 was used in all analyses,
package ’meta’ 36 and ’metafor’ 37were used for the meta-analysis and package car 38 to
clean the data.