Statistical analysis
Statistical meta-analysis was performed with RStudio using themeta and metafor functions (RStudio Team (2015). RStudio: Integrated Development for R. RStudio, Inc., Boston, MA URL http://www.rstudio.com/). Statistical heterogeneity was not considered during the evaluation of the appropriate model of statistical analysis as the anticipated methodological heterogeneity of included studies did not leave space for assumption of comparable effect sizes among studies included in the meta-analysis [17]. Confidence intervals were set at 95%. We calculated pooled mean differences (MD) and 95% confidence intervals (CI) with the Hartung-Knapp-Sidik-Jonkman instead of the traditional Dersimonian-Laird random effects model analysis (REM). The decision to proceed with this type of analysis was taken after taking into consideration recent reports that support its superiority compared to the Dersimonian-Laird model when comparing studies of varying sample sizes and between-study heterogeneity [18]. When variables where expressed as median (range), median (interquartile range) or interquartile range and sample size transformation where performed to acquire the mean and standard deviation to include the studies in the meta-analysis [19].
Subgroup analysis was planned to be conducted on the basis of pregnancy trimester (1st, 2nd or 3rd), preeclampsia onset (early or late), severity (mild or severe) and complications (eclampsia and HELLP syndrome). Residual heterogeneity was planned to be explored by conducting meta-regression analysis taking into account the following parameters: year of publication, sample size (using a cut-off of 100 patients in at least one arm of the analysis), region (stratified in North America, Europe and other countries), Newcastle-Ottawa Scale score, study design, type of sample and definition of preeclampsia. Meta-regression was not performed for covariate levels with <3 studies. Publication bias was assessed by examining the possibility of small-study effects through the visual inspection of funnel plots. The asymmetry of funnel plots was statistically evaluated using the Egger’s regression and Begg-Mazumdar’s rank correlation tests. The Trim and Fill function was also used to evaluate potential differences in summary estimates after correction of asymmetry. Publication bias was evaluated by examining the potential presence of small-study effects through the visual inspection of contour enhanced and traditional funnel plots. Contour enhanced funnel plots permit the assessment of statistical significance of observed study estimates and may help differentiate if asymmetry arises from publication bias or other variables such as study quality.
Prediction intervals
Prediction intervals (PI) were also calculated, using the metafunction in RStudio, to evaluate the estimated effect that is expected to be seen by future studies in the field. The estimation of prediction intervals takes into account the inter-study variation of the results and express the existing heterogeneity at the same scale as the examined outcome.
Trial sequential analysis
To evaluate the information size, we performed trial sequential analysis (TSA) which permits investigation of the type I error in the aggregated result of meta-analyses performed for primary outcomes that were predefined in the present meta-analysis. A minimum of 3 studies was considered as appropriate to perform the analysis. Repeated significance testing increases the risk of type I error in meta-analyses and TSA has the ability to re-adjust the desired significance level by using the O‘ Brien-Flemming a-spending function. Therefore, during TSA sequential interim analyses are performed that permit investigation of the impact of each study in the overall findings of the meta-analysis. The risk for type I errors was set at 5% and for type II errors at 20%. The TSA analysis was performed using the TSA v. 0.9.5.10 Beta software (http://www.ctu.dk/tsa/).