A meta-analysis done for you by a professional statistical service delivers quantitative evidence synthesis that peer reviewers and journal editors expect: properly specified statistical models, publication-quality forest plots, comprehensive heterogeneity assessment, pre-specified subgroup and sensitivity analyses, publication bias evaluation, and GRADE summary of findings tables. The service uses validated software (R, Stata, or Comprehensive Meta-Analysis) and follows the statistical methods recommended by the Cochrane Handbook (Higgins et al., 2023).
What Professional Meta-Analysis Deliverables Include
Understanding exactly what you receive from a professional meta-analysis service eliminates uncertainty about the value of the investment. A complete deliverable package typically includes:
Primary forest plots for each outcome, showing individual study effect sizes with confidence intervals and the pooled summary estimate. Professional forest plots include study weights, heterogeneity statistics, and the statistical model used. These are publication-ready figures formatted to your target journal's specifications.
Heterogeneity assessment with I-squared (proportion of variation due to between-study differences), tau-squared (absolute between-study variance), and prediction intervals (the range within which a future study's effect is expected to fall). Understanding what these statistics mean is essential for interpreting your results.
Subgroup analyses stratifying results by pre-specified clinical or methodological moderators. Common subgroups include study design (RCT vs. observational), population characteristics (adults vs. children), intervention parameters (dose, duration), and risk of bias level (low vs. high).
Sensitivity analyses testing the robustness of the main finding. Standard sensitivity analyses include leave-one-out analysis (removing each study sequentially), excluding high risk-of-bias studies, comparing random-effects vs. fixed-effect models, and restricting to studies with low risk of bias.
Publication bias assessment using funnel plot creation tool (visual), Egger's regression test (statistical), and trim-and-fill analysis (adjusted estimate). For larger evidence bases, selection models and p-curve analysis may be added.
GRADE summary of findings tables rating the certainty of evidence across five domains: risk of bias, inconsistency, indirectness, imprecision, and publication bias. The our guide to grade framework is required by Cochrane and most clinical guideline panels.
Statistical code and output files so you can verify, reproduce, and modify the analyses. Professional services provide annotated R or Stata scripts that document every analytical decision.
The Data You Need to Provide
A meta-analysis service needs your extracted data in a structured format. The specific data requirements depend on your outcome types:
For continuous outcomes (means, scales, physiological measures): sample size (n), mean, and standard deviation for each group in each study. When studies report standard error, confidence intervals, or p-values instead, these can be converted. Our accurate effect size calculator demonstrates common conversions.
For binary outcomes (events, response rates, adverse events): the number of events and total participants in each group. This creates the familiar 2x2 table for odds ratio or risk ratio calculation.
For time-to-event outcomes (survival data): hazard ratios with confidence intervals or standard errors, or sufficient information to estimate them from Kaplan-Meier curves.
For correlation data: correlation coefficients with sample sizes, or regression coefficients with standard errors that can be converted.
Professional services typically provide a standardized data extraction template aligned with your planned analyses. This template ensures you collect exactly the data needed without missing critical fields or collecting unnecessary variables.