What Is a Meta-Analysis Service?
A meta-analysis service is a professional statistical service where PhD biostatisticians perform every step of quantitative evidence synthesis on your behalf. Unlike meta-analysis software that requires you to run the analysis yourself, our service means we do the work for you, from data extraction through final deliverables.
A meta-analysis is distinct from a systematic review. A systematic review identifies, screens, and qualitatively appraises the literature. A meta-analysis is the statistical component that pools quantitative results from included studies to produce a single summary effect estimate. Many projects require both, but not all systematic reviews include a meta-analysis, and not all meta-analyses are embedded within a systematic review.
When you hire a meta-analysis expert through Research Gold, you receive publication-ready results that satisfy journal peer reviewers and editorial boards. Our team follows the statistical methods described in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins et al., 2023) and reports findings in accordance with the PRISMA 2020 statement (Page et al., 2021).
Whether you already have extracted data from completed studies or you need us to pull the numbers from published papers, our meta-analysis writing service handles the entire quantitative synthesis pipeline.
The value of hiring a professional meta-analysis expert becomes clear when you consider the statistical complexity involved. Selecting the correct effect size metric, choosing between random-effects and fixed-effect models, diagnosing heterogeneity sources, assessing publication bias, and applying the GRADE framework all require specialized training. Our biostatisticians hold doctoral degrees in biostatistics, epidemiology, or a related quantitative discipline, and they have published meta-analyses across clinical medicine, public health, psychology, education, and the social sciences.
What Our Meta-Analysis Service Delivers
Every project includes a comprehensive set of statistical outputs designed for immediate use in your manuscript, thesis, or grant application. Here is exactly what you receive.
Effect Size Calculation
We calculate or convert the appropriate effect size for every included study. Depending on your outcome type, we compute:
- Cohen's d and Hedges' g for continuous outcomes (standardized mean differences)
- Odds ratios and risk ratios for dichotomous outcomes
- Mean differences for continuous outcomes measured on the same scale
- Hazard ratios for time-to-event data
- Correlation coefficients for association studies
When primary studies report incomplete data, we apply validated imputation methods to estimate effect sizes from confidence intervals, p-values, t-statistics, or F-statistics. You can explore how these calculations work using our free effect size computation tool.
Random-Effects or Fixed-Effect Model Selection
We select the appropriate pooling model based on the clinical and methodological characteristics of your included studies. The random-effects model accounts for between-study heterogeneity by assuming that true effects vary across studies, which is the typical scenario in health and social sciences research. The fixed-effect model assumes a single common effect size across all studies and is appropriate when studies are functionally identical in design, population, and intervention delivery.
In practice, we often present results from both models for transparency, highlighting any differences in the pooled estimate and its confidence interval. We explain the rationale for model selection in your results narrative, ensuring that reviewers can follow the analytical reasoning.
Forest Plots (Publication Quality)
Every meta-analysis includes forest plots for each pooled outcome. Forest plots display the individual study effect sizes, confidence intervals, weights, and the pooled summary estimate with its diamond. All forest plots are delivered in high-resolution PNG and editable PDF format, sized to match your target journal specifications. Preview the format using our free forest plot generator.
Funnel Plots with Egger's Test
We generate funnel plots to visually assess publication bias and small-study effects. Each funnel plot is accompanied by Egger's regression test for funnel plot asymmetry. When asymmetry is detected, we apply the trim-and-fill method to estimate how many studies may be missing and how the pooled estimate would change if those studies were included. Explore publication bias detection using our free publication bias detection tool.
Heterogeneity Assessment
Statistical heterogeneity is the variability in study results that exceeds what would be expected by sampling error alone. We report:
- I-squared (I2): the percentage of total variability attributable to between-study differences rather than chance
- Tau-squared: the estimated between-study variance in the random-effects model
- Cochran's Q-statistic: the formal chi-squared test for the presence of heterogeneity
- Prediction intervals: the range within which the true effect is expected to fall in a future study
Learn more about interpreting these metrics in our guide to understanding heterogeneity.
Subgroup Analyses
We conduct planned subgroup analyses to investigate whether the pooled effect varies across predefined study characteristics. Common subgroup variables include study design (randomized controlled trial versus observational), population age, intervention dose or duration, geographic region, and follow-up length. Each subgroup analysis includes a separate forest plot and a formal test for subgroup differences.
Subgroup analyses are pre-specified in your analysis plan to avoid the methodological criticism associated with data-driven, post hoc explorations. When reviewers ask for additional subgroups after submission, we add them under the unlimited revisions policy included with every tier.
Meta-Regression (Where Applicable)
When continuous moderators are hypothesized to explain heterogeneity, we run meta-regression models. Meta-regression quantifies the relationship between study-level covariates (such as mean age, sample size, publication year, or intervention intensity) and the observed effect sizes. This provides a more nuanced understanding of what drives variability across studies than subgroup analysis alone.
Sensitivity Analysis (Leave-One-Out)
We perform leave-one-out sensitivity analysis, systematically removing each study in turn and recalculating the pooled estimate to test its robustness. This identifies whether any single study disproportionately influences the overall result. Additional sensitivity analyses include restricting the pool to low-risk-of-bias studies and excluding studies with imputed data. Try this approach using our free leave-one-out analysis tool.
GRADE Summary of Findings Table
For each critical outcome, we produce a GRADE assessment that rates the certainty of evidence across five domains: risk of bias, inconsistency, indirectness, imprecision, and publication bias. The output is a formatted summary of findings table following Cochrane standards, ready for inclusion in your manuscript.
Fully Annotated R or Stata Code
Every analysis is accompanied by fully annotated, reproducible code. We use the metafor and meta packages in R, and the metan suite in Stata. The code covers every step from data import to final figure generation, allowing you and your reviewers to verify and replicate every result.
Our Meta-Analysis Process
Our process follows a structured, transparent workflow from initial consultation to final deliverables.
-
Consultation and feasibility assessment. We review your research question, included studies, and extracted data. If studies are already identified, we assess whether quantitative pooling is appropriate based on clinical and methodological similarity. We consider factors such as consistency of outcome measurement, comparability of interventions, and adequacy of reported statistics. If pooling is not feasible, we recommend alternative synthesis approaches such as narrative summary with individual study effect sizes.
-
Data preparation and verification. We extract or verify the numerical data needed for each included study. This includes sample sizes, means, standard deviations, event counts, group totals, and any other statistics required to calculate effect sizes. Where data are missing or reported inconsistently, we apply validated imputation methods.
-
Model specification and analysis. We run all planned analyses: pooled effect sizes under the appropriate model (random-effects or fixed-effect), forest plots for each outcome, heterogeneity assessment, subgroup analyses, meta-regression where applicable, sensitivity analyses, and publication bias tests.
-
GRADE certainty assessment. For each critical and important outcome, we assess the certainty of evidence using the GRADE framework and produce formatted summary of findings tables.
-
Deliverable preparation. We compile all outputs: annotated R or Stata code, forest plots, funnel plots, summary tables, GRADE tables, and a complete results narrative for your manuscript. All figures are delivered in publication-ready format.
-
Revisions and reviewer support. All tiers include unlimited revisions. If journal peer reviewers request additional analyses after your manuscript is submitted, we handle those requests at no additional cost.
Standalone Versus Bundled With Systematic Review
You can order our meta-analysis service on its own or bundle it with a full systematic review. The right choice depends on where you are in your research.
| Feature | Standalone Meta-Analysis | Systematic Review + Meta-Analysis Bundle |
|---|---|---|
| Starting price | $825 | $1,500 |
| Best for | Researchers who already have identified studies and extracted data | Researchers who need the full review from protocol to final manuscript |
| What is included | Effect size calculation, pooling, forest plots, funnel plots, heterogeneity assessment, subgroup and sensitivity analyses, GRADE tables, reproducible code | Everything in the standalone meta-analysis plus protocol development, database searching, screening, data extraction, risk of bias assessment, PRISMA flow diagram, and narrative synthesis |
| You provide | Extracted numerical data (or we extract from your included papers) | Research question and inclusion criteria |
| Typical timeline | 1 to 5 weeks depending on tier | 2 to 6 weeks depending on tier |
If you have already completed your systematic review and have data ready for pooling, the standalone option saves time and cost. If you need the complete evidence synthesis pipeline, our bundle provides significant savings compared to ordering each service separately. Request a quote and we will recommend the best option for your situation.
Pricing and Delivery Tiers
Our standalone meta-analysis pricing is transparent, with three tiers based on delivery speed. All tiers include the same comprehensive deliverables and unlimited revisions.
| Tier | Delivery Timeline | Price | Best For |
|---|---|---|---|
| Bronze | 4 to 5 weeks | $825 | Standard academic timelines |
| Silver | 2 to 3 weeks | $990 | Conference or grant deadlines |
| Gold | 1 week | $1,238 | Urgent reviewer requests or tight submission windows |
All tiers include: effect size calculation, forest plots, funnel plots, heterogeneity assessment, subgroup analyses, sensitivity analyses, meta-regression (where applicable), GRADE tables, reproducible R or Stata code, complete results narrative, and unlimited revisions.
View the full breakdown on our transparent pricing page or request a quote for a personalized estimate based on the number of studies and outcomes in your project.
Software We Use
We perform all statistical analyses using industry-standard software recognized by Cochrane, the Joanna Briggs Institute, and leading peer-reviewed journals.
R (metafor and meta packages). R is an open-source statistical computing environment. The metafor package provides a comprehensive suite of functions for fitting meta-analytic models, including random-effects and fixed-effect models, meta-regression, and diagnostic tests. The meta package offers additional convenience functions for forest plots, funnel plots, and influence analyses. All R code we deliver is fully reproducible.
Stata (metan suite). Stata is a commercial statistical platform widely used in epidemiology, public health, and clinical research. The metan suite provides commands for meta-analysis, forest plot generation, and publication bias testing. We use Stata when clients specifically request it or when their institution requires Stata-based reproducibility.
We select R or Stata based on your preference, your institution's requirements, or the conventions of your target journal. Both produce identical statistical results. All code is fully annotated with comments explaining each step, so you or your reviewers can follow the analytical logic from raw data to final output.
Who Uses Our Meta-Analysis Service
Our clients come from diverse academic and professional backgrounds, united by the need for rigorous quantitative synthesis.
-
Systematic review teams needing quantitative synthesis. You have completed the qualitative review and now need a biostatistician to pool the results. This is our most common use case.
-
Researchers with completed data extraction. You have a spreadsheet of effect sizes or raw data from included studies and need someone to run the analysis, generate figures, and write the results section.
-
PhD candidates. Your thesis committee requires a meta-analysis chapter, and you need expert statistical support to ensure the methodology is defensible at your viva or defense.
-
Grant applicants. A preliminary meta-analysis of existing evidence strengthens your grant application by demonstrating the evidence base and identifying gaps your proposed study would fill.
-
Authors responding to reviewer requests for meta-analysis. Journal reviewers have asked you to add a meta-analysis to your submitted manuscript. Our Gold tier can deliver results within one week to meet your revision deadline.
-
Guideline development groups. Clinical practice guidelines increasingly require meta-analytic evidence to support recommendations. Our GRADE assessments and summary of findings tables are formatted to meet guideline panel requirements.
-
Health technology assessment teams. Regulatory submissions and reimbursement dossiers often require formal meta-analyses of efficacy and safety data. We deliver the statistical rigor and documentation these submissions demand.
Learn more about our full range of analytical services on our biostatistics consulting page, or read how to read and interpret forest plots for a primer on the primary visual output of every meta-analysis.
Free Meta-Analysis Tools
We offer a suite of free, browser-based tools for researchers who want to explore meta-analytic concepts or run preliminary calculations before ordering our full service.
- Effect size calculator: Compute Cohen's d, Hedges' g, odds ratios, risk ratios, and other effect size metrics from your study data.
- Forest plot generator: Create publication-style forest plots directly in your browser.
- Funnel plot generator: Visualize potential publication bias with interactive funnel plots.
- Sample size calculator: Determine the required sample size for your primary study or the minimum number of studies for a well-powered meta-analysis.
- Sensitivity analysis tool: Run leave-one-out analyses to test the robustness of your pooled estimates.
These tools complement our professional service. Use them for exploration, teaching, or preliminary analysis. For publication-ready results with expert oversight, our full meta-analysis statistical service provides the rigor and documentation that peer reviewers expect.
For a detailed walkthrough of the methodology behind every analysis we perform, read our complete meta-analysis guide. It covers study selection, data extraction, model fitting, and results interpretation in step-by-step detail.
Get Started
Ready to outsource your meta-analysis to PhD biostatisticians? Share your research question, included studies, and extracted data with us. We reply with a detailed, personalized quote within 2 hours.
Get a free quote or explore our meta-analysis service page for a full overview of capabilities. If you also need the systematic review component, see our systematic review service or view transparent pricing for all tiers and bundles.