Meta-Analysis
ScribeLabWriter's meta-analysis service is led by PhD biostatisticians who use R (metafor and meta packages) and Stata as the primary analytical platforms. Every analysis is delivered with reproducible, annotated code so you, your supervisor, or a journal reviewer can verify every calculation.
We support standalone meta-analyses for researchers who have already completed a systematic review and need statistical synthesis, and bundled meta-analyses as part of our full systematic review writing service.
Narrative synthesis describes patterns across studies in words. A meta-analysis quantifies them. It tells the reader not just that "most studies found a positive effect" but that the pooled odds ratio is 1.47 (95% CI: 1.12 to 1.93, p = 0.006) with moderate heterogeneity (I-squared = 58%) and moderate certainty of evidence on GRADE.
That level of precision is what journals, clinical guideline developers, and grant reviewers expect. A systematic review without a meta-analysis when the data supports one is often flagged by peer reviewers as incomplete. A systematic review with an incorrectly conducted meta-analysis is worse, because the pooled estimate may be misleading.
The most common errors in published meta-analyses include using fixed-effect models when random-effects are appropriate, reporting I-squared without tau-squared or prediction intervals, failing to conduct sensitivity analyses, ignoring publication bias, and not producing a GRADE Summary of Findings table. Our service addresses each of these by default.
We calculate the appropriate effect size based on your study designs and outcome types:
We use REML (Restricted Maximum Likelihood) as the default heterogeneity estimator, which is the current recommendation in the Cochrane Handbook (version 6.5, 2024) and the default in the R metafor package. REML produces less biased estimates of tau-squared than the traditional DerSimonian-Laird method, particularly when the number of studies is small. For reviews with fewer than 10 studies, we apply the Hartung-Knapp variance correction to produce more conservative confidence intervals.
We report both fixed-effect and random-effects models when appropriate, with a clear rationale for the primary model choice based on the expected clinical and methodological heterogeneity across your included studies.
Every meta-analysis includes a GRADE certainty of evidence rating per outcome, assessing five domains: risk of bias, inconsistency, indirectness, imprecision, and publication bias. Ratings are produced using GRADEpro GDT and delivered as a formatted Summary of Findings table ready for journal submission.
Every meta-analysis project includes:
| Need a meta-analysis for your systematic review? |
|---|
Our PhD biostatisticians deliver publication-ready meta-analyses with forest plots, GRADE tables, and reproducible R or Stata code. Get a Free Quote or Chat on WhatsApp. Get a Free Quote |
We use the following statistical software and tools for every meta-analysis:
All code is delivered in fully annotated, runnable scripts so any reviewer can reproduce every result.
Meta-analysis is appropriate when the included studies are sufficiently similar in design, population, intervention, and outcome measurement to justify statistical pooling. It is not always the right approach.
| Meta-Analysis Is Appropriate When | Narrative Synthesis Is More Appropriate When |
|---|---|
| Studies use comparable designs, populations, and outcome measures | Studies are too heterogeneous in design or population to pool meaningfully |
| At least 2 studies report extractable quantitative data for the same comparison | Only 1 study reports data for a given comparison |
| A precise summary estimate would inform clinical or policy decisions | The research question is exploratory and a pooled number would oversimplify the findings |
| Heterogeneity can be explored through pre-specified subgroup analyses | Heterogeneity is so high that subgroup analyses would not produce meaningful subsets |
If you are unsure whether meta-analysis is appropriate for your review, include that question in your enquiry. Our biostatisticians will assess the feasibility based on your included studies before recommending an analytical approach.
| Tier | Timeline | Best For |
|---|---|---|
| Standard | 3 to 5 weeks | Thesis chapters, dissertation meta-analyses, projects with flexible timelines |
| Priority | 1 to 2 weeks | Journal submissions with reviewer-requested additional analyses |
| Express | 3 to 7 days | Urgent revise-and-resubmit deadlines, conference submissions |
A meta-analysis requires a minimum of two studies reporting extractable quantitative data for the same comparison and outcome. However, meta-analyses with very few studies (fewer than five) have limited statistical power for heterogeneity assessment and publication bias testing. We will advise you on feasibility based on your included studies.
We use R (metafor and meta packages) and Stata as the primary platforms. For Cochrane reviews, we use RevMan. GRADE Summary of Findings tables are produced in GRADEpro GDT. Risk of bias visualizations use robvis. All code is delivered in annotated, reproducible scripts.
Yes. If you have already completed the systematic review (search, screening, data extraction) and need only the statistical synthesis, you can order the meta-analysis as a standalone service. We work from your extracted data. If the extraction needs refinement, we will advise you before proceeding.
A fixed-effect model assumes all studies estimate the same true effect. A random-effects model assumes the true effect varies across studies and estimates both the average effect and the between-study variance (tau-squared). Random-effects is the more conservative and more commonly appropriate choice for clinical reviews where populations, settings, and interventions differ across studies. We report both when relevant, with a clear rationale for the primary model.
I-squared quantifies the proportion of variability in the results that is due to heterogeneity rather than sampling error. An I-squared of 0% suggests no observed heterogeneity, 25% is low, 50% is moderate, and 75% or above is high (Higgins and Thompson, 2002). However, I-squared alone is insufficient. We always report tau-squared (the absolute between-study variance) and prediction intervals to give a complete picture of heterogeneity, as recommended by the Cochrane Handbook.
Yes. Every meta-analysis includes a GRADE certainty of evidence rating per outcome, assessing risk of bias, inconsistency, indirectness, imprecision, and publication bias. The output is a formatted Summary of Findings table produced in GRADEpro GDT, ready for your manuscript.
Yes. Network meta-analysis (comparing multiple interventions simultaneously using R netmeta or Stata network) and dose-response meta-analysis are available as specialized services. Include the details in your enquiry and we will scope the project accordingly.
Yes. Every meta-analysis is delivered with a fully annotated R or Stata script that reproduces every calculation, every figure, and every table. You or your reviewer can run the code independently and verify the results.
Describe your project and a PhD specialist will reply with an itemized quote within 24 hours. No signup, no payment, no obligation.
Prefer email? Send your project details to info@scribelabwriter.com
