PhD Researchers & Doctoral Candidates
A systematic review or meta-analysis chapter done to standard—a strong, citable contribution that also frames the rest of your thesis.
Research Methods
Meta-Analysis & Evidence Synthesis
A single study rarely settles a question. We pool the evidence properly—a pre-registered protocol, a reproducible search, careful effect-size extraction, and the right synthesis model—so your review says what the whole literature supports, not just what one dataset happened to show.
Systematic reviews & meta-analysis
PRISMA-standard reporting
R (metafor) · Stata
Reproducible, journal-ready
Sample output
study effect
pooled estimate
Overview
Synthesis that says what the evidence supports
A meta-analysis is only as trustworthy as the process behind the number. A pooled effect built on a search that missed half the literature, effect sizes extracted inconsistently, or a fixed-effects model masking real heterogeneity will produce a confident-looking estimate that a referee can dismantle. The credibility lives in the protocol, not the forest plot.
We treat the review as a designed study. That means a pre-specified protocol and inclusion criteria, a documented and reproducible search across the right databases, transparent screening with inter-rater checks, and careful, consistent effect-size extraction—before any pooling happens. Then the synthesis model is chosen to match the evidence: random-effects where heterogeneity is real, multilevel or three-level structures for dependent effects, meta-regression to explain variation rather than average it away.
What you receive is a defensible summary of a whole literature: the pooled estimate, its heterogeneity and its sources, a full publication-bias assessment, and a PRISMA-compliant write-up reproducible from the search string forward.
Who We Work With
For anyone synthesizing a body of evidence
If your question is "what does the literature as a whole say," this is the right desk to write to.
Faculty & Academic Researchers
Review and synthesis capacity for a high-visibility paper, with the screening and modeling handled to current methodological standards.
Research Institutes & Think Tanks
Evidence synthesis that consolidates a field—the kind of review that becomes a reference point for later work.
Policy Organizations
Evidence reviews for decisions, where knowing the weight and consistency of the evidence matters as much as any single study.
Journals & Editorial Teams
Independent methodological review of submitted systematic reviews and meta-analyses where synthesis rigor is in question.
Research Groups With Longitudinal Evidence
Teams synthesizing cross-lagged or panel findings across studies—where continuous-time methods (CoTiMA) apply.
Capabilities
The full synthesis toolkit, matched to your evidence
Organized from review design through advanced synthesis. If your evidence base needs a method not listed here, ask—this is the core, not the boundary.
Structured evidence reviews
The right review format for your question and evidence base—each with its own protocol and reporting standard.
- Narrative reviews
- Systematic reviews
- Scoping reviews
- Integrative reviews
- Umbrella reviews
- Systematic model reviews
Pooling, explaining & stress-testing effects
From conventional pooling to models for dependent effects, network comparisons, and rigorous bias assessment.
- Conventional meta-analysis
- Meta-regression
- Multilevel meta-analysis
- Three-level meta-analysis
- Bayesian meta-analysis
- Network meta-analysis
- Multivariate meta-analysis
- Dose-response meta-analysis
- Cumulative meta-analysis
- Robust variance estimation
- Publication-bias analysis
- Sensitivity analysis
- Meta-analytic SEM
Time-aware evidence synthesis
For pooling longitudinal and cross-lagged effects measured across studies with different time lags.
- Longitudinal meta-analysis
- Cross-lagged meta-analysis
- CoTiMA (continuous-time)
- Continuous-time meta-analytic models
How the Analysis Works
Six steps from question to pooled evidence
A transparent, PRISMA-aligned sequence—the protocol fixed before screening and every decision documented in the final review. Nothing is a black box.
Steps are adapted to your evidence base: the number and comparability of studies, the effect-size metric, and whether the effects are dependent or measured over time. We confirm the protocol with you before screening begins.
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1
Protocol
Define the question, inclusion criteria, effect-size metric, and analysis plan—pre-registered before any study is seen.
Inputs: question · PICO/criteria · effect metric
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2
Search
Build and run a reproducible search across the right databases, then deduplicate—documented so it can be re-run exactly.
Sources: Scopus · Web of Science · databases · grey literature
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3
Screen & extract
Title/abstract and full-text screening with inter-rater checks, then consistent effect-size and moderator extraction.
Steps: screening · inter-rater · extraction · risk of bias
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4
Synthesize
Pool effects with the model the evidence requires, quantifying heterogeneity rather than assuming it away.
Models: random-effects · multilevel · meta-regression · network
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5
Stress-test
Assess publication bias, run sensitivity and leave-one-out analyses, and test how robust the pooled estimate is.
Checks: funnel · Egger · trim-and-fill · leave-one-out
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6
Report
Deliver forest and funnel plots, a PRISMA flow diagram, methods and results text, and reproducible code and data.
Output: forest/funnel plots · PRISMA diagram · methods · R/Stata code
Rigor by default
The checks that decide whether a review holds up
A pooled estimate is easy to produce and easy to attack. The safeguards that make a synthesis credible are standard on every engagement.
Included on every project
- Pre-specified protocol and inclusion criteria
- Reproducible search string and PRISMA flow diagram
- Heterogeneity quantified (I², tau²) and explained
- Publication-bias assessment and sensitivity analysis
- Reproducible code and extraction dataset you keep
What You Receive
Every engagement, delivered in full
Not a black-box result and a number, but a complete, documented package you can submit, defend, and reproduce.
- Registered protocol and reproducible search string
- Screening records and PRISMA flow diagram
- Clean, documented effect-size extraction dataset
- Pooled estimates with heterogeneity statistics
- Forest, funnel, and moderator plots
- Publication-bias and sensitivity analysis
- Interpretation of the pooled effect and its limits
- Reproducible R (metafor) or Stata code
- Journal-ready methodology and results sections
Where this fits
Part of a larger arc
A synthesis is strongest when the review design ahead of it is deliberate and the reporting after it is precise—each handled with the same care.
Research Design & Planning
Identification strategy, power, and specification decided before estimation begins.
Explore methodsStatistical & Methodological Audit
An independent check of assumptions, specification, and reproducibility before submission.
Explore auditPublication & Research Support
Methods and results reporting, journal selection, and reviewer-response support.
Explore support
FAQ
Common questions
Answers to what most researchers and project leads ask before we begin a review or meta-analysis engagement.
A systematic review is a structured, reproducible search and appraisal of all evidence on a question; a meta-analysis is the statistical step that pools effect sizes across those studies into a summary estimate. Not every systematic review supports a meta-analysis—when studies are too heterogeneous, a rigorous narrative or scoping synthesis is the honest choice, and we will say so.
Yes. We work to PRISMA reporting standards, and where appropriate we help prepare and register a protocol (for example on PROSPERO or OSF) before screening begins. A pre-specified protocol is what protects the review from the criticism that decisions were made after seeing the results.
We quantify it, then explain it. That means reporting I² and tau², choosing random-effects over fixed-effects where heterogeneity is real, and using meta-regression and subgroup analysis to identify the study-level factors that drive differences in effects, rather than averaging them away.
Yes. We use funnel plots, Egger's and related tests, trim-and-fill, p-curve or selection models, and sensitivity analysis to gauge how far small-study effects or missing null results might be shaping the pooled estimate—and we report the pooled effect's robustness to them.
CoTiMA—continuous-time meta-analysis—pools longitudinal effects measured across studies that used different time lags, by estimating an underlying continuous-time model. It is the right tool when your evidence base is cross-lagged panel studies with varying intervals, where conventional meta-analysis of lag-specific coefficients would be misleading.
Either. We can design and run the full pipeline—search strategy, deduplication, title/abstract and full-text screening, and data extraction—or take a completed, screened dataset and handle the synthesis and modeling. We document each stage so the review is reproducible from the search string forward.
Primarily R (metafor, meta, and related packages) and Stata, with reference-management and screening tools for the review stage. You receive the extraction dataset, versioned analysis code, and a PRISMA flow diagram alongside the results.
Yes, and it is the best time to involve us. Defining the question, inclusion criteria, and effect-size metric up front—and checking that enough comparable studies exist to support a meta-analysis at all—prevents months of screening that ends in a synthesis the data can't support.
Related Expertise
Often paired with econometrics
Synthesizing a literature?
Tell us the question and roughly how many studies you're working with—we'll tell you whether it supports a meta-analysis, and what the review will take.