Meta-analysis in R Support
Meta-analysis support in R for effect-size preparation, random- and fixed-effect models, heterogeneity, subgroup analysis, meta-regression, and forest plots.
Meta-analysis in R should begin with compatible effect measures and a defensible synthesis plan. Tezyar helps prepare study-level estimates, choose an appropriate model, assess heterogeneity, run justified subgroup or meta-regression analyses, and produce reproducible tables and forest plots.
Prepare Comparable Effect Sizes
Outcomes may need to be expressed as risk ratios, odds ratios, mean differences, standardized effects, correlations, or other compatible measures before pooling.
Choose the Synthesis Model
Fixed-effect and random-effects models answer different assumptions about underlying effects; the choice should reflect the clinical or methodological context.
Heterogeneity and Influence
Heterogeneity statistics, prediction intervals where appropriate, influence diagnostics, and sensitivity analyses help assess whether one pooled estimate is an adequate summary.
Reproducible Output
Scripted analysis in R allows the dataset, model specification, plots, and tables to be regenerated consistently when the review is updated.
Frequently Asked Questions
Can you run subgroup analysis or meta-regression?
Yes, when there is a prespecified or scientifically defensible moderator and enough information to support the analysis.
Is a random-effects model always required?
No. Model choice should reflect the inferential target and assumptions, not a blanket rule.
Related specialized topics
Systematic review search strategy support for concepts, databases, controlled vocabulary, Boolean logic, documentation, and reproducibility.
Risk-of-bias assessment support using study-design-appropriate tools such as RoB 2, ROBINS-I, and JBI instruments.
GRADE support for rating certainty of evidence across outcomes and preparing transparent evidence profiles.
PRISMA 2020 support for systematic review reporting, flow diagrams, checklists, methods transparency, and manuscript review.
PROSPERO protocol support for eligible systematic reviews: review question, eligibility, outcomes, search approach, bias assessment, and synthesis plan.
Network meta-analysis support for multi-treatment evidence networks, assumptions, modelling, inconsistency assessment, ranking, and reporting.