Meta-analysis software

Turn reconciled data into results you can defend.

Build compatible evidence datasets, inspect study effects, assess heterogeneity and generate clear analysis outputs without disconnecting the statistics from the review workflow.

Start an analysisDiagnostic analysis
✓ MD / SMD✓ RR / OR / HR✓ Forest & funnel outputs
Forest plot previewRandom effects
Study A
0.84
Study B
0.91
Study C
0.77
Study D
1.02
Pooled
0.87
8studies
31%I² heterogeneity
Standard evidence synthesis

Match the analysis to the evidence.

MetaWiz supports common continuous, binary and time-to-event structures while keeping outcomes, units and time points explicit.

MD

Continuous outcomes

Analyze compatible means and SDs as mean differences or standardized mean differences.

RR

Binary outcomes

Pool event data using relative risk or odds ratio structures where appropriate.

HR

Time-to-event effects

Work with hazard ratios and confidence intervals while preserving model context.

Before you pool

Compatibility first. Statistics second.

Meta-analysis should not hide incompatible outcomes, units or populations. The workflow encourages researchers to verify the evidence set before running the model.

✓
Outcome compatibilityConfirm studies are answering the same analytic question.
✓
One independent contributionAvoid unintended double counting of overlapping cohorts or multiple models.
✓
Transparent sensitivity analysisUse subgroup and sensitivity decisions deliberately rather than as post hoc decoration.
Analysis readinessReady
12reconciled study rows
1outcome definition
0unit mismatches
0duplicate cohorts selected

Bring your review into one connected workspace.

Start with citation screening and keep evidence, reviewer decisions, datasets and analysis connected from beginning to end.

Request a demo