Statistical Synthesis

Transform extracted data into meaningful evidence summaries with automated meta-analysis and GRADE assessments.

Meta-Analysis Capabilities

Effect Measures

For Binary Outcomes

Risk Ratio (RR)
Odds Ratio (OR)
Risk Difference (RD)
Number Needed to Treat (NNT)

For Continuous Outcomes

Mean Difference (MD)
Standardized Mean Difference (SMD)
Weighted Mean Difference

For Time-to-Event

Hazard Ratio (HR)
Log Hazard Ratio

Analysis Models

Fixed-Effect Model

When studies are homogeneous
Single underlying effect assumed
Inverse variance weighting

Random-Effects Model

When heterogeneity exists
Accounts for between-study variance
DerSimonian-Laird or REML methods

Heterogeneity Assessment

Automatic calculation of:

I² statistic: Percentage of variability
Tau²: Between-study variance
Q statistic: Chi-squared test
Prediction intervals: Expected range for future studies

GRADE Assessment

Evidence Certainty

Rate confidence in your findings:

High: Very confident in estimate
Moderate: Moderately confident
Low: Limited confidence
Very Low: Very little confidence

Domains Assessed

Downgrading Factors

Risk of bias
Inconsistency
Indirectness
Imprecision
Publication bias

Upgrading Factors

Large effect
Dose-response gradient
Plausible confounding

Summary of Findings Tables

Automatically generated tables showing:

Each outcome
Relative and absolute effects
Number of studies and participants
Certainty rating
Plain language interpretation

Visualization Tools

Forest Plots

Publication-quality plots showing:

Individual study effects
Confidence intervals
Pooled estimate (diamond)
Weights
Heterogeneity statistics

Customization Options

Study labels
Subgroup divisions
Color schemes
Font sizes

Funnel Plots

Assess publication bias:

Study precision vs effect
Expected funnel shape
Asymmetry detection
Egger's test results

Additional Plots

L'Abbé plots
Galbraith plots
Influence plots
Leave-one-out analyses

Subgroup & Sensitivity Analyses

Subgroup Analysis

Compare effects across:

Study characteristics
Population subgroups
Intervention variations
Geographic regions

Sensitivity Analysis

Test robustness by:

Excluding high risk of bias studies
Using different effect measures
Varying statistical models
Removing outliers

AI-Powered Features

Intelligent Suggestions

The system recommends:

Appropriate effect measures
Analysis model selection
Potential subgroups to explore
Studies flagging for sensitivity analysis

Result Interpretation

Natural language summaries of:

Main findings
Heterogeneity interpretation
Clinical significance
Limitations

Export & Integration

Statistical Software

Export to:

R (metafor, meta packages)
Stata
RevMan
Comprehensive Meta-Analysis

Publication Formats

Generate:

Journal-ready figures
Supplementary data files
PRISMA flow diagrams

Best Practices

Before Analysis

Check data entry accuracy
Verify outcome definitions match
Consider clinical heterogeneity
Plan subgroup analyses a priori

Interpreting Results

Don't over-interpret small effects
Consider confidence interval width
Report heterogeneity transparently
Acknowledge limitations