Calculate and convert between common effect size measures: Cohen’s d, Hedges’ g, odds ratios, risk ratios, and correlation coefficients. Central hub of the analysis pipeline—compute effect sizes from raw data or test statistics, then send results directly to forest plot, funnel plot, or heterogeneity tools.
Features
SMD, OR, RR, r
Batch CSV/Excel import
Pipeline to forest & funnel
From t/F/χ²/p conversion
Assess heterogeneity with I², Cochran’s Q, τ², and prediction intervals using DerSimonian-Laird and REML estimators. Import studies directly from the pipeline for seamless meta-analysis workflow.
Features
I² & Cochran’s Q
REML τ² estimator
Pipeline import
CSV/Excel import
Create publication-ready forest plots with subgroup analysis, cumulative meta-analysis, Galbraith and Baujat diagnostic plots, REML and DerSimonian-Laird estimators, prediction intervals, R code generation, and leave-one-out sensitivity testing. Selectable confidence levels (80%, 90%, 95%, 99%).
Features
Subgroup analysis
Cumulative meta-analysis
Galbraith & Baujat plots
REML estimator
Prediction intervals
R code (metafor)
Leave-one-out sensitivity
Methods text generator
Copy plot to clipboard
CSV/Excel import
Generate contour-enhanced funnel plots with Egger's regression, Begg's rank test, trim-and-fill analysis, and fail-safe N (Rosenthal, Orwin, Rosenberg). Selectable confidence levels, R code generation, and auto-generated methods paragraphs for publication.
Features
Contour-enhanced plot
Egger's & Begg's tests
Trim-and-fill
Fail-safe N (3 methods)
R code (metafor)
Methods text generator
Copy plot to clipboard
Pipeline import
Convert between odds ratios, risk ratios, absolute risk reduction, and number needed to treat. Enter any measure with baseline risk to compute all others.
Features
OR/RR/ARR/NNT
Baseline risk input
Bidirectional conversion
Copy results
Structure moderator data for meta-regression analysis. Validate and format study-level data for export to R (metafor), Stata, or Comprehensive Meta-Analysis.
Features
Moderator variables
Data validation
Multi-format export
Template builder
Perform leave-one-out sensitivity analysis with D3-powered visualization showing how each study impacts the pooled estimate. Selectable confidence levels, copy plot to clipboard, auto-generated methods paragraphs, and high-resolution PNG/SVG export.
Features
Leave-one-out
D3 forest-style plot
Copy plot to clipboard
Methods text generator
Selectable CI levels
PNG & SVG export
Evaluate whether significant findings contain evidential value or show signs of p-hacking using p-curve analysis (Simonsohn et al., 2014). Binomial and continuous right-skew tests, flatness test against 33% power, D3.js histogram, R code for dmetar, and auto-generated methods text.
Features
P-value input or t/F/χ²/z conversion
Binomial right-skew test
Stouffer continuous test
Flatness test vs 33% power
D3.js p-curve histogram
R code (dmetar)
Methods text
CSV import/export
Generate Doi plots with LFK index for detecting publication bias in meta-analyses, especially with fewer than 10 studies. Superior statistical power compared to funnel plots and Egger's test (Furuya-Kanamori, Barendregt & Doi, 2018).
Features
Doi plot (normal quantile vs Z-score)
LFK index with interpretation
Asymmetry shading
Identity line reference
D3.js interactive plot
R code generation
Methods text
CSV import/export
Compute sensitivity, specificity, positive and negative predictive values, likelihood ratios, and diagnostic odds ratio from a 2×2 contingency table.
Features
2×2 table input
Sensitivity/Specificity
LR+/LR−
DOR & Youden’s J
Compute Bayes factors for common test scenarios including one-sample and two-sample t-tests, correlations, and proportions with interpretation guidelines.
Features
Multiple test types
BF10 & BF01
Interpretation scale
Jeffreys’ guidelines
Pool single-group proportions (prevalence, complication rates, response rates) across studies using raw, logit, Freeman-Tukey double arcsine, or arcsine square root transformations with fixed or random-effects models.
Features
4 transformations
DerSimonian-Laird
Forest plot
Prediction interval
Generate L'Abbe plots (L'Abbe et al., 1987) to visualize treatment vs control event rates in binary outcome meta-analyses. Bubble sizing by sample size, diagonal no-effect line, optional constant risk ratio reference lines, and heterogeneity detection.
Features
Binary outcome scatter
Bubble sizing by N
Constant RR lines
D3.js interactive
R code generation
Methods text
CSV import
PDF/PNG export
Generate GOSH plots (Olkin et al., 2012) for all-subsets heterogeneity analysis. Visualize how pooled effect and I-squared vary across all possible study combinations to identify influential outliers and heterogeneity drivers.
Features
All-subsets analysis
Study highlighting
Batched computation
D3.js scatter
R code (metafor::gosh)
Methods text
CSV import
PDF/PNG export
Generate bubble plots for meta-regression visualization (Thompson & Higgins, 2002). Plot study effect sizes against moderator values with precision-weighted bubbles, WLS regression line, and 95% CI band.
Features
WLS regression
Precision-weighted bubbles
CI band
Custom covariate label
R code (metafor::regplot)
Methods text
CSV import
PDF/PNG export
Convert between effect size types: Cohen's d to Odds Ratio, d to correlation r, OR to r, RR to OR, d to eta-squared, and more. Based on Borenstein et al. (2009) and Chinn (2000) formulas with SE propagation.
Features
10 conversion directions
SE propagation
Batch mode
CI construction
R code
Methods text
Pipeline send
CSV import
Perform Trial Sequential Analysis with alpha-spending monitoring boundaries (O'Brien-Fleming), Required Information Size calculation, cumulative Z-curve, and futility boundaries. Based on Wetterslev et al. (2008).
Features
O'Brien-Fleming boundaries
Required Information Size
Cumulative Z-curve
Futility boundaries
Binary + continuous
D3.js TSA diagram
R code
PDF/PNG export
Build network meta-analysis diagrams, validate network geometry, generate league tables, and format data for R netmeta, WinBUGS, and STATA. Force-directed D3.js network visualization with connectivity checking.
Features
Force-directed network diagram
League table
Connectivity check
R netmeta format
WinBUGS format
STATA format
D3.js interactive
Methods text
Pool dichotomous data using Mantel-Haenszel fixed-effect method (OR, RR, RD) or Peto Odds Ratio for rare events. Direct 2x2 table input with zero-cell correction, forest plot, and heterogeneity assessment.
Features
MH-OR / MH-RR / MH-RD
Peto OR for rare events
Zero-cell correction
D3 forest plot
Heterogeneity stats
R code (rma.mh/rma.peto)
Methods text
CSV import
Interactive Fagan nomogram for diagnostic test interpretation. Enter pre-test probability with sensitivity and specificity, direct likelihood ratios, or a 2x2 contingency table. Visualize the classic three-scale nomogram showing post-test probabilities for positive and negative test results.
Features
D3.js three-scale nomogram
Sensitivity/Specificity input
Direct LR+/LR- input
2x2 table input
Post-test probability
R code generation
Methods text
PNG/PDF export
Generate summary receiver operating characteristic curves for diagnostic test accuracy meta-analysis. Enter sensitivity and specificity pairs from multiple studies, fit the Moses-Littenberg model, and plot study points as weighted bubbles with the fitted SROC curve, AUC, and Q* index.
Features
D3.js ROC space
Weighted study bubbles
Moses-Littenberg SROC curve
AUC calculation
Q* index
Sens/Spec or 2x2 input
R code (mada)
Methods text
PNG/PDF export
CSV import
Assess publication bias using Vevea-Hedges weight-function selection models. Compare unadjusted random-effects estimates against selection-adjusted estimates across moderate and severe one-tailed and two-tailed weight patterns. Sensitivity analysis with D3.js visualizations.
Features
Step-function weights
Moderate/severe selection
One-tailed/two-tailed
Comparison forest plot
Weight function chart
R code (weightr)
Methods text
PNG/PDF export
CSV import
Pipeline import
Visualize the estimated distribution of true effect sizes in a random-effects meta-analysis. Plot the Normal(mu, tau-squared) density curve, prediction intervals, study effect marks, histogram overlay, and compute the probability that the true effect exceeds any threshold. DerSimonian-Laird estimation with R code and methods paragraph export.
Features
Normal density curve
Prediction interval
Threshold probability
Histogram overlay
Tau/I²/Q statistics
Configurable CI level
R code (metafor + dnorm)
Methods paragraph
PNG/PDF export
CSV import
Pipeline import