Meta-analysis, complete
A comprehensive Python toolkit for systematic reviews and meta-analysis. Fixed and random effects, Bayesian models, publication bias, diagnostics, forest plots, and automated reporting โ all in one package.
pip install Mikoshi Meta-Analysis
*Beyond standard scientific Python (numpy, scipy, pandas, matplotlib, statsmodels)
Meta-analysis tools are fragmented
Researchers typically juggle multiple tools: R's metafor for random effects, separate scripts for bias detection, manual forest plot generation, and Word documents for reporting. There's no single Python package that does it all.
Mikoshi Meta-Analysis is that package. Everything from effect size estimation to PRISMA flow diagrams to Bayesian sensitivity analysis โ in one pip install.
Everything a systematic reviewer needs
Fixed & Random Effects
DerSimonian-Laird, REML, ML, and fixed-effect models. Heterogeneity statistics (Iยฒ, Q, ฯยฒ), prediction intervals, and confidence intervals.
Meta-Regression
Weighted least squares and mixed-effects meta-regression. Hierarchical models with random slopes. Bubble plots for moderator analysis.
Bayesian Models
Bayesian random-effects meta-analysis using analytical approximation (no PyMC dependency). Prior sensitivity analysis, Bayes factors, posterior forest plots.
Publication Bias
Egger's test, trim-and-fill, PET-PEESE, selection models (Vevea & Hedges), p-curve analysis, z-curve analysis, and Rosenthal's fail-safe N.
Diagnostics
Leave-one-out influence analysis, Baujat plots, GOSH plots, Cook's distance, DFBETAS, hat values, studentised residuals, and outlier detection.
Plots
Forest plots, funnel plots (with contour), bubble plots, radial (Galbraith) plots, L'Abbรฉ plots, cumulative forest plots, network geometry plots. All publication-quality matplotlib.
Cumulative Meta-Analysis
Add studies one at a time sorted by year, effect size, precision, or sample size. Visualise how the pooled estimate evolves as evidence accumulates.
Robust Variance
Cluster-robust variance estimation for dependent effects. Sandwich standard errors for meta-regression. Permutation-based p-values.
Automated Reporting
Generate full HTML or LaTeX reports with embedded plots, summary tables, PRISMA flow diagrams, and APA-formatted citations. One function call.
GRADE Quality
Certainty of evidence assessment following GRADE methodology. Quality-stratified effects analysis and sensitivity analysis by risk of bias.
Network Meta-Analysis
Basic network (multiple-treatments) meta-analysis with league tables and network geometry plots.
Interactive Dashboard
Built-in Plotly Dash dashboard. Upload data, run analyses, generate plots, and export reports โ all from a web browser. No code required.
Modular by design
Each module is independent. Use the full suite or import just what you need.
Three ways to use Mikoshi Meta-Analysis
1. Python API
from Mikoshi Meta-Analysis import core, plotting, diagnostics, bias_extra # Load your data effects = [0.5, 0.8, 0.3, 1.1, 0.6] variances = [0.04, 0.09, 0.02, 0.16, 0.05] labels = ["Study A", "Study B", "Study C", "Study D", "Study E"] # Random-effects meta-analysis result = core.random_effects(effects, variances) # โ {'effect': 0.58, 'ci_lower': 0.31, 'ci_upper': 0.85, 'tau2': 0.04, 'I2': 62.3} # Forest plot plotting.forest_plot(effects, variances, labels, save="forest.png") # Leave-one-out sensitivity loo = diagnostics.leave_one_out(effects, variances, labels) # Trim-and-fill adjusted = bias_extra.trim_and_fill(effects, variances)
2. Command Line
# Run random-effects meta-analysis pymetasuite run data.csv --effect effect_size --var variance # Generate forest plot pymetasuite forest data.csv --effect effect_size --var variance # Funnel plot with trim-and-fill pymetasuite funnel data.csv --effect effect_size --var variance # Bayesian meta-analysis pymetasuite bayesian data.csv --effect effect_size --var variance # Launch interactive dashboard pymetasuite dashboard --port 8050
3. Interactive Dashboard
Run pymetasuite dashboard and open your browser. Upload a CSV, select columns, choose analysis type, and generate publication-quality plots โ no code needed. Export results as HTML or PDF reports.
Install in 10 seconds
# From GitHub pip install git+https://github.com/DarrenEdwards111/Mikoshi Meta-Analysis.git # Or clone and install locally git clone https://github.com/DarrenEdwards111/Mikoshi Meta-Analysis.git cd Mikoshi Meta-Analysis pip install .
Built by researchers, for researchers
Systematic Reviewers
Everything you need for a Cochrane-style review. PRISMA flow diagrams, GRADE certainty assessment, forest plots, and automated reporting in one toolkit.
Clinical Researchers
Random-effects models, subgroup analysis, meta-regression, and publication bias detection. Supports both continuous and binary outcomes.
Graduate Students
Clear API, comprehensive documentation, and an interactive dashboard. Learn meta-analysis methodology by doing, not just reading about it.
Cite Mikoshi Meta-Analysis
Edwards, D. (2025). Mikoshi Meta-Analysis: A comprehensive Python meta-analysis toolkit
(Version 0.9.0) [Computer software].
GitHub. https://github.com/DarrenEdwards111/Mikoshi Meta-Analysis