https://github.com/mahmood726-cyber/ubcma
UBCMA: Unified Bias-Calibrated Meta-Analysis via Joint Heterogeneity-Selection Modeling
https://github.com/mahmood726-cyber/ubcma
bayesian e156 evidence-synthesis mcmc meta-analysis pymc
Last synced: 1 day ago
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UBCMA: Unified Bias-Calibrated Meta-Analysis via Joint Heterogeneity-Selection Modeling
- Host: GitHub
- URL: https://github.com/mahmood726-cyber/ubcma
- Owner: mahmood726-cyber
- License: mit
- Created: 2026-03-27T09:45:44.000Z (4 months ago)
- Default Branch: master
- Last Pushed: 2026-07-05T10:44:32.000Z (20 days ago)
- Last Synced: 2026-07-05T12:14:01.801Z (20 days ago)
- Topics: bayesian, e156, evidence-synthesis, mcmc, meta-analysis, pymc
- Language: Python
- Size: 43.4 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
- Codeowners: .github/CODEOWNERS
- Security: SECURITY.md
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README
# UBCMA: Unified Bias-Calibrated Meta-Analysis
[](https://github.com/mahmood726-cyber/ubcma/actions/workflows/ci.yml) [](https://github.com/mahmood726-cyber/ubcma/actions/workflows/codeql.yml) [](LICENSE) [](https://www.python.org/)
A Python framework for meta-analysis that jointly models heterogeneity, publication selection bias, and study quality-dependent bias. UBCMA uses a mixture normal likelihood with a smooth selection function, optimized via multi-start L-BFGS-B with optional Bayesian (PyMC) inference.
## Install
```bash
pip install . # frequentist only
pip install ".[bayes]" # adds PyMC for Bayesian inference
```
## Quick Start
```python
from ubcma import MetaAnalysisDataset, UBCMAFit
data = MetaAnalysisDataset.from_csv("data.csv", study_id_col="study_id")
result = UBCMAFit(n_restarts=20).fit(data)
print(f"mu = {result.mu:.3f}, tau = {result.tau1:.3f}")
print(result.study_table())
```
## CLI
```bash
# Fit a model
ubcma fit data.csv --quality rob_selection,rob_measurement --profile-ci
# Run diagnostics (AIC/BIC, LOO influence, residuals)
ubcma diagnose data.csv
# Bayesian fit with prior sensitivity
ubcma fit-bayes data.csv --chains 4 --prior-sensitivity
# Simulation study (pilot tier, ~30 min)
ubcma study --tier pilot --replicates 50
```
## Key Features
- **Multi-start optimization** with Latin hypercube sampling
- **Profile likelihood CIs** (exact, no HKSJ correction needed)
- **BCa bootstrap CIs** with jackknife acceleration
- **Bayesian backend** via PyMC (NUTS, prior sensitivity analysis)
- **8 comparator methods**: DerSimonian-Laird, DL-HKSJ, REML, REML-HKSJ, trim-and-fill, PET-PEESE, Copas, quality-effects
- **HKSJ correction** for DL and REML comparators
- **Diagnostics**: AIC/BIC for 5 model variants, LOO influence, Cook's D
- **Three-tier simulation study** framework (pilot/focused/full factorial)
## Model
For study *i*:
```
y_i ~ f_selected(y_i | theta_i, s_i, q_i, R_i = 1)
theta_i = mu + x_i' beta + z_i' delta + h_i
b_i = q_i' lambda
h_i ~ w N(0, tau_1^2) + (1 - w) N(0, tau_2^2)
y_i | theta_i, b_i ~ N(theta_i + b_i, s_i^2)
P(R_i = 1 | y_i, s_i, q_i) = logistic(g0 + g1 sig_i + g2 prec_i + g3 dir_i + g4 qbar_i)
```
Where `y_i` is the observed effect, `s_i` the standard error, `q_i` quality/bias indicators (0-1 scale), `x_i` moderators, `z_i` design indicators. The two-component mixture captures heterogeneity. The selection function models publication bias via significance, precision, and direction terms.
The target estimand `mu` is the expected effect for a future study at the reference design, mean moderator profile, and low quality-shift settings.
## Data Schema
Minimum CSV columns: `yi` (effect), `sei` (standard error).
Optional: `rob_*` / `bias_*` quality columns, `quality_score`, moderators (e.g. `dose`, `followup_months`), `design`, `study_id`.
## Files
- `src/ubcma/model.py` — core likelihood and optimizer
- `src/ubcma/data.py` — CSV ingestion and design matrix construction
- `src/ubcma/comparators.py` — REML, trim-and-fill, PET-PEESE, Copas, quality-effects, HKSJ
- `src/ubcma/inference.py` — bootstrap CIs, profile likelihood
- `src/ubcma/diagnostics.py` — AIC/BIC, LOO influence, Cook's D, residuals
- `src/ubcma/simulation.py` — synthetic data generation
- `src/ubcma/simulation_study.py` — tier-based simulation framework
- `src/ubcma/bayesian.py` — PyMC NUTS backend
- `src/ubcma/cli.py` — command-line entry point
- `examples/` — quickstart script and validation datasets
## Citation
```bibtex
@software{ubcma2026,
title = {UBCMA: Unified Bias-Calibrated Meta-Analysis},
year = {2026},
url = {https://github.com/TODO/ubcma}
}
```
## References
- Bohnning D. Meta-analysis: a unifying meta-likelihood approach. Methods Inf Med. 2005. PMID 15778804.
- Verde PE. A bias-corrected meta-analysis model. Biom J. 2021. PMID 32996196.
- Bartos F et al. Robust Bayesian meta-analysis. Psychol Methods. PMID 35588075.
- McShane BB et al. Adjusting for publication bias in meta-analysis. Perspect Psychol Sci. 2016. PMID 27694467.
## License
MIT