prismalab-meta-analysis
Perform meta-analyses using Azure Databricks for computation and Power BI for visualization. Supports fixed/random effects, subgroup analysis, and publication bias assessment.
ソース情報
- リポジトリ
- Insightpulseai/odoo
- ソースの最終更新活動
- 2026年4月18日 08:34
- 検出された SKILL.md の言語
- 英語
- スター
- 6
- フォーク
- 2
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SKILL.md
ソースの指示 · 読み取り専用プレビュー- name
- prismalab-meta-analysis
- description
- Perform meta-analyses using Azure Databricks for computation and Power BI for visualization. Supports fixed/random effects, subgroup analysis, and publication bias assessment.
# PrismaLab Meta-Analysis Skill
## When to Use
- User needs to compute pooled effect sizes
- User asks about heterogeneity assessment (I-squared, Q-statistic)
- User needs forest plots, funnel plots, or sensitivity analyses
- User asks about publication bias (Egger's test, trim-and-fill)
## Azure Services Used
- **Databricks** (`dbw-ipai-dev`): Python computation (statsmodels, scipy, meta-analysis libraries)
- **Power BI**: Publication-ready visualizations
- **Azure AI Foundry**: Interpretation and narrative synthesis
## Computation Approach
### Effect Size Calculation
```python
# Run in Databricks notebook
import numpy as np
from scipy import stats
def compute_effect_size(study_data, metric="SMD"):
"""
Compute standardized mean difference (Cohen's d) or odds ratio.
study_data: list of dicts with keys:
SMD: n1, mean1, sd1, n2, mean2, sd2
OR: events1, total1, events2, total2
"""
if metric == "SMD":
d = (study_data["mean1"] - study_data["mean2"]) / pooled_sd
se = np.sqrt((n1 + n2) / (n1 * n2) + d**2 / (2 * (n1 + n2)))
elif metric == "OR":
OR = (a * d) / (b * c)
se = np.sqrt(1/a + 1/b + 1/c + 1/d)
return {"effect": d_or_OR, "se": se, "ci_lower": ..., "ci_upper": ...}
```
### Pooled Effect (Random Effects)
```python
def random_effects_meta(effects, variances):
"""DerSimonian-Laird random effects model."""
weights_fixed = 1.0 / variances
Q = np.sum(weights_fixed * (effects - weighted_mean_fixed)**2)
tau2 = max(0, (Q - (k - 1)) / (sum_w - sum_w2 / sum_w))
weights_random = 1.0 / (variances + tau2)
pooled = np.sum(weights_random * effects) / np.sum(weights_random)
se_pooled = np.sqrt(1.0 / np.sum(weights_random))
I2 = max(0, (Q - (k - 1)) / Q * 100)
return {"pooled_effect": pooled, "se": se_pooled, "I2": I2, "tau2": tau2, "Q": Q}
```
### Heterogeneity Interpretation
| I-squared | Interpretation |
|-----------|---------------|
| 0-25% | Low heterogeneity |
| 25-50% | Moderate heterogeneity |
| 50-75% | Substantial heterogeneity |
| 75-100% | Considerable heterogeneity |
## Visualization (Power BI or HTML/SVG)
### Forest Plot
Generate SVG forest plot with:
- Study labels with year
- Effect size point + CI whiskers
- Weight proportional to square size
- Diamond for pooled estimate
- Vertical line at null effect
### Funnel Plot
Generate for publication bias assessment:
- X-axis: effect size
- Y-axis: standard error (inverted)
- Pseudo-95% CI lines
- Egger's regression line if significant
## Subgroup and Sensitivity Analyses
- **Subgroup**: stratify by study characteristics (design, population, intervention type)
- **Leave-one-out**: recalculate excluding each study
- **Cumulative**: add studies chronologically
- **Influence**: Cook's distance equivalent for meta-analysis
## Quality Standards
- Report per PRISMA 2020 synthesis methods
- Use GRADE framework for certainty of evidence
- Report prediction intervals alongside confidence intervals
- Always assess publication bias for >=10 studies
- Declare statistical software and version
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