prismalab-meta-analysis

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

# 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)

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