effect-size-extraction

name	effect-size-extraction
description	Systematically extract effect sizes and conditions from papers for meta-analytic synthesis
execution	tactic
dependencies	{"sops":["data-extraction-form","effect-size-planning","risk-of-bias-assessment"]}

Effect Size Extraction Tactic

Systematically extract quantitative effect sizes, study conditions, and precision metrics from included studies.

Stages

Stage 1: Paper Identification

Identify the specific sections of each paper containing quantitative results.

• Locate results tables, figures, and statistical reporting sections
• Identify primary and secondary outcomes
• Flag studies reporting insufficient statistics for effect size calculation

Tools: dare-scholar (paper_content, paper_reading), alphaxiv (answer_pdf_queries)

Stage 2: Data Point Location

For each study, locate the exact data points needed for effect size calculation.

• Sample sizes per group (N treatment, N control)
• Central tendency (means, proportions, hazard ratios)
• Variability (SD, SE, CI, IQR)
• Test statistics (t, F, chi-square, z) when direct data unavailable
• p-values as last resort for back-calculation

SOPs: effect-size-planning (determine what to extract)

Stage 3: Effect Size Calculation Planning

Plan the calculation method for each study based on available data.

• Direct calculation from means + SDs
• Conversion from test statistics (t → d, F → d, r → z)
• Conversion between effect size families (OR ↔ d, r ↔ d)
• Handling of multi-arm studies (shared control correction)
• Cluster-adjusted effect sizes (design effect)

SOPs: effect-size-planning

Stage 4: Condition Recording

Record all study-level conditions and moderator variables.

• Population characteristics (N, demographics, baseline severity)
• Intervention details (dose, duration, delivery mode)
• Comparison conditions (active control, placebo, waitlist)
• Outcome measurement (tool, timing, blinding)
• Study design features (randomization, allocation concealment)

SOPs: data-extraction-form

Stage 5: Quality Annotation

Annotate each extracted effect size with quality indicators.

• Intention-to-treat vs per-protocol
• Handling of missing data (complete case, imputation)
• Outcome measurement reliability
• Potential for selective reporting (registered vs reported)
• Confidence in the extracted value (high/medium/low)

SOPs: risk-of-bias-assessment

Minimum Yield

Per execution of this tactic:

• At least 5 studies processed
• At least 5 effect sizes extracted or calculation planned
• All moderator variables recorded for extracted studies
• Quality annotation complete for all extractions

Output Format

extractions:
  - study_id: [identifier]
    effect_size_type: [SMD/OR/RR/MD/r]
    point_estimate: [value or calculation formula]
    precision: [SE/CI/variance]
    sample_size: [N_treatment, N_control]
    conditions: [moderator variables]
    quality: [high/medium/low confidence]
    notes: [calculation method, assumptions]

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

SOP	When to use
data-extraction-form	Design structured data extraction form for systematic meta-analysis data collection
effect-size-planning	Determine effect size types and calculation methods for meta-analytic synthesis
risk-of-bias-assessment	Assess methodological bias using RoB2, PROBAST, or QUADAS-2 validated tools