heterogeneity-investigation

Research

Explain why different studies reach different conclusions — heterogeneity investigation protocol. Budget: 30 studies, 30 effect sizes, 50 web searches.

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Heterogeneity Investigation Strategy

Design a protocol to investigate and explain between-study heterogeneity — why studies of the same question reach different conclusions.

Purpose

When a meta-analysis reveals substantial heterogeneity (I2 > 50%, significant Q-test, large tau2), this strategy designs the investigation protocol: subgroup analyses, meta-regression, moderator identification, and outlier diagnostics. Produces the investigation plan, not the computation.

Budget

Resource	Floor	Target
Studies identified	20	30
Effect sizes extracted	20	30
Web searches	35	50
Moderator candidates	5	10+
Quality assessments	15	30

Budget gate: cannot exit until 80% of floor met.

State Ledger

<HARD-GATE>
| Metric | Current | Floor | Target | Status |
|--------|---------|-------|--------|--------|
| Studies found | 0 | 20 | 30 | BLOCKED |
| Effect sizes planned | 0 | 20 | 30 | BLOCKED |
| Web searches done | 0 | 35 | 50 | BLOCKED |
| Moderators identified | 0 | 5 | 10+ | BLOCKED |
| Quality assessed | 0 | 15 | 30 | BLOCKED |
</HARD-GATE>

Available Tactics

Tactic	When to Use
effect-size-extraction	Extract effect sizes with full study characteristics
quality-assessment-protocol	Assess whether quality explains heterogeneity
evidence-synthesis-planning	Plan subgroup and meta-regression models

Available SOPs

SOP	When to Use
pico-formulation	Frame the heterogeneity question
inclusion-criteria-design	Broad inclusion to capture variation
effect-size-planning	Standardize for comparability
data-extraction-form	Rich moderator variable extraction
risk-of-bias-assessment	RoB as potential moderator
heterogeneity-source-analysis	Core SOP — classify heterogeneity sources
sensitivity-analysis-design	Outlier removal, influence diagnostics
publication-bias-assessment	Bias as heterogeneity source
meta-analysis-synthesis	Final investigation protocol

Execution Guidance

Frame — Run pico-formulation emphasizing variation in P/I/C/O
Scope — Run inclusion-criteria-design with broad criteria (capture variation)
Search — Systematic search + web research on known moderators
Extract — Use effect-size-extraction with rich study-level covariates
Hypothesize — Run heterogeneity-source-analysis to generate moderator hypotheses
Assess — Use quality-assessment-protocol (RoB as moderator)
Plan — Use evidence-synthesis-planning for subgroup + meta-regression
Synthesize — Run meta-analysis-synthesis for investigation protocol

Web searches focus on domain knowledge about why results might differ (methodological, clinical, statistical heterogeneity).

Output Format

protocol:
  question: [Why do studies of X reach different conclusions?]
  heterogeneity_metrics: [I2, tau2, Q-test, prediction interval]
  moderator_candidates:
    clinical: [population, intervention details, outcome timing]
    methodological: [study design, RoB, measurement tools]
    statistical: [effect size type, analysis method, sample size]
  investigation_plan:
    subgroup_analyses: [categorical moderators]
    meta_regression: [continuous moderators]
    outlier_diagnostics: [influence analysis, Baujat plot]
    sensitivity: [leave-one-out, cumulative by quality]
  a_priori_hypotheses: [pre-specified moderator hypotheses]
  multiple_testing: [correction strategy]
  reporting: PRISMA-2020 + heterogeneity reporting guidelines

Available Tactics

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

Tactic	When to use
effect-size-extraction	Systematically extract effect sizes and conditions from papers for meta-analytic synthesis
evidence-synthesis-planning	Plan the statistical synthesis approach — model selection, heterogeneity strategy, and reporting
quality-assessment-protocol	Methodological quality and bias risk assessment of included studies using validated tools

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
heterogeneity-source-analysis	Identify and classify sources of between-study heterogeneity (clinical, methodological, statistical)
inclusion-criteria-design	Define inclusion/exclusion criteria for systematic study selection in meta-analysis
meta-analysis-synthesis	Produce final meta-analysis protocol document assembling all planning outputs into PRISMA-compliant protocol
pico-formulation	Construct PICO/PECO framework for the meta-analysis research question
publication-bias-assessment	Plan funnel plots, Egger's test, trim-and-fill, p-curve, and selection model analyses for publication bias
risk-of-bias-assessment	Assess methodological bias using RoB2, PROBAST, or QUADAS-2 validated tools
sensitivity-analysis-design	Design leave-one-out, influence diagnostics, subgroup analyses, and robustness checks