Evidence Synthesis Planning Tactic

Plan the complete statistical synthesis approach: effect size standardization, model selection, heterogeneity quantification, sensitivity analyses, and PRISMA-compliant reporting.

Stages

Stage 1: Effect Size Type Determination

Determine the appropriate effect size metric for the synthesis.

| Outcome Type | Effect Size | When | |--------------|-------------|------| | Continuous (same scale) | Mean Difference (MD) | All studies use same measurement | | Continuous (different scales) | Standardized Mean Difference (SMD) | Studies use different instruments | | Binary | Odds Ratio (OR) / Risk Ratio (RR) | Dichotomous outcomes | | Time-to-event | Hazard Ratio (HR) | Survival data | | Correlation | Fisher's z (transformed r) | Association studies | | Count/rate | Incidence Rate Ratio (IRR) | Event rate data |

SOPs: effect-size-planning

Stage 2: Model Selection

Choose between fixed-effect and random-effects models.

• Fixed-effect (Mantel-Haenszel, Inverse Variance): assumes one true effect, studies estimate same parameter
• Random-effects (DerSimonian-Laird, REML, Paule-Mandel, Knapp-Hartung): assumes distribution of true effects
• Decision criteria: clinical/methodological diversity, number of studies, intended inference scope
• Knapp-Hartung adjustment: recommended when k < 20 studies

Decision tree: If studies are clinically homogeneous AND methodologically identical → fixed-effect. Otherwise → random-effects with REML + Knapp-Hartung.

Stage 3: Heterogeneity Strategy

Plan heterogeneity quantification and investigation.

• Quantification: I2 (proportion), tau2 (absolute), H2, prediction interval
• Testing: Cochran's Q (detection), confidence intervals for I2
• Investigation: pre-specified subgroups, meta-regression (if k >= 10)
• Thresholds: I2 interpretation (0-40% low, 30-60% moderate, 50-90% substantial, 75-100% considerable)

SOPs: heterogeneity-source-analysis

Stage 4: Sensitivity Design

Design robustness checks for the primary analysis.

• Leave-one-out analysis (influence of individual studies)
• Influence diagnostics (Cook's distance, DFFITS, hat values)
• Subgroup analyses (pre-specified moderators only)
• Alternative model (fixed vs random comparison)
• Alternative effect size (OR vs RR, SMD vs MD)
• Excluding high risk-of-bias studies

SOPs: sensitivity-analysis-design

Stage 5: Reporting Plan

Design PRISMA-2020 compliant reporting.

• PRISMA flow diagram (identification, screening, eligibility, inclusion)
• Forest plot specifications (study labels, weights, diamonds)
• Summary of findings table (GRADE certainty)
• Heterogeneity reporting (I2, tau2, prediction interval)
• Sensitivity analysis presentation
• Protocol registration (PROSPERO)

Minimum Yield

Per execution of this tactic:

• Effect size type justified and documented
• Model selection with explicit rationale
• Heterogeneity quantification plan complete
• At least 3 sensitivity analyses designed
• PRISMA reporting checklist addressed

Output Format

yaml
synthesis_plan:
  effect_size:
    type: [SMD/OR/RR/MD/HR/z]
    justification: [why this metric]
    conversions_needed: [any transformations]
  model:
    type: [fixed-effect/random-effects]
    estimator: [IV/MH/REML/DL/PM]
    adjustment: [Knapp-Hartung/none]
    justification: [rationale]
  heterogeneity:
    metrics: [I2, tau2, Q, prediction interval]
    investigation:
      subgroups: [list of categorical moderators]
      meta_regression: [list of continuous moderators]
      minimum_k_per_subgroup: [threshold]
  sensitivity:
    - leave_one_out
    - influence_diagnostics
    - alternative_model
    - rob_exclusion
    - [additional pre-specified]
  reporting:
    standard: PRISMA-2020
    registration: [PROSPERO ID or plan]
    grade_domains: [risk_of_bias, inconsistency, indirectness, imprecision, publication_bias]

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Available SOPs

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

| SOP | When to use | | --- | --- | | 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) | | sensitivity-analysis-design | Design leave-one-out, influence diagnostics, subgroup analyses, and robustness checks |

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