evidence-synthesis-planning

Research

Plan the statistical synthesis approach — model selection, heterogeneity strategy, and reporting

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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

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]

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