| name | bias-detection |
| description | Assess systematic biases in the evidence body — publication bias, reporting bias, and selective outcome reporting. Budget: 40 studies, 40 effect sizes, 40 web searches. |
Bias Detection Strategy
Design a protocol to systematically assess biases that threaten the validity of meta-analytic conclusions.
Purpose
Bias in the evidence body (publication bias, outcome reporting bias, citation bias, time-lag bias, language bias) can invalidate pooled estimates. This strategy designs the complete bias detection and adjustment protocol — funnel plots, statistical tests, sensitivity analyses, and GRADE certainty downgrading.
Budget
| Resource | Floor | Target |
|---|
| Studies identified | 28 | 40 |
| Effect sizes extracted | 28 | 40 |
| Web searches | 28 | 40 |
| Bias domains assessed | 5 | 8 |
| Quality assessments | 20 | 40 |
Budget gate: cannot exit until 80% of floor met.
State Ledger
<HARD-GATE>
| Metric | Current | Floor | Target | Status |
|--------|---------|-------|--------|--------|
| Studies found | 0 | 28 | 40 | BLOCKED |
| Effect sizes planned | 0 | 28 | 40 | BLOCKED |
| Web searches done | 0 | 28 | 40 | BLOCKED |
| Bias domains assessed | 0 | 5 | 8 | BLOCKED |
| Quality assessed | 0 | 20 | 40 | BLOCKED |
</HARD-GATE>
Available Tactics
| Tactic | When to Use |
|---|
| effect-size-extraction | Extract effect sizes with precision (SE, CI) |
| quality-assessment-protocol | Full RoB2 assessment per study |
| evidence-synthesis-planning | Plan bias-adjusted models |
Available SOPs
| SOP | When to Use |
|---|
| pico-formulation | Frame the evidence assessment question |
| inclusion-criteria-design | Include grey literature, preprints |
| effect-size-planning | Ensure precision metrics extracted |
| data-extraction-form | Template capturing reporting completeness |
| risk-of-bias-assessment | Per-study RoB (core of this strategy) |
| publication-bias-assessment | Core SOP — funnel plots, statistical tests |
| sensitivity-analysis-design | Trim-and-fill, selection models |
| heterogeneity-source-analysis | Bias as heterogeneity driver |
| meta-analysis-synthesis | Final bias assessment protocol |
Execution Guidance
- Frame — Run
pico-formulation for the evidence reliability question
- Scope — Run
inclusion-criteria-design maximizing source diversity (grey lit, preprints, registries)
- Search — Search for published AND unpublished studies, trial registries
- Extract — Use
effect-size-extraction with precision metrics (SE, CI, N)
- Assess — Use
quality-assessment-protocol for comprehensive RoB2
- Detect — Run
publication-bias-assessment for statistical detection plan
- Investigate — Run
heterogeneity-source-analysis for bias-driven heterogeneity
- Adjust — Run
sensitivity-analysis-design for bias-adjustment methods
- Synthesize — Run
meta-analysis-synthesis for final protocol
Web searches target: trial registries, grey literature databases, dissertation repositories, conference abstracts.
Output Format
protocol:
question: [Is the evidence body for X biased?]
bias_domains:
publication_bias:
visual: [funnel plot, contour-enhanced funnel]
statistical: [Egger's test, Begg's test, Peters' test]
adjustment: [trim-and-fill, Copas selection model, PET-PEESE]
outcome_reporting_bias:
detection: [registry-publication comparison]
tool: [ROB-ME, ORBIT]
time_lag_bias:
detection: [time-to-publication analysis]
citation_bias:
detection: [citation network analysis]
language_bias:
mitigation: [multi-language search strategy]
small_study_effects:
detection: [funnel asymmetry, regression tests]
adjustment: [limit meta-analysis]
grey_literature_search: [databases, registries, contacts]
grade_assessment:
domain: publication_bias
downgrading_criteria: [when to downgrade certainty]
sensitivity_plan: [selection model, 3PSM, p-curve, z-curve]
reporting: PRISMA-2020 + ROB-ME guidelines
