How to use GRADE (Grading of Recommendations Assessment, Development, and Evaluation)

How to Use the GRADE System

Grading of Recommendations Assessment, Development, and Evaluation (GRADE) is a transparent framework for rating the quality of evidence and strength of recommendations in systematic reviews and clinical guidelines. Below is a step-by-step guide.


1. Understand Key Concepts

  • Quality of Evidence (Certainty):
    • High (⊕⊕⊕⊕): Further research is unlikely to change confidence.
    • Moderate (⊕⊕⊕◯): Further research may have an impact.
    • Low (⊕⊕◯◯): Further research is likely to change confidence.
    • Very Low (⊕◯◯◯): Evidence is very uncertain.
  • Strength of Recommendation:
    • Strong (“We recommend…”): Benefits clearly outweigh risks.
    • Weak/Conditional (“We suggest…”): Trade-offs are uncertain.

2. Step-by-Step GRADE Process

Step 1: Start with a Study Design Baseline

  • RCTs start as High quality.
  • Observational studies (e.g., cohort, case-control) start as Low quality.

Step 2: Assess Reasons to Upgrade or Downgrade

Factors That Can Downgrade Evidence

Factor Explanation Example
Risk of Bias Serious flaws in study design (e.g., lack of blinding, high dropout). Downgrade by 1-2 levels if most studies have high RoB (Cochrane RoB 2).
Inconsistency Unexplained heterogeneity (e.g., I² > 50%). Downgrade if results vary widely across studies.
Indirectness Population, intervention, or outcome differs from research question. Downgrade if study uses surrogate outcomes instead of patient-important ones.
Imprecision Wide confidence intervals or small sample size. Downgrade if 95% CI crosses “no effect” (RR=1).
Publication Bias Suspected missing studies (e.g., funnel plot asymmetry). Downgrade if small-study effects are likely.

Factors That Can Upgrade Evidence (for Observational Studies)

Factor Explanation Example
Large Effect Strong association (e.g., RR > 2 or < 0.5). Upgrade if treatment reduces mortality by 50%.
Dose-Response Evidence of a gradient (e.g., higher dose → better outcome). Upgrade if higher drug doses show better efficacy.
Plausible Confounding All residual confounding would reduce effect. Upgrade if smoking cessation reduces lung cancer despite confounding.

Step 3: Determine Final Quality of Evidence

  • Apply downgrades/upgrades to reach a final rating (High → Moderate → Low → Very Low).
  • Example:
    • Baseline: RCT (High)
    • Downgrades: Risk of bias (–1), Imprecision (–1)
    • FinalModerate (⊕⊕⊕◯)

Step 4: Formulate Recommendations

  • Strong recommendation: High-quality evidence with clear benefits.
    • Example: “We recommend statins for CVD prevention (⊕⊕⊕⊕).”
  • Weak recommendation: Lower-quality evidence or uncertain trade-offs.
    • Example: “We suggest omega-3 supplements for mild depression (⊕⊕◯◯).”

3. Practical Example

Scenario: Assessing a meta-analysis of RCTs on aspirin for stroke prevention.

Factor Judgment Adjustment
Study Design RCTs Start at High (⊕⊕⊕⊕)
Risk of Bias Some studies had unclear blinding Downgrade 1 (–⊕) → Moderate
Inconsistency I² = 60% (substantial heterogeneity) Downgrade 1 (–⊕) → Low
Indirectness All studies used similar populations No change
Imprecision 95% CI for benefit: 0.7–1.1 (crosses 1) Downgrade 1 (–⊕) → Very Low
Publication Bias Funnel plot symmetric No change
Final Rating Very Low (⊕◯◯◯) “Evidence is highly uncertain.”

Recommendation:

  • Weak: “We suggest considering aspirin for stroke prevention in high-risk patients (⊕◯◯◯), but shared decision-making is needed due to uncertain evidence.”

4. When to Use GRADE

✔ Systematic reviews (e.g., Cochrane reviews).
✔ Clinical practice guidelines (e.g., WHO, AHA).
✔ Health technology assessments (HTA).


5. Comparison with Other Tools

Tool Purpose Output
GRADE Rates evidence certainty & recommendations High/Moderate/Low/Very Low
Cochrane RoB 2 Assesses RCT bias Low/Some/High risk
SIGN Rates study quality (++/+/–) For guideline development

Final Tips

  • Use GRADEpro (gradepro.org) for creating summary tables.
  • Clearly document downgrading/upgrading decisions.
  • Involve multiple reviewers to reduce bias.

Would you like a GRADE evidence profile template for your review?