Grading evidence and recommendations The GRADE initiative

1. Grading evidence and recommendationsThe GRADE initiative Holger Schünemann, MD, PhD Associate Professor Italian National Cancer Institute Regina Elena, Rome

2. Professional good intentions and plausible theories are insufficient for selecting policies and practices for protecting, promoting and restoring health. Iain Chalmers

3. How can we judge the extent of our confidence that adherence to a recommendation will do more good than harm?

4. GRADE Grades of Recommendation Assessment, Development and Evaluation

5. What do you know about GRADE? • Have prepared a guideline • Read the BMJ paper • Have prepared a systematic review and a summary of findings table • Have attended a GRADE meeting, workshop or talk

6. About GRADE* • Began as informal working group in 2000 • Researchers/guideline developers with interest in methodology • Aim: to develop a common system for grading the quality of evidence and the strength of recommendations that is sensible and to explore the range of interventions and contexts for which it might be useful* • 13 meetings (~10 – 35 attendants) • Evaluation of existing systems and reliability* • Workshops at Cochrane Colloquia, WHO, GIN and various conferences since 2000 *Grade Working Group. CMAJ 2003, BMJ 2004, BMC 2004, BMC 2005

7. David Atkins, chief medical officera Dana Best, assistant professorb Martin Eccles, professord Francoise Cluzeau, lecturerx Yngve Falck-Ytter, associate directore Signe Flottorp, researcherf Gordon H Guyatt, professorg Robin T Harbour, quality and information director h Margaret C Haugh, methodologisti David Henry, professorj Suzanne Hill, senior lecturerj Roman Jaeschke, clinical professork Regina Kunx, Associate Professor Gillian Leng, guidelines programme directorl Alessandro Liberati, professorm Nicola Magrini, directorn James Mason, professord Philippa Middleton, honorary research fellowo Jacek Mrukowicz, executive directorp Dianne O’Connell, senior epidemiologistq Andrew D Oxman, directorf Bob Phillips, associate fellowr Holger J Schünemann, associate professorg,s Tessa Tan-Torres Edejer, medical officert Jane Thomas, Lecturer, UK Helena Varonen, associate editoru Gunn E Vist, researcherf John W Williams Jr, professorv Stephanie Zaza, project directorw a) Agency for Healthcare Research and Quality, USA b) Children's National Medical Center, USA c) Centers for Disease Control and Prevention, USA d) University of Newcastle upon Tyne, UK e) German Cochrane Centre, Germany f) Norwegian Centre for Health Services, Norway g) McMaster University, Canada h) Scottish Intercollegiate Guidelines Network, UK i) Fédération Nationale des Centres de Lutte Contre le Cancer, France j) University of Newcastle, Australia k) McMaster University, Canada l) National Institute for Clinical Excellence, UK m) Università di Modena e Reggio Emilia, Italy n) Centro per la Valutazione della Efficacia della Assistenza Sanitaria, Italy o) Australasian Cochrane Centre, Australia p) Polish Institute for Evidence Based Medicine, Poland q) The Cancer Council, Australia r) Centre for Evidence-based Medicine, UK s) National Cancer Institute, Italy t) World Health Organisation, Switzerland u) Finnish Medical Society Duodecim, Finland v) Duke University Medical Center, USA w) Centers for Disease Control and Prevention, USA x) University of London, UK GRADE Working Group

8. What do users want from guidelines? • users looking for different things • just tell me what to do (recommendation) • what to do, and on strong or weak grounds • recommendation and grade • recommend, grade, evidence summary, values • systematic review, value statement • evidence from individual studies

9. When to make a recommendation? • never • patient values differ • just lay out benefits and risks • when evidence strong enough • when very weak, too uncertain • clinicians need guidance • intense study demands decision

10. Why bother about grading? • People draw conclusions about the • quality of evidence • strength of recommendations • Systematic and explicit approaches can help • protect against errors • resolve disagreements • facilitate critical appraisal • communicate information • However, there is wide variation in currently used approaches

11. Evidence Recommendation II-2 B C+ 1 Strong Strongly recommended Organization USPSTF ACCP GCPS Who is confused?

12. EvidenceRecommendation B Class I C+ 1 IV C Organization AHA ACCP SIGN Still not confused? Recommendation for use of oral anticoagulation in patients with atrial fibrillation and rheumatic mitral valve disease

13. Grading System • current profusion: can there be consensus? • trade-off benefits and risks • do it (or don’t do it) • probably do it (or probably don’t do it) • quality of underlying evidence • high quality (well done RCT) • intermediate (quasi-RCT) • low (well done observational) • very low (anything else)

14. Moving down • poor RCT design, implementation • randomization, concealment, follow-up • inconsistency • indirect • patients, interventions, outcomes • A vs B, but have A to C, B to C • reporting bias

15. Moving up • magnitude of effect • dose-response • biases favor control

16. Guidelines development process

17. Guidelines development process

18. Example ACCP • First ACCP guidelines in 1986 (J. Hirsh; J. Dalen) • Initially aimed at consensus • Methodologists involved since beginning • Now formally convening every 2 to 3 years • Seventh conference held in 2003; > 200.000 copies published in Chest • 87 panel members, 22 chapters • Across subspecialties • 565 recommendations, 230 new • Evidence Based Recommendations • Next conference in 2006

19. What makes guidelines evidence based (in 2005)? • Evidence – recommendation: transparent link • Explicit inclusion criteria • Comprehensive search • Standardized consideration of study quality • Conduct/use meta-analysis • Evaluate overall quality of evidence • Grade recommendations • Acknowledge values and preferences Schünemann et al. Chest 2004

20. Judgements about the overall quality of evidence • Most systems not explicit • Options: • strongest outcome • primary outcome • benefits • weighted • separate grades for benefits and harms • no overall grade • weakest outcome • Based on lowest of all the critical outcomes • Beyond the scope of a systematic review

21. Quality of evidence “The extent to which one can be confident that an estimate of effect or association is correct.” It depends on the: • study design (e.g. RCT, cohort study) • study quality/limitations (protection against bias; e.g. concealment of allocation, blinding, follow-up) • consistency of results • directness of the evidence including the • populations (those of interest versus similar; for example, older, sicker or more co-morbidity) • interventions (those of interest versus similar; for example, drugs within the same class) • outcomes (important versus surrogate outcomes) • comparison (A - C versus A - B & C - B)

22. Quality of evidence The quality of the evidence (i.e. our confidence) may also be REDUCEDwhen there is: • Sparse or imprecise data • Reporting bias The quality of the evidence (i.e. our confidence) may be INCREASEDwhen there is: • A strong association • A dose response relationship • All plausible confounders would have reduced the observed effect • All plausible biases would have increased the observed lack of effect

23. Quality assessment criteria

24. Categories of quality • High: Further research is very unlikely to change our confidence in the estimate of effect. • Moderate: Further research is likely to have an important impact on our confidence in the estimate of effect and may change the estimate. • Low: Further research is very likely to have an important impact on our confidence in the estimate of effect and is likely to change the estimate. • Very low: Any estimate of effect is very uncertain.

25. Strength of recommendation “The extent to which one can be confident that adherence to a recommendation will do more good than harm.” • quality of the evidence • translation of the evidence into practice in a specific setting • uncertainty about baseline risk • trade-offs (the relative value attached to the expected benefits, harms and costs)

26. Clarity of the trade-offs between benefits and the harms • the estimated size of the effect for each main outcome • the precision of these estimates • important factors that could be expected to modify the size of the expected effects in specific settings; e.g. proximity to a hospital • the relative value attached to the expected benefits and harms • the variation in values between people

27. ← Option 1 (pink card) Option 2 → (green card)

28. You are hiking.Which of the following animals would you prefer to encounter?

29. ← Option 1 (pink card) Option 2 → (green card)

30. You are buying an ice cream.Which flavor do you prefer?

31. Strawberry ← Option 1 (pink card) Chocolate Option 2 → (green card)

32. You are buying a new car.Which one would you buy?

33. ← Option 1 (pink card) Red Ferrari Option 2 → (green card) Yellow fox

34. Judgements about the balance between benefits and harms • Before considering cost and making a recommendation

35. Judgements about recommendations

36. Judgements about recommendations • “We recommend”…”should” …“Do it” • “We suggest”…”may” … “Probably do it” • “We recommend not”… “may not” …“Probably don’t do it” • “We suggest not”…”should not”… “Don’t do it” No recommendation This could include considerations of costs; i.e. “Is the net gain (benefits-downsides) worth the costs?”

37. Will GRADE lead to change? Should healthy asymptomatic postmenopausal women have been given oestrogen + progestin for prevention in 1992? • Quality of evidence across studies for • CHD • Hip fracture • Colorectal cancer • Breast cancer • Stroke • Thrombosis • Gall bladder disease • Quality of evidence across critical outcomes • Balance between benefits and harms • Recommendations

38. Oestrogen + progestin for prevention after WHI and HERS

39. Oestrogen + progestin for prevention after WHI and HERS

40. Further GRADE developments • Diagnostic tests • Costs • (Equity) • Empirical evaluations • Free software application

41. GRADE Profiler (GRADEpro)

42. GRADE profiler (GRADEpro)

48. GRADE Profile • Excel, HTML, MS Word format • Linked to REVMAN (direct import from REVMAN)