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Holger Schünemann, MD, PhD Chair, Department of Clinical Epidemiology & Biostatistics Professor of Clinical Epidemiology, Biostatistics and Medicine McMaster University, Hamilton, Ontario, Canada. The GRADE system. 1. Formulating questions.

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the grade system

Holger Schünemann, MD, PhD

Chair, Department of Clinical Epidemiology & Biostatistics

Professor of Clinical Epidemiology, Biostatistics and Medicine

McMaster University, Hamilton, Ontario, Canada

The GRADE system

1 formulating questions
1. Formulating questions

Guidelines are a way of answering questions about clinical, communication, organisational or policy interventions, in the hope of improving health care or health policy.

It is therefore helpful to structure a guideline in terms of answerable questions.

WHO Guideline Handbook, 2008

different types of questions
Different types of questions

Background Questions

Definition: e.g. What is Human Papilloma Virus (HPV) infection?

 Mechanism: e.g. How does HPV cause cancer?

Foreground Questions

Efficacy: e.g. What is the efficacy of an HPV vaccine?

Recommendations/decisions: e.g. e.g. Should we use HPV vaccine?

different types of questions1
Different types of questions

Background Questions

Definition: e.g. What is Human Papilloma Virus (HPV) infection?

 Mechanism: e.g. How does HPV cause cancer?

Foreground Questions

Efficacy: e.g. What is the efficacy of an HPV vaccine?

Recommendations/decisions: e.g. e.g. Should we use HPV vaccine?

Actionable items

2 choosing outcomes
2. Choosing outcomes
  • Every decision comes with desirable and undesirable consequences
    • Developing recommendations must include a consideration of desirable and undesirable consequences
desirable and undesirable consequences
Desirable and undesirable consequences
  • desirable effects
    • lower mortality
    • improvement in quality of life, fewer hospitalizations
    • reduction in the burden of treatment
    • reduced resource expenditure
  • undesirable consequences
    • deleterious impact on morbidity, mortality or quality of life, increased resource expenditure
limitations of older systems approaches
Limitations of older systems & approaches
  • confuse quality of evidence with strength of recommendations
  • lack well-articulated conceptual framework
  • criteria not comprehensive or transparent
  • focus on single outcomes
g rades of r ecommendation a ssessment d evelopment and e valuation
Grades of Recommendation Assessment, Development and Evaluation

GRADE Working Group

CMAJ 2003, BMJ 2004, BMC 2004, BMC 2005, AJRCCM 2006, Chest 2006, BMJ 2008, Lancet ID 2007, PLOS Medicine 2007

grade working group
GRADE Working Group

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

JacekMrukowicz, executive directorp

Dianne O’Connell, senior epidemiologistq

Andrew D Oxman, directorf

Bob Phillips, associate fellowr

Holger J Schünemann, professorg,s

Tessa Tan-Torres Edejer, medical officert

David Tovey, Editory

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) FédérationNationale des Centres de LutteContre 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 ValutazionedellaEfficaciadellaAssistenza 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

Y) BMJ Clinical Evidence, UK

the grade approach
The GRADE approach

Clear separation of 2 issues:

1) 4 categories of quality of evidence: very low, low, moderate, or high quality?

  • methodological quality of evidence
  • likelihood of systematic deviation from truth
  • by outcome

2) Recommendation: 2 grades – conditional or strong (for or against)?

  • Quality of evidence only one factor

*www.GradeWorkingGroup.org

determinants of quality
Determinants of quality
  • RCTs start high
  • observational studies start low
  • 5 factors lower the quality of evidence
    • limitations in detailed design and execution
    • inconsistency
    • indirectness
    • reporting bias
    • imprecision
  • 3 factors can increase the quality of evidence
example limitations in design and execution
Example: Limitations in Design and Execution
  • Limitations – observational studies
    • Failure to develop and apply appropriate eligibility criteria - under- or over-matching in case-control studies
    • Selection of exposed and unexposed in cohort studies from different populations
    • Flawed measurement of both exposure and outcome (e.g. recall bias in CC studies)
    • Differential surveillance for outcome in exposed and unexposed in cohort studies
    • Failure to adequately measure/control for confounding
    • Failure to match for prognostic factors and/or adjustment in statistical analysis
categories of recommendations
Categories of recommendations

Although the degree of confidence is a continuum, we suggest using two categories: strong and weak/conditional.

Strong recommendation: the panel is confident that the desirable effects of adherence to a recommendation outweigh the undesirable effects.

Conditional recommendation: the panel concludes that the desirable effects of adherence to a recommendation probably outweigh the undesirable effects, but is not confident.

Recommend

 

Suggest

 

judgements about the strength of a recommendation
Judgements about the strength of a recommendation

No precise threshold for going from a strong to a weak recommendation

The presence of important concerns about one or more of these factors make a weak recommendation more likely.

Panels should consider all of these factors and make the reasons for their judgements explicit.

Recommendations should specify the perspective that is taken (e.g. individual patient, health system) and which outcomes were considered (including which, if any costs).

finally there are no rcts
Finally: There are no RCTs!
  • We will do a consensus statement/guideline (and not use rigorous methods)
  • Do you think that those using the recommendations would like to be informed about the basis (explanation) for a recommendation if they were asked (by their patients)?
  • I suspect the answer is “yes”
there are no rcts cont d
There are no RCTs! Cont’d
  • Another reason for using structured approaches: any form of recommendation needs agreement/consensus – whether based on high or lower quality evidence (voting as a forced form of consensus)
  • Ergo: Consensus statement is a misnomer in regards to differentiating from guideline
  • The level of detail depends on other aspects:
    • Funds, time, greater interest, higher priority
  • Transparency is key
conclusions
Conclusions
  • Clinical practice guidelines should be based on the best availableevidence
  • GRADE provides a structure approach to improve communication – official WHO system
  • Criteria for evidence assessment across questions and outcomes
  • Criteria for moving from evidence to recommendations
  • Transparent, systematic
    • four categories of quality of evidence
    • two grades for strength of recommendations
  • Transparency in decision making and judgments is key
what can raise quality
What can raise quality?

2. dose response relation

  • (higher INR – increased bleeding)
  • childhood lymphoblastic leukemia
    • risk for CNS malignancies 15 years after cranial irradiation
    • no radiation: 1% (95% CI 0% to 2.1%)
    • 12 Gy: 1.6% (95% CI 0% to 3.4%)
    • 18 Gy: 3.3% (95% CI 0.9% to 5.6%)

3. all plausible confounding may be working to reduce the demonstrated effect or increase the effect if no effect was observed

all plausible confounding would result in an underestimate of the treatment effect
All plausible confounding would result in an underestimate of the treatment effect
  • Higher death rates in private for-profit versus private not-for-profit hospitals
    • patients in the not-for-profit hospitals likely sicker than those in the for-profit hospitals
    • for-profit hospitals are likely to admit a larger proportion of well-insured patients than not-for-profit hospitals (and thus have more resources with a spill over effect)
all plausible biases would result in an overestimate of effect
All plausible biases would result in an overestimate of effect
  • Hypoglycaemic drug phenformin causes lactic acidosis
  • The related agent metformin is under suspicion for the same toxicity.
  • Large observational studies have failed to demonstrate an association
    • Clinicians would be more alert to lactic acidosis in the presence of the agent
implications of a strong recommendation
Implications of a strong recommendation

Patients: Most people in your situation would want the recommended course of action and only a small proportion would not

Clinicians: Most patients should receive the recommended course of action

Policy makers: The recommendation can be adapted as a policy in most situations

implications of a weak conditional recommendation
Implications of a weak/conditional recommendation

Patients: The majority of people in your situation would want the recommended course of action, but many would not

Clinicians: Be prepared to help patients to make a decision that is consistent with their own values

Policy makers: There is a need for substantial debate and involvement of stakeholders

should oseltamivir be used for treatment of patients hospitalised with avian influenza h5n1
Should oseltamivir be used for treatment of patients hospitalised with avian influenza (H5N1)?
should oseltamivir be used for treatment of patients hospitalised with avian influenza h5n11
Should oseltamivir be used for treatment of patients hospitalised with avian influenza (H5N1)?

Summary of findings

Transmission: No human to human transmission

Patient or population: Hospitalised, clinical and serologically confirmed cases of avian influenza

oseltamivir for avian flu
Oseltamivir for Avian Flu

Summary of findings:

No clinical trial of oseltamivir for treatment of H5N1 patients.

4 systematic reviews and health technology assessments (HTA) reporting on 5 studies of oseltamivir in seasonal influenza.

Hospitalization: OR 0.22 (0.02 – 2.16)

Pneumonia: OR 0.15 (0.03 - 0.69)

3 published case series.

Many in vitro and animal studies.

No alternative that is more promising at present.

Cost: ~ Euro 50 per treatment course

what would you recommend
What would you recommend?
  • Strong recommendation: the panel is confident that the desirable effects of adherence to a recommendation outweigh the undesirable effects.
  • Weak recommendation: the panel concludes that the desirable effects of adherence to a recommendation probably outweigh the undesirable effects, but is not confident.
slide32
Judgments about the strength of a recommendation - oseltamivir for treatment of patients hospitalised with avian influenza (H5N1)
slide33
Who would recommend oseltamivir for these patients (no other alternative)?
  • YES (green card)
  • No (pink card)
example oseltamivir for avian flu
Example: Oseltamivir for Avian Flu

Recommendation: In patients with confirmed or strongly suspected infection with avian influenza A (H5N1) virus, clinicians should administer oseltamivir treatment as soon as possible (????? recommendation, very low quality evidence).

Schunemann et al., The Lancet ID, 2007

example oseltamivir for avian flu1
Example: Oseltamivir for Avian Flu

Recommendation: In patients with confirmed or strongly suspected infection with avian influenza A (H5N1) virus, clinicians should administer oseltamivir treatment as soon as possible (strong recommendation, very low quality evidence).

Values and Preferences

Remarks: This recommendation places a high value on the prevention of death in an illness with a high case fatality. It places relatively low values on adverse reactions, the development of resistance and costs of treatment.

Schunemann et al., The Lancet ID, 2007

other explanations
Other explanations

Remarks: Despite the lack of controlled treatment data for H5N1, this is a strong recommendation, in part, because there is a lack of known effective alternative pharmacological interventions at this time.

The panel voted on whether this recommendation should be strong or weak and there was one abstention and one dissenting vote.

Schunemann et al., The Lancet ID, 2007