holger schünemann professor of clinical epidemiology , biostatistics and medicine

Holger SchünemannProfessor of Clinical Epidemiology, Biostatistics and MedicineMcMaster University, Hamilton, CanadaItalian NCI „Regina Elena“, Rome, Italy

Principles guideline development and theGRADE system

Content Describe the grade of recommendation

and what each category means: strong/weak and optional language

How the quality of evidence can be upgraded/down-graded

What happens when you’re recommending something not be done?

Maybe provide some ID-type examples if possible - I’m attaching our clinical questions that may be used as examples?

Provide a quick tutorial of GRADEPro Questions

The GRADE approachClear separation of 2 issues:1) 4 categories of quality of evidence: very

low, low, moderate, or high quality? methodological considerations likelihood of systematic deviation from truth by outcome

2) Recommendation: 2 grades – weak/conditional or strong (for or against)? Quality of evidence only one factor Influenced by magnitude of effect(s) – balance

of benefits and harms, values and preferences, cost

*www.GradeWorkingGroup.org

Implications of a strong recommendation Patients: Most people in this 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 recommendation Patients: The majority of people in this

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/decision aids and shared decision making

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

Answer

Same type of interpretation

Determinants of quality - For body of evidence -

RCTs start high observational studies start low 5 factors that can lower quality

1. limitations of detailed design and execution2. inconsistency3. Indirectness/applicability4. publication bias5. Imprecision

3 factors can increase quality1. large magnitude of effect2. all plausible confounding may be working to reduce

the demonstrated effect or increase the effect if no effect was observed

3. dose-response gradient

Assessing the quality of evidence

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1. Design and Execution

limitations lack of concealment intention to treat principle violated inadequate blinding loss to follow-up early stopping for benefit selective outcome reporting

Example: RCT suggests that danaparoid sodium is of benefit in treating HIT complicated by thrombosis Key outcome: clinicians’ assessment of when the

thromboembolism had resolved Not blinded – subjective judgement

2. Inconsistency of results(Heterogeneity)

if inconsistency, look for explanation patients, intervention, outcome, methods

unexplained inconsistency downgrade quality

Bleeding in thrombosis-prophylaxed hospitalized patients seven RCTs 4 lower, 3 higher risk

Example: Bleeding in the hospital

Dentali et al. Ann Int Med, 2007

Judgment variation in size of effect overlap in confidence intervals statistical significance of heterogeneity I2

Akl E, Barba M, Rohilla S, Terrenato I, Sperati F, Schünemann HJ. “Anticoagulation for the long term treatment of venous thromboembolism in patients with cancer”. Cochrane Database Syst Rev. 2008 Apr 16;(2):CD006650.

Heparin or vitamin K antagonists for survival in patients with cancer

Non-steroidal drug use and risk of pancreatic cancer

Capurso G, Schünemann HJ, Terrenato I, Moretti A, Koch M, Muti P, Capurso L, Delle Fave G. Meta-analysis: the use of non-steroidal anti-inflammatory drugs and pancreatic cancer risk for different exposure categories.

Aliment Pharmacol Ther. 2007 Oct 15;26(8):1089-99.

3. Directness of Evidence

differences in populations/patients (mild versus severe COPD,

older, sicker or more co-morbidity) interventions (all inhaled steroids, new vs. old) outcomes (important vs. surrogate; long-term

health-related quality of life, short –term functional capacity, laboratory exercise, spirometry)

indirect comparisons interested in A versus B have A versus C and B versus C formoterol versus salmeterol versus tiotropium

Alendronate

Risedronate

Placebo

Directness

interested in A versus B available data A vs C, B vs C

4. Publication Bias & Imprecision Publication bias

number of small studies

Egger M, Smith DS. BMJ 1995;310:752-54 21

I.V. Mg in acute myocardial infarctionPublication bias

Meta-analysisYusuf S.Circulation 1993

ISIS-4Lancet 1995

Egger M, Cochrane Colloquium Lyon 2001 22

Funnel plotS

tand

ard

Err

or

Odds ratio0.1 0.3 1 3

3

2

1

0

100.6

Symmetrical:No publication bias

Egger M, Cochrane Colloquium Lyon 2001 23

Funnel plotS

tand

ard

Err

or

Odds ratio0.1 0.3 1 3

3

2

1

0

100.6

Asymmetrical:Publication bias?


I.V. Mg in acute myocardial infarctionPublication bias

Meta-analysisYusuf S.Circulation 1993

ISIS-4Lancet 1995


Meta-analysis confirmed by mega-trials

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Publication bias (File Drawer Problem) Faster and multiple publication of

“positive” trials Fewer and slower publication of

“negative” trials

5. Imprecision

small sample size small number of events

wide confidence intervals uncertainty about magnitude of effect

how to decide if CI too wide? grade down one level? grade down two levels?

extent to which confidence in estimate of effect adequate to support decision

Example: Bleeding in the hospital

Dentali et al. Ann Int Med, 2007

Offer all effective treatments? atrial fib at risk of stroke warfarin increases serious gi bleeding

3% per year

1,000 patients 1 less stroke 30 more bleeds for each stroke prevented

1,000 patients 100 less strokes 3 strokes prevented for each bleed

where is your threshold? how many strokes in 100 with 3% bleeding?

What can raise quality?1. large magnitude can upgrade (RRR 50%)

very large two levels (RRR 80%) common criteria

everyone used to do badly almost everyone does well

oral anticoagulation for mechanical heart valves insulin for diabetic ketoacidosis

hip replacement for severe osteoarthritis2. dose response relation

(higher INR – increased bleeding)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 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 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

Relevant clinical question?Example from a not so common disease

Clinical question:Population: Avian Flu/influenza A (H5N1) patientsIntervention: Oseltamivir (or Zanamivir) Comparison: No pharmacological interventionOutcomes: Mortality, hospitalizations,

resource use, adverse outcomes, antimicrobial resistanceSchunemann et al. The Lancet ID, 2007

Methods – WHO Rapid Advice Guidelines for management of Avian Flu Applied findings of a recent systematic

evaluation of guideline development for WHO/ACHR

Group composition (including panel of 13 voting members):

clinicians who treated influenza A(H5N1) patients infectious disease experts basic scientists public health officers methodologists

Independent scientific reviewers: Identified systematic reviews, recent RCTs, case

series, animal studies related to H5N1 infection

Evidence Profile

No of studies(Ref) Design Limitations Consistency Directness Other

considerations Oseltamivir Placebo Relative(95% CI )

Absolute(95% CI )

Mortality 0 - - - - - - - - - 9

5(TJ 06)

Randomised trial

No limitations One trial only Major uncertainty (-2)1

Imprecise or sparse data (-1)

- - OR 0.22(0.02 to 2.16)

- Very low

6

0 - - - - - - - - - - 7

5(TJ 06)

Randomised trial

No limitations One trial only Major uncertainty (-2)1

Imprecise or sparse data (-1)2

2/982(0.2%)

9/662(1.4%)

RR 0.149(0.03 to 0.69)

- Very low

8

53

(TJ 06)(DT 03)

Randomised trials

No limitations4 Important inconsistency(-1)5

Major uncertainty (-2)1

- - - HR 1.303

(1.13 to 1.50)-

Very low5

26

(TJ 06)Randomised trials

No limitations -7 Major uncertainty (-2)1

None - - - WMD -0.738

(-0.99 to -0.47)

Low4

0 - - - - - - - - - - 4

0 - - - - - - - - - - 7

09 - - - - - - - - - - 7

311

(TJ 06)Randomised trials

No limitations -12 Some uncertainty (-1)13

Imprecise or sparse data (-1)14

- - OR range15

(0.56 to 1.80)-

Low

0 - - - - - - - - - - 4

I mportance

Summary of findings

Cost of drugs

Outbreak control

Resistance

Serious adverse effects (Mention of significant or serious adverse effects)

Minor adverse effects 10 (number and seriousness of adverse effects)

Viral shedding (Mean nasal titre of excreted virus at 24h)

Duration of disease (Time to alleviation of symptoms/median time to resolution of symptoms – influenza cases only)

Duration of hospitalization

LRTI (Pneumonia - influenza cases only)

Healthy adults:

Hospitalisation (Hospitalisations from influenza – influenza cases only)

Quality assessmentNo of patients Effect

Quality

Oseltamivir for treatment of H5N1 infection:

-

-

Oseltamivir for Avian FluSummary 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: ~ 40$ per treatment course

Schunemann et al. Lancet ID, 2007

& PLOS Medicine 2007

Determinants of the strength of recommendation

Factors that can strengthen a recommendation

Comment

Quality of the evidence The higher the quality of evidence, the more likely is a strong recommendation.

Balance between desirable and undesirable effects

The larger the difference between the desirable and undesirable consequences, the more likely a strong recommendation warranted. The smaller the net benefit and the lower certainty for that benefit, the more likely weak recommendation warranted.

Values and preferences The greater the variability in values and preferences, or uncertainty in values and preferences, the more likely weak recommendation warranted.

Costs (resource allocation) The higher the costs of an intervention – that is, the more resources consumed – the less likely is a strong recommendation warranted

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


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 PreferencesRemarks: 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.


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.

Strength of recommendation

“The strength of a recommendation reflects the extent to which we can, across the range of patients for whom the recommendations are intended, be confident that desirable effects of a management strategy outweigh undesirable effects.”

Strong or weak/conditional

Quality of evidence & strength of recommendation Linked but no automatism Other factors beyond the quality of

evidence influence our confidence that adherence to a recommendation causes more benefit than harm

Systems/approaches failed to make this explicit

GRADE separates quality of evidence from strength of recommendation

Creating a new GRADEpro file

Profile groups

Profiles

Profiles: Questions

Importing a RevMan 5 file of a systematic review

Imported data from RevMan 5 file: • outcomes• meta-analyses results• bibliographic information

Managing outcomes to include a maximum of 7

Entering/editing information for dichotomous outcomes

Entering/editing information to grade the quality of the evidence


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 PreferencesThis 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. RemarksDespite 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.


Confidence in evidence

There always is evidence “When there is a question there is

evidence” Research evidence alone is never

sufficient to make a clinical decision Better research greater confidence

in the evidence and decisions

77

Sequence gen.

Allocation concealment

Blinding/Masking

Intention-to-treat analysis

Blinding/Masking

Baseline

Allocation

A B

Intervention No interv.

Follow up Follow up

Outcome Outcome

Method

Random?

Selectionbias?

Performance bias?

Attritionbias?

Detectionbias?

Question

Factors leading to bias?Can you explain them?

P-values and confidence intervals important?

CONSENSUS ALWAYS REQUIRED

Limitations of existing systems

confuse quality of evidence with strength of recommendations

lack well-articulated conceptual framework

criteria not comprehensive or transparent GRADE unique

breadth, intensity of development process wide endorsement and use conceptual framework comprehensive, transparent criteria

GRADE WORKING GROUP

Grades of Recommendation Assessment,

Development and Evaluation

*Grade Working Group. CMAJ 2003, BMJ 2004, BMC 2004, BMC 2005, AJRCCM 2006, BMJ 2008

GRADE Working GroupDavid 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 ProfessorGillian 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, professorg,s Tessa Tan-Torres Edejer, medical officert David Tovey, Editory

Jane Thomas, Lecturer, UKHelena 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, USAc) Centers for Disease Control and Prevention, USAd) University of Newcastle upon Tyne, UKe) German Cochrane Centre, Germanyf) Norwegian Centre for Health Services, Norwayg) McMaster University, Canadah) Scottish Intercollegiate Guidelines Network, UKi) Fédération Nationale des Centres de Lutte Contre le Cancer, Francej) University of Newcastle, Australiak) McMaster University, Canadal) National Institute for Clinical Excellence, UKm) Università di Modena e Reggio Emilia, Italyn) Centro per la Valutazione della Efficacia della Assistenza Sanitaria, Italyo) Australasian Cochrane Centre, Australia p) Polish Institute for Evidence Based Medicine, Polandq) The Cancer Council, Australiar) Centre for Evidence-based Medicine, UKs) National Cancer Institute, Italyt) World Health Organisation, Switzerland u) Finnish Medical Society Duodecim, Finland v) Duke University Medical Center, USA w) Centers for Disease Control and Prevention, USAx) University of London, UKY) BMJ Clinical Evidence, UK

GRADE Uptake World Health Organization National Institute Clinical Excellence (NICE) Allergic Rhinitis in Asthma Guidelines (ARIA) American Thoracic Society British Medical Journal Infectious Disease Society of America American College of Chest Physicians UpToDate American College of Physicians Cochrane Collaboration Infectious Disease Society of America European Society of Thoracic Surgeons Clinical Evidence Agency for Health Care Research and Quality (AHRQ) Over 20 major organizations

The GRADE approachClear 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 – weak/conditional or strong (for or against)? Quality of evidence only one factor Influenced by magnitude of effect(s) – balance

of benefits and harms, values and preferences, cost

*www.GradeWorkingGroup.org

GRADE Quality of Evidence

“Extend of confidence on how adequate the estimate of effect is to support decision”

high: considerable confidence in estimate of effect.

moderate: further research likely to have impact on confidence in estimate, may change estimate.

low: further research is very likely to impact on confidence, likely to change the estimate.

very low: any estimate of effect is very uncertain

Developing recommendations

Conclusion

clinicians, policy makers need summaries that separate: quality of evidence strength of recommendations

explicit rules transparent, informative

GRADE four categories of quality of evidence two grades for strength of recommendations transparent, systematic by and across outcomes applicable to diagnosis wide adoption

Consistency of results

consistency of results if inconsistency, look for explanation

patients, intervention, outcome, methods unexplained inconsistency downgrade

quality oxygen for day-to-day dyspnea in COPD with

exercise hypoxemia five cross-over RCTs oxygen versus placebo 4 no benefit, 1 substantial benefit

Evidence profiles

Directness of Evidence

indirect comparisons interested in A versus B have A versus C and B versus C formoterol versus salmeterol versus tiotropium Acetylcysteine alone for Pulmonary Fibrosis

(all that is available is Acetylcysteine + Prednisone + Azathioprine vs. Prednisone + Azathioprine)


differences in patients (inhalers for mild versus moderate to

severe COPD) interventions (all inhaled steroids versus those

used in clinical trials – drug class effect) outcomes (long-term health-related quality of

life, short –term functional capacity, laboratory exercise, spirometry)

Reporting Bias & Imprecision reporting bias

reporting of studies publication bias

number of small studies reporting of outcomes

small sample size small number of events wide confidence intervals uncertainty about magnitude of effect

Differences in exercise capacity in short-term randomized trials of oxygen in COPD patients.

What can raise quality?

large magnitude can upgrade (RRR 50%) very large two levels (RRR 80%) common criteria

everyone used to do badly almost everyone does well

Insulin in diabetic ketoacidosis dose response relation

(smoking - cancer)

The clinical scenario

A 68 year old male long-term patient of yours. He suffers from COPD but is unable to stop smoking after over 30 years of tobacco use. He has been taking beta-carotene supplements for several months because someone in the “healthy food” store recommended it to prevent cancer. He wants to know whether this will prevent him from getting cancer and whether he should use beta-carotene.

The clinical question

Population: In patients with COPDIntervention: does beta-carotene supplComparison: compared to no suppl.Outcomes: reduce the risk of lung

cancer?

Where do you look for an answer?

Clinical Practice Guidelines

Systematically developed statements to assist practitioner and patient decisions about appropriate health care for specific clinical circumstances

Institute of Medicine, 1992

Determinants of quality RCTs start high observational studies start low 5 Factors that lower quality (bias) 3 Factors that increase quality (bias

is unlikely to explain observed effect) Final quality by outcome:

High Moderate Low Very low

Design and Execution

limitations lack of concealment intention to treat principle violated inadequate blinding loss to follow-up early stopping for benefit

13 RCTs bacterial extract (immunomodulation) for preventing exacerbation unclear concealment of randomization questionable intention to treat inadequate attention to loss to follow-up

Consistency of results

consistency of results if inconsistency, look for explanation

patients, intervention, outcome, methods unexplained inconsistency downgrade

quality oxygen for day-to-day dyspnea in COPD with

exercise hypoxemia five cross-over RCTs oxygen versus placebo 4 no benefit, 1 substantial benefit


indirect comparisons interested in A versus B have A versus C and B versus C formoterol versus salmeterol versus tiotropium

differences in patients (mild versus severe COPD) interventions (all inhaled steroids) outcomes (long-term health-related quality of

life, short –term functional capacity, laboratory exercise, spirometry)

How should recommendations be formulated and presented? Few written standards exist For strong recommendations, the GRADE

working group has suggested adopting terminology such as, “We recommend…” or “Clinicians should…”.

For weak recommendation, they should use less definitive wording, “We suggest…” or “Clinicians might…”.

Clinicians and patients want to know!

1) UpToDate® Users 2) Mini Medical School attendees*:

• Participants preferred to know about the uncertainty relating to outcomes of a treatment or a test

• more interested in knowing about uncertainty relating to benefits than harms (96% vs. 90%; P<0.001).

• strong preference to be informed about the quality of evidence that supports a recommendation.

*Akl et al. J Clin Epi, 2007, in press

GRADE Quality of Evidence

Extent to which confidence in estimate of effect adequate to support decision

high: considerable confidence in estimate of effect.

moderate: further research likely to have impact on confidence in estimate, may change estimate.

low: further research is very likely to impact on confidence, likely to change the estimate.

very low: any estimate of effect is very uncertain

There always is evidence

The better the research and the evidence, the more confident the decision

Evidence alone is never sufficient to make a clinical decision

Do evidence based guidelines make a difference?Non-rigorous guidelines:• Create noise & bias• Make more aggressive recommendations• Can harm patients and impair research efforts• Can reduce credibility of professional societiesEvidence-based clinical practice guidelines can:• reduce delivery of inappropriate care• support introduction of new knowledge into clinical

practice

Grimshaw et al (1992); Woolf et al (1999);

Fretheim et al (2002)

holger schünemann professor of clinical epidemiology , biostatistics and medicine

Documents

quality of evidence

idtype examples

grade of recommendation

high quality

determinants of quality

body of evidence

categories of quality

clinical questions