Holger Schünemann
Professor of Clinical Epidemiology, Biostatistics and Medicine
McMaster University, Hamilton, Canada
Italian NCI „Regina Elena“, Rome, Italy
Principles guideline
development and the
GRADE 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 approach
Clear 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
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
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
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
Answer
Same type of interpretation
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
Determinants of quality
- For body of evidence -
 RCTs start high
 observational studies start low
 5 factors that can lower quality
1.
2.
3.
4.
5.
limitations of detailed design and execution
inconsistency
Indirectness/applicability
publication bias
Imprecision
 3 factors can increase quality
1.
2.
3.
large magnitude of effect
all plausible confounding may be working to reduce the
demonstrated effect or increase the effect if no effect was
observed
dose-response gradient
Assessing the quality of
evidence
11
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
Heparin or vitamin K
antagonists for survival in
patients with cancer
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.
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 healthrelated 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
Directness
interested in A versus B
available data A vs C, B vs C
Alendronate
Risedronate
Placebo
4. Publication Bias &
Imprecision
 Publication bias
 number of small studies
I.V. Mg in
acute
myocardial
infarction
ISIS-4
Lancet 1995
Meta-analysis
Yusuf S.Circulation 1993
Publication bias
Egger M, Smith DS. BMJ 1995;310:752-54
21
Funnel plot
Standard Error
0
Symmetrical:
No publication bias
1
2
3
0.1
0.3
0.6 1
3
10
Odds ratio
Egger M, Cochrane Colloquium Lyon 2001
22
Funnel plot
Standard Error
0
Asymmetrical:
Publication bias?
1
2
3
0.1
0.3
0.6 1
3
10
Odds ratio
Egger M, Cochrane Colloquium Lyon 2001
23
I.V. Mg in
acute
myocardial
infarction
ISIS-4
Lancet 1995
Meta-analysis
Yusuf S.Circulation 1993
Publication bias
Egger M, Smith DS. BMJ 1995;310:752-54
24
Metaanalysis
confirme
d by
megatrials
Egger M, Smith DS. BMJ 1995;310:752-54
25
Publication bias (File
Drawer Problem)
 Faster and multiple publication of “positive”
trials
 Fewer and slower publication of “negative”
trials
26
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?
1.0%
0
1.0%
0
1.0%
0
1.0%
0
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 osteoarthritis
2. 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-forprofit 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
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
Relevant clinical question?
Example from a not so common disease
Clinical question:
Population:
Avian Flu/influenza A (H5N1) patients
Intervention: Oseltamivir (or Zanamivir)
Comparison: No pharmacological intervention
Outcomes:
Mortality, hospitalizations,
resource use, adverse outcomes,
antimicrobial resistance
Schunemann 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
Oseltamivir for treatment of H5N1 infection:
Summary of findings
Quality assessment
No of studies
(Ref)
Design
Limitations
Consistency
No of patients
Other
considerations
Directness
Effect
Oseltamivir
Placebo
Relative
(95% CI)
Absolute
(95% CI)
Quality
Importance
Healthy adults:
Mortality
0
Hospitalisation (Hospitalisations from influenza – influenza cases only)
-
-
-
-
-
5
(TJ 06)
Imprecise or
sparse data (-1)
-
-
OR 0.22
(0.02 to 2.16)
-

Very low
6
-
-
-
-
-
-
7
2/982
(0.2%)
9/662
(1.4%)
RR 0.149
(0.03 to 0.69)
-

Very low
8
Randomised
trial
No limitations One trial only
-
Major
uncertainty
(-2)1
9
Duration of hospitalization
0
LRTI (Pneumonia - influenza cases only)
5
(TJ 06)
Randomised
trial
-
No limitations One trial only
-
Major
uncertainty
(-2)1
Imprecise or
sparse data (-1)2
Duration of disease (Time to alleviation of symptoms/median time to resolution of symptoms – influenza cases only)
Randomised
53
No limitations4 Important
trials
inconsistency
(TJ 06)
(DT 03)
(-1)5
Viral shedding (Mean nasal titre of excreted virus at 24h)
26
(TJ 06)
Randomised
trials
No limitations
-7
Major
uncertainty
(-2)1
-
-
-
HR 1.303
(1.13 to 1.50)
-

Very low
5
Major
uncertainty
(-2)1
None
-
-
-
WMD -0.738
(-0.99 to -0.47)

Low
4
-
-
-
-
-
-
4
-
-
-
-
-
-
7
-
-
-
-
-
-
7
Imprecise or
sparse data (-1)14
-
-
OR range15
(0.56 to 1.80)
-

Low
-
-
-
-
-
-
Outbreak control
0
Resistance
-
-
-
-
0
Serious adverse effects (Mention of significant or serious adverse effects)
09
Minor adverse effects
311
(TJ 06)
10
-
-
-
(number and seriousness of adverse effects)
Randomised
trials
No limitations
-12
Some
uncertainty
(-1)13
Cost of drugs
0
-
-
-
-
4
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: ~ 40$ per treatment course
Schunemann et al. Lancet ID, 2007
& PLOS Medicine 2007
Determinants of the strength
of recommendation
Factors that can strengthen a Comment
recommendation
Quality of the evidence
The higher the quality of evidence, the
more likely is a strong
recommendation.
Balance between desirable
The larger the difference between the
and undesirable effects
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
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
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
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
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
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
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
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.
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.
Schunemann et al. The Lancet ID, 2007
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
Factors leading to bias?
Can you explain them?
Question
Baseline
Method
Random?
Allocation
Sequence gen.
Selection
bias?
A
Performance Intervention
bias?
B
Allocation
concealment
No interv.
Blinding/Masking
Attrition
bias?
Follow up
Follow up
Intention-to-treat
analysis
Detection
bias?
Outcome
Outcome
Blinding/Masking
P-values and confidence intervals important?
CONSENSUS ALWAYS REQUIRED
77
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
Grades of Recommendation Assessment,
Development and Evaluation
GRADE
WORKING GROUP
*Grade Working Group. CMAJ 2003, BMJ 2004, BMC 2004, BMC 2005,
AJRCCM 2006, BMJ 2008
GRADE Working Group
David Atkins, chief medical officera
a) Agency for Healthcare Research and Quality, USA
Dana Best, assistant professorb
b) Children's National Medical Center, USA
Martin Eccles, professord
c) Centers for Disease Control and Prevention, USA
Francoise Cluzeau, lecturerx
d) University of Newcastle upon Tyne, UK
Yngve Falck-Ytter,
Signe
associate directore
e) German Cochrane Centre, Germany
Flottorp, researcherf
Gordon H
f) Norwegian Centre for Health Services, Norway
Guyatt, professorg
Robin T Harbour, quality
g) McMaster University, Canada
and information director h
Margaret C Haugh, methodologisti
David Henry,
i) Fédération Nationale des Centres de Lutte Contre le Cancer, France
professorj
Suzanne Hill, senior
j) University of Newcastle, Australia
lecturerj
Roman Jaeschke, clinical
h) Scottish Intercollegiate Guidelines Network, UK
k) McMaster University, Canada
professork
l) National Institute for Clinical Excellence, UK
Regina Kunx, Associate Professor
m) Università di Modena e Reggio Emilia, Italy
Gillian Leng, guidelines programme directorl
n) Centro per la Valutazione della Efficacia della Assistenza Sanitaria, Italy
Alessandro Liberati, professorm
o) Australasian Cochrane Centre, Australia
Nicola
Magrini, directorn
p) Polish Institute for Evidence Based Medicine, Poland
James Mason, professord
q) The Cancer Council, Australia
Philippa Middleton, honorary research
Jacek Mrukowicz, executive
Dianne O’Connell, senior
fellowo
directorp
epidemiologistq
Andrew D Oxman, directorf
Bob Phillips,
associate fellowr
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
Holger J Schünemann, professorg,s
w) Centers for Disease Control and Prevention, USA
Tessa Tan-Torres Edejer, medical officert
x) University of London, UK
David Tovey, Editory
Y) BMJ Clinical Evidence, UK
Jane Thomas, Lecturer, UK
Helena Varonen, associate editoru
Gunn E Vist, researcherf
John W Williams Jr, professorv
Stephanie Zaza, project directorw
GRADE Uptake
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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 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 – 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)
Directness of Evidence
 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:
Intervention:
Comparison:
Outcomes:
In patients with COPD
does beta-carotene suppl
compared to no suppl.
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
Directness of Evidence
 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 societies
Evidence-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)