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Value and Limitations of
Meta-Analysis in the Era of
Evidence-Based Medicine
Giuseppe Biondi-Zoccai, MD
Division of Cardiology, Department of Internal
Medicine, University of Turin, Turin, Italy
Meta-analysis and Evidence-based medicine
Training in Cardiology (METCARDIO), Turin, Italy
[email protected]
www.metcardio.org
Index
• How to define meta-analyses? Key concepts
• What comes first? Scientific hierarchy and The
Cochrane Collaboration
• Where’s the beef? Strenghts of meta-analyses
• Any toxic asset? Weaknesses of meta-analyses
• See one, do one, teach one. Structured
approach to systematic reviews
[email protected]
www.metcardio.org
Why should you trust me?
Meta-analyses or manuscript pertinent to metaanalyses that I have co-authored since graduation
Total = 51
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Why are meta-analysis important:
exponential increase in worldwide PubMed citations
PubMed search strategy: ("2001"[PDAT] : "2005"[PDAT]) AND (("systematic"[title/abstract] AND "review"[title/abstract]) OR
("systematic"[title/abstract] AND "overview"[title/abstract]) OR ("meta-analysis"[title/abstract] OR "meta-analyses"[title/abstract]))
[email protected]
www.metcardio.org
Index
• How to define meta-analyses? Key concepts
• What comes first? Scientific hierarchy and The
Cochrane Collaboration
• Where’s the beef? Strenghts of meta-analyses
• Any toxic asset? Weaknesses of meta-analyses
• See one, do one, teach one. Structured
approach to systematic reviews
[email protected]
www.metcardio.org
Famous quotes
“If I have seen further it is by standing on the
shoulders of giants”
Isaac Newton
“The great advances in science usually
result from new tools rather than from new
doctrines”
Freeman Dyson
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Famous quotes
“I like to think of the meta-analytic process
as similar to being in a helicopter.
On the ground individual trees are visible
with high resolution.
This resolution diminishes as the helicopter
rises, and in its place we begin to see
patterns not visible from the ground”
Ingram Olkin
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www.metcardio.org
Baby steps of meta-analysis
• 1904 - Karl Pearson (UK): correlation between inoculation of
vaccine for typhoid fever and mortality across apparently
conflicting studies
• 1931 – Leonard Tippet (UK): comparison of differences between
and within farming techniques on agricultural yield adjusting for
sample size across several studies
• 1937 – William Cochran (UK): combination of effect sizes across
different studies of medical treatments
• 1970s – Robert Rosenthal and Gene Glass (USA), Archie
Cochrane (UK): combination of effect sizes across different
studies of, respectively, educational and psychological
treatments
• 1980s – exponential development/use of meta-analytic methods
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Minimal glossary
• Review: viewpoint on a subject quoting different primary authors
• Overview: as above
• Qualitative review: deliberately avoids a systematic approach
• Systematic review: deliberately uses a systematic approach to study search,
selection, abstraction, appraisal and pooling
• Quantitative review: uses quantitative methods to appraise or synthesize
data
• Meta-analysis: uses specific statistical methods for data pooling and/or
exploratory analysis
• Individual patient data meta-analysis: uses specific stastistical methods
for data pooling or exploration exploiting individual patient data
→
Our goal: systematic review (± meta-analysis)
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Qualitative review
Tung et al, Ann Intern Med 2006
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Systematic review and meta-analyses
• What is a systematic review?
– A systematic appraisal of the methodological quality,
clinical relevance and consistency of published
evidence on a specific clinical topic in order to provide
clear suggestions for a specific healthcare problem
• What is a meta-analysis?
– A quantitative synthesis that, preserving the identity of
individual studies, tries to provide an estimate of the
overall effect of an intervention, exposure, or diagnostic
strategy
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Systematic review (w/o meta-analysis)
Hackan et al, JAMA 2003
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Systematic review and meta-analysis
Agostoni et al, J Am Coll Cardiol 2004
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Index
• How to define meta-analyses? Key concepts
• What comes first? Scientific hierarchy and The
Cochrane Collaboration
• Where’s the beef? Strenghts of meta-analyses
• Any toxic asset? Weaknesses of meta-analyses
• See one, do one, teach one. Structured
approach to systematic reviews
[email protected]
www.metcardio.org
EBM hierarchy of evidence
1. N of 1 randomized controlled trial
2. Systematic reviews of homogeneous randomized trials
3. Single (large) randomized trial
4. Systematic review of homogeneous observational studies
addressing patient-important outcomes
5. Single observational study addressing patient-important
outcomes
6. Physiologic studies (eg blood pressure, cardiac output, exercise
capacity, bone density, and so forth)
7. Unsystematic clinical observations
Guyatt and Rennie, Users’ guide to the medical literature, 2002
[email protected]
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Parallel hierarchy of scientific
studies in cardiovascular medicine
Qualitative reviews
Systematic reviews
Meta-analyses from
individual studies
Meta-analyses from
individual patient data
Case reports and series
Observational studies
Observational controlled
studies
Randomized controlled
trials
Multicenter randomized
controlled trials
Biondi-Zoccai, Ital Heart J 2003
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The Cochrane Collaboration
Mission Statement:
The Cochrane Collaboration is an world-wide
organization that aims to help people make
wellinformed decisions about healthcare by
preparing, maintaining and promoting the
accessibility of systematic reviews of the
effects of healthcare interventions
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The Cochrane Collaboration
•
•
•
•
•
•
•
Over 6000 contributors
50 Collaborative Review Groups (CRGs)
12 centers throughout the world
9 fields
11 Methods Groups
1 Consumer Network
The Campbell Collaboration (focusing
on education/social sciences)
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Cochrane resources
• Cochrane Database of Systematic Reviews (CDSR) – contains
Cochrane systematic reviews
• Database of Abstracts of Reviews of Effectiveness (DARE) –
contains abstracts of non-Cochrane reviews
• Cochrane Central Controlled Trials Register (CENTRAL) –
contains titles or abstracts of RCTs from multiple sources
• Cochrane Database of Methodology Reviews – contains
Cochrane reviews of methods papers
• Cochrane Methodology Register (CMR) – contains abstracts of
non-Cochrane methods papers
• Health Technology Assessment Database (HTA) – contains
abstracts of HTA papers
• NHS Economic Evaluation Database (NHS EED) – contains
abstracts of economic analysis papers
[email protected]
www.metcardio.org
Index
• How to define meta-analyses? Key concepts
• What comes first? Scientific hierarchy and The
Cochrane Collaboration
• Where’s the beef? Strenghts of meta-analyses
• Any toxic asset? Weaknesses of meta-analyses
• See one, do one, teach one. Structured
approach to systematic reviews
[email protected]
www.metcardio.org
Pros
• Application to any clinical research question
• Systematic searches for clinical evidence
• Explicit and standardized methods for search and selection
of evidence sources
• Thorough appraisal of the internal validity of primary studies
• Quantitative synthesis with increased statistical power
• Increased external validity by appraising the effect of an
intervention (exposure) across different settings
• Test subgroup hypotheses
• Explore clinical and statistical heterogeneity
Lau et al, Lancet 1998
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Any application feasible:
meta-analysis of intervention studies
Landoni et al, Am J Kidney Dis 2006
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Any application feasible:
meta-analysis of diagnostic studies
Hamon et al, JACC 2006
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Any application feasible:
meta-analysis of prognostic studies
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Thorough appraisal of internal validity
and quality of selected studies
Landoni et al, J Cardiothorac Vasc Anesth 2007
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Increasing statistical power and
external validity
De Luca et al, EHJ 2009
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Test subgroup analyses
ATC, BMJ 2002
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Explore statistical and clinical
heterogeneity
[email protected]
Biondi-Zoccai et al, Am Heart J 2005
www.metcardio.org
Explore small study effects
Review:
Late percutaneous coronary intervention for infarct-related artery occlusion
Comparison: 01 Late perc utaneous coronary intervention vs best medical therapy for infarct-rel ated artery occlusion
Outcome:
01 Death
0.0
SE(log OR)
0.4
0.8
1.2
1.6
0.1
0.2
0.5
1
2
Abbate et al, J Am Coll Cardiol 2008
[email protected]
5
10
OR (fixed)
www.metcardio.org
Arguably the most important
meta-analysis ever….
Antman et al, JAMA 1992
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…showing discrepancies
among evidence and experts
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Index
• How to define meta-analyses? Key concepts
• What comes first? Scientific hierarchy and The
Cochrane Collaboration
• Where’s the beef? Strenghts of meta-analyses
• Any toxic asset? Weaknesses of meta-analyses
• See one, do one, teach one. Structured
approach to systematic reviews
[email protected]
www.metcardio.org
Cons
• “Exercise in mega-silliness”
• “Mixing apples with oranges”
• Not original research
• Big RCTs definitely better
• Pertinent studies might not be found, or may be of low
quality or internal validity
• Publication and small study bias
• Average effect largely unapplicable to individuals
• Duplicate efforts may lead to discordant results
Lau et al, Lancet 1998
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What if I mix apples and oranges…
Hooper et al, BMJ 2006
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What if I mix apples and oranges…
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What if only few/low quality
studies are found?
Biondi-Zoccai et al, J Endovasc Ther 2009
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What if small positive studies
are selectively published?
(standard error of log relative risk)
Precision
0.0
P<0.001 at Egger test
P<0.001 at Peters test
0.4
0.8
1.2
1.6
0.01
0.1
Favours cilostazol
1
Favours control
10
100
Effect
(relative risk)
Biondi-Zoccai et al, Am Heart J 2008
[email protected]
www.metcardio.org
What if meta-analyses disagree?
Biondi-Zoccai et al, BMJ 2006
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Appraisal tools: QUOROM
Moher et al, Lancet 1999
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Appraisal tools: Oxman and Guyatt’s
Evaluates the internal validity of a review on 9 separate questions for
which 3 distinct anwers are eligible (“yes”, “partially/can’t tell”, “no”):
1. Where the search methods used to find evidence stated?
2. Was the search for evidence reasonably comprehensive?
3. Were the criteria for deciding which studies to include in the overview reported
4. Was bias in the selection of studies avoided
5. Were the criteria used for assessing the validity of the included studies reported?
6. Was the validity of all studies referred to in the text assessed using appropriate
criteria
7. Were the methods used to combine the findings of the relevant studies reported?
8. Were the findings of the relevant studies combined appropriately relative to the
primary question the overview addresses?
9. Were the conclusions made by the author(s) supported by the data and/or
analysis reported in the overview?
Question 10 summarizes the previous ones and, specifically, asks to rate the
scientific quality of the review from 1 (being extensively flawed) to 3 (carrying
major flaws) to 5 (carrying minor flaws) to 7 (minimally flawed). The developers of
the index specify that if the “partially/can’t tell” answer is used one or more times
in questions 2, 4, 6, or 8, a review is likely to have minor flaws at best and is
difficult to rule out major flaws (ie a score≤4). If the “no” option is used on question
2, 4, 6 or 8, the review is likely to have major flaws (ie a score≤3).
Oxman et al, J Clin Epidemiol 1991
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Index
• How to define meta-analyses? Key concepts
• What comes first? Scientific hierarchy and The
Cochrane Collaboration
• Where’s the beef? Strenghts of meta-analyses
• Any toxic asset? Weaknesses of meta-analyses
• See one, do one, teach one. Structured
approach to systematic reviews
[email protected]
www.metcardio.org
Algorithm for systematic reviews
• Definition of question and hypothetical solution
• Prospective design of the systematic review
• Data search
• Data abstraction and appraisal
• Data analysis ± quantitative synthesis
FEED-BACK ON HYPOTHESIS
• Problem formulation (population, intervention or
exposure, comparison, outcome [PICO])
• Result interpretation and dissemination
Biondi-Zoccai et al, Ital Heart J 2004
[email protected]
www.metcardio.org
Definition of question and
prospective design
• The clinical question should be clearly
stated, being as much explicit as possible
• The review should be designed in as much
details as possible, and yet with a limited a
priori knowledge of the subject
Biondi-Zoccai et al, Ital Heart J 2004
[email protected]
www.metcardio.org
Problem formulation according
to the PICO approach
• Population of interest – eg elderly male >2 weeks after
myocardial infarction)
• Intervention (or exposure) – eg intracoronary
infusion of progenitor blood cells
• Comparison – eg patients treated with progenitor cells vs
standard therapy
• Outcome(s) – eg change in echocardiographic left ventricular
ejection fraction from discharge to 6-month control
Biondi-Zoccai et al, Ital Heart J 2004
[email protected]
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Data search
• After definition of question according to
PICO approach, the appropriate key-words
are used to search several databases
• Useful resources: BioMedCentral, CENTRAL,
clinicaltrials.gov, EMBASE/Scopus, LILACS, and
PubMed
• Conference proceedings
• Cross-referencing (snowballing)
• Contact with experts
[email protected]
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Example of search strategies
A simple PubMed strategy for clinical studies on percutaneous
coronary intervention for left main coronary artery disease: left AND main
AND coronary AND stent* NOT case reports [pt] NOT review [pt] NOT editorial [pt]
A complex PubMed strategy for randomized clinical trials on invasive
vs conservative strategies in acute coronary syndromes: (randomized
controlled trial[pt] OR controlled clinical trial[pt] OR randomized controlled trials[mh] OR random
allocation[mh] OR double-blind method[mh] OR single-blind method[mh] OR clinical trial[pt] OR
clinical trials[mh] OR (clinical trial[tw] OR ((singl*[tw] OR doubl*[tw] OR trebl*[tw] OR tripl*[tw])
AND (mask*[tw] OR blind[tw])) OR (latin square[tw]) OR placebos[mh] OR placebo*[tw] OR
random*[tw] OR research design[mh:noexp] OR comparative study[mh] OR evaluation
studies[mh] OR follow-up studies[mh] OR prospective studies[mh] OR cross-over studies[mh]
OR control*[tw] OR prospectiv*[tw] OR volunteer*[tw]) NOT (animal[mh] NOT human[mh]) NOT
(comment[pt] OR editorial[pt] OR meta-analysis[pt] OR practice-guideline[pt] OR review[pt]))
AND ((invasive OR conservative AND (coronary OR unstable angina OR acute coronary
syndrome* OR unstable coronary syndrome* OR myocardial infarction)))
Biondi-Zoccai et al, Int J Epidemiol 2005
Biondi-Zoccai et al, Am Heart J 2008
Biondi-Zoccai et al, Am Heart J 2005
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Study selection
• 1st - screening of titles and abstracts
• 2nd – potentially pertinent citations are then
retrieved as full reports and appraised
according to prespecified and explicit
inclusion/exclusion criteria
• 3rd – studies fullfilling both inclusion and
exclusion criteria, are then included in the
systematic review
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Andreotti et al,
Eur Heart J 2005
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Data abstraction and appraisal
• Abstraction of outcomes and moderator
variables, possibly on prespecified data form
• Appraisal of the internal validity of primary
studies (eg the risk of selection, performance,
adjudication and attrition bias)
• Performed by single vs multiple reviewers, with
divergences resolved by consensus (possibly
after formal tests for agreement)
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Internal validity of primary studies
• Many scales for the quality of included studies have
been reported, but none is reliable or robust
• The recommended approach is to individually
appraise the potential risk of the 4 biases (eg A-low,
B-moderate, C-high, D-unclear from reported data):
– Selection bias (one group is different than the other)
– Performance bias (treatment is systematically different)
– Adjudication bias (outcome adjudication is selectively
different)
– Attrition bias (follow-up duration or completeness is
different)
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Another common classification
scheme for bias
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Data synthesis
• Quantitative data synthesis is central to
the practice of meta-analysis, and is based
on a major assumptio:
individual studies that are going to be
pooled are relatively homogeneous, both
clinically and statistically, to provide a
meaningful central tendency effect
estimate
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Effect sizes and p values
Forms of research findings suitable to meta-analysis:
• Central tendency research:
– incidence or prevalence rates
– mean (standard error)
• Pre-post contrasts:
– changes in continuous or categorical variables
• Group contrasts:
– experimentally created groups:
• comparison of outcomes between experimental and control groups
– naturally or non-experimentally occurring groups
• treatment, prognostic or diagnostic features
• Association between variables:
– correlation coefficients
– regression coefficients
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Effect sizes and p values
• The effect size makes meta-analysis possible:
– it is the “dependent variable”
– it standardizes findings across studies such that they can be
directly compared
• Any standardized index can be an “effect size” as long
as it meets the following:
– is comparable across studies (generally requires
standardization)
– represents the magnitude and direction of the relationship of
interest
– is independent of sample size
• We identify p values (for effect) for measuring alpha
error for hypothesis testing and corresponding
confidence intervals
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Continous variables
• Continous variables can be pooled with
– Weighted mean differences (WMD), if the
same variable is used across studies
– Standardized mean differences (SMD), if
similar but not identical variables are used
– Inverse variance weighting, if only point
estimates and standard errors are available
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Relative risks
• Relative risks (RR) are defined as the ratio
of incidence rates, and are thus used for
dichotomic variables)
• What is the meaning of RR:
– RR=1 means no difference in risk
– RR<1 means reduced risk in group 1 vs 2
– RR>1 means increased risk in group 1 vs 2
• RRs are easier to interpret but are less
userfriendly from a statistical point of view
(RRAvsB≠1/RRBvsA) and may appear overoptimistic
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Odds ratios
• Odds ratios (OR) are defined as the
ratio of the odds (P/[1-P]) and also
used for dichotomic variables
• When prevalences are low, they are a
good approximation of RR
• They behave similarly to RR (OR=1
means no difference in risk, …)
• ORs are less easy to interpret but more
flexible from a statistical point of view
(ORAvsB=1/ORBvsA), yet also overoptimistic
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Risk differences and number
needed to treat/harm
• The risk difference (RD), ie absolute risk difference, is
the difference between the incidence of events in the
experimental vs control groups
• The RD is theoretically the most clinically relevant
statistics, but changes too much with disease prevalence
• The number to treat (NNT), defined as 1/RD, identifies
the number of patients that we need to treat with the
experimental therapy to avoid one event*
• The NNT is the most clinically meaningful parameter to
express the impact of a treatment on a dichotomic
outcome (eg death), but has the same limits of RD
*Numbers needed to harm (NNH) similarly express the number of patients that we
have to treat with the experimental therapy to cause one adverse event
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RR, OR or RD/NNT?
OR
RR
RD/NNT
Communication
-
+
++
Consistency
+
++
-
Mathematics
++
-
-
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Our advice
• Both RR and OR can be your first choice statistics for
uncommon events
• For common events, the OR is clearly less informative
than the RR for the busy reader
• Complete your analyses by reporting RD and/or NNT for
the sake of clarity
• Fixed effect methods are quite fine for homogeneous/
consistent data
• Random effect methods may be more appropriate for
heterogeneous/inconsistent data, but often metaregression (or even refraining from meta-analysis at all)
might be the best option
[email protected]
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Small study bias
• Publication bias (eg the lower likelihood of
being published for studies with negative
findings, or those originating in non-English
speaking countries) may bias the results of
a meta-analysis
• Other types of small study bias may
undermine the validity of a meta-analysis
• A number of tests, analogical (eg the funnel
plot) or analytical (eg Egger’s or Peter’s)
have been proposed to appraise the
likelihood of such small study bias
Peters et al, JAMA 2006
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Statistical heterogeneity
• Statistical heterogeneity may be suspected
by inspecting tables (summary estimates/SE)
and forest plots, or analytically
• Chi-square, Breslow, or Cochran tests are
most commonly used
• While a 2-tailed p=0.05 is used for cut-off for
hypothesis testing of effect, a 2-tailed p=0.10
is conventionally chosen for heterogeneity
[email protected]
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Statistical inconsistency
• Statistical inconsistency (I2) has been
recently introduced to overcome the risk of
alpha and beta error of standard tests for
statistical heterogeneity
• It is computed as [(Q – df)/Q] x 100%, where
Q is the chi-squared statistic and df is its
degrees of freedom
• I2 values of 25% suggest low inconsistency,
50% moderate inconsistency, and 75%
severe inconsistency
Higgins et al, BMJ 2003
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Statistical packages
• RevMan (http://www.cochrane.org)
» For meta-analyses of medical interventions
• Meta-Test ([email protected])
FREEWARES!
• EasyMA (http://www.spc.univ-lyon1.fr/easyma.net/)
• Meta-DiSc (http://www.hrc.es/investigacion/metadisc.html)
» For meta-analyses of diagnostic tests
•
•
•
•
•
•
FastPro
NCSS
SAS
SPSS
Stata
WEasyMA
[email protected]
Not for
free
• U of Pittsburgh (http://www.pitt.edu/~super1/lecture/lec1171/index.htm)
www.metcardio.org
Typical Revman output
Review:
Comparison:
Outcome:
Late percutaneous coronary intervention for infarct-related artery occlusion
01 Late percutaneous coronary intervention vs best medical therapy for infarct-related artery occlusion
01 Death
Study
or sub-category
PCI
n/N
0/42
1/25
1/44
2/32
6/145
6/109
4/182
0/18
TOPS
TOMIIS
Horie
TOAT
Zeymer et al
DECOPI
BRAVE-2
Silva et al
597
Total (95% CI)
Total events: 20 (PCI), 41 (Medical Rx)
Test for heterogeneity: Chi² = 4.25, df = 6 (P = 0.64), I² = 0%
Test for overall effect: Z = 2.53 (P = 0.01)
Medical Rx
n/N
OR (random)
95% CI
0/45
1/19
5/39
1/34
17/151
7/103
8/183
2/18
Not estimable
0.75 [0.04, 12.82]
0.16 [0.02, 1.42]
2.20 [0.19, 25.52]
0.34 [0.13, 0.89]
0.80 [0.26, 2.46]
0.49 [0.15, 1.66]
0.18 [0.01, 3.99]
592
O-E
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Variance
0.00
2.10
1.25
1.56
0.24
0.33
0.39
2.51
0.48 [0.28, 0.85]
0.1
0.2
0.5
1
Favours PCI
Review:
Comparison:
Outcome:
OR (random)
95% CI
2
5
10
Favours medical Rx
Late percutaneous coronary intervention for infarct-related artery occlusion
01 Late percutaneous coronary intervention vs best medical therapy for infarct-related artery occlusion
01 Death
Study
or sub-category
TOPS
TOMIIS
Horie
TOAT
Zeymer et al
DECOPI
BRAVE-2
Silva et al
PCI
n/N
0/42
1/25
1/44
2/32
6/145
6/109
4/182
0/18
597
Total (95% CI)
Total events: 20 (PCI), 41 (Medical Rx)
Test for heterogeneity: Chi² = 4.25, df = 6 (P = 0.64), I² = 0%
Test for overall effect: Z = 2.75 (P = 0.006)
Medical Rx
n/N
OR (fixed)
95% CI
0/45
1/19
5/39
1/34
17/151
7/103
8/183
2/18
Not estimable
0.75 [0.04, 12.82]
0.16 [0.02, 1.42]
2.20 [0.19, 25.52]
0.34 [0.13, 0.89]
0.80 [0.26, 2.46]
0.49 [0.15, 1.66]
0.18 [0.01, 3.99]
592
O-E
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Variance
0.00
2.10
1.25
1.56
0.24
0.33
0.39
2.51
0.47 [0.27, 0.80]
0.1
0.2
0.5
Favours PCI
[email protected]
OR (fixed)
95% CI
1
2
5
10
Favours medical Rx
www.metcardio.org
A few references
• Biondi-Zoccai GGL et al. Parallel hierarchy of scientific studies in cardiovascular medicine. Ital Heart J 2003; 4: 819-20
• Biondi-Zoccai GGL et al. Compliance with QUOROM and quality of reporting of overlapping meta-analyses on the role of
acetylcysteine in the prevention of contrast associated nephropathy: case study. BMJ 2006;332:202-209
• Biondi-Zoccai GGL et al. A practical algorithm for systematic reviews in cardiovascular medicine. Ital Heart J 2004;5:486 -7
• Bucher HC et al. The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials. J
Clin Epidemiol 1997;50:683– 9
• Cappelleri JC et al. Large trials vs meta-analysis of smaller trials: how do their results compare? JAMA 1996; 276: 1332-8
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Take home messages
• The validity of a meta-analysis refers to the
soundness of the original studies and the
procedures used to combine them (if appropriate)
• Dozens of potential validity threats have been
identified, and should always be borne in mind
• Given its current pivotal role in the hierarchy of
clinical evidence, all clinical decision-makers should
have a working knowledge of how to appraise
and/or conduct a systematic review/meta-analysis
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