meta-analysis principles and practice in cardiovascular research giuseppe biondi zoccai istituto di...

Meta-analysisMeta-analysisPrinciples and practice in Principles and practice in

cardiovascular researchcardiovascular research

Giuseppe Biondi ZoccaiGiuseppe Biondi Zoccai

Istituto di Cardiologia, Università di TorinoIstituto di Cardiologia, Università di Torino

DisclosureDisclosure

• No funding or conflict of interest to declare

IndexIndex

• Introduction & definitions

• Scientific hierarchy

• The Cochrane Collaboration

• Structured approach to systematic reviews

• Additional topics

• Statistical packages

• Further examples

Exponential increase in Exponential increase in PubMed citationsPubMed citations

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systematic review systematic overview meta-analysis any

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]))

Famous quotesFamous 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

Famous quotesFamous 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

Baby steps of meta-analysisBaby 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

Minimal glossaryMinimal 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 (systematic review (± ± meta-analysis)meta-analysis)

Qualitative reviewQualitative review

Systematic review and meta-analysesSystematic 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

Systematic reviewSystematic review

Systematic review and meta-analysesSystematic review and meta-analyses

Individual patient dataIndividual patient data meta-analysis meta-analysis

• Ideally should be a systematic review and meta-analysis based on individual patient data

• Major pros:– a unique database containing primary studies is

created and used (consistency checks and homogenous variables are created)

– the same analytical tools can be used across studies– subgroup analyses can be performed even for groups

that were not reported in the original publications• Major cons:

– some studies may have to be excluded (publication bias) because original authors may not provide source data

– poses major logistical and financial challenges

Individual patient data meta-analysisIndividual patient data meta-analysis

BMJ 2002

Systematic review and Systematic review and meta-regressionmeta-regression

• A meta-regression employs meta-analytic methods to explore the impact of covariates or moderators on the main effect measure or on other

• All the limitations of non-RCT studies applies, and thus they should mainly be regarded as hypothesis generating

Meta-regressionMeta-regression

Cumulative and prospective Cumulative and prospective meta-analysesmeta-analyses

• A cumulative meta-analysis recomputes and plots the pooled effect estimate every time a new study is added

• A prospective meta-analysis is based on a specific a priori protocol for its conduct, analysis, and reporting, and may use also a cumulative design

Cumulative meta-analysisCumulative meta-analysis

Antman et al, JAMA 1992

Cumulative meta-analysisCumulative meta-analysis

ProsPros• 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 heterogeneit

Lau et al, Lancet 1998

ConsCons• “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

Lau et al, Lancet 1998

ConsCons

Smith et al, BMJ 2003

Biondi-Zoccai

et al, BMJ 2006

IntroductionIntroduction








EBM hierarchy of evidenceEBM hierarchy of evidence1.1. N of 1 randomized controlled trialN of 1 randomized controlled trial

2.2. Systematic reviews of homogeneous randomized trialsSystematic reviews of homogeneous randomized trials

3.3. Single (large) randomized trialSingle (large) randomized trial

4.4. Systematic review of homogeneous observational Systematic review of homogeneous observational studies addressing patient-important outcomesstudies addressing patient-important outcomes

5.5. Single observational study addressing patient-important Single observational study addressing patient-important outcomesoutcomes

6.6. Physiologic studies Physiologic studies (eg blood pressure, cardiac output, (eg blood pressure, cardiac output, exercise capacity, bone density, and so forth)exercise capacity, bone density, and so forth)

7.7. Unsystematic clinical observationsUnsystematic clinical observations

Guyatt and Rennie, Users’ guide to the medical literature, 2002

Parallel hierarchy of scientific Parallel hierarchy of scientific studies in cardiovascular medicinestudies in cardiovascular medicine

Biondi-Zoccai, Ital Heart J 2003

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

Benson et al,

NEJM 2000

The Cochrane CollaborationThe Cochrane Collaboration

Mission Statement:

The Cochrane Collaboration is an world-wide organisation that aims to help people make well informed decisions about healthcare by preparing, maintaining and promoting the accessibility of systematic reviews of the effects of healthcare interventions

The Cochrane CollaborationThe Cochrane Collaboration

• About 6000 contributors

• 50 Collaborative Review Groups (CRGs)

• 12 Centres throughout the world

• 9 Fields

• 11 Methods Groups

• 1 Consumer Network

• Campbell Collaboration

The Cochrane CollaborationThe Cochrane CollaborationObjectives:• Collaboration• Building on the enthusiasm of individuals• Avoiding duplication• Minimising bias• Keeping up to date• Striving for relevance• Promoting access• Ensuring quality• Continuity

The Cochrane CollaborationThe Cochrane Collaboration• 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

Algorithm for systematic reviewsAlgorithm for systematic reviews

• Definition of question and hypothetical solution

• Prospective design of the systematic review

• Problem formulation (population, intervention or

exposure, comparison, outcome [PICO])

• Data search

• Data abstraction and appraisal

• Data analysis ± quantitative synthesis

• Result interpretation and dissemination

Biondi-Zoccai et al, Ital Heart J 2004

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Definition of question and Definition of question and prospective designprospective 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


Problem formulation according Problem formulation according to the PICO approachto the PICO approach

• PPopulation of interest - eg elderly male >2 weeks after

myocardial infarction)

• IIntervention (or exposure) – eg intracoronary

infusion of progenitor blood cells

• CComparison – eg patients treated with progenitor cells vs

standard therapy

• OOutcome(s) – eg change in echocardiographic left ventricular

ejection fraction from discharge to 6-month control


Data searchData 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, LILACS, and

PubMed

• Conference proceedings

• Cross-referencing (snowballing)

• Contact with experts

Study searchStudy search

Biondi-Zoccai et al, Int J Epidemiol 2005

A simple PubMed strategy for clinical studies on LM PCI: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 ACS:(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)))

Study searchStudy search

Reveiz et al, J Clin Epidemiol 2006

Study selectionStudy 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

Andreotti et al,

Eur Heart J 2005

Data abstraction and appraisalData 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)

Data extractionData extraction

Buscemi et al, J Clin Epidemiol 2006

Internal validity of primary studiesInternal 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)

Quality scales are unreliableQuality scales are unreliable

Internal validity of primary studiesInternal validity of primary studies

Hill et al, Eur Heart J 2004

Data synthesisData 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

Effect sizes and p valuesEffect sizes and p valuesForms 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

Effect sizes and p valuesEffect 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 as p values (for effect) the measures of alpha error for hypothesis testing

Relative risksRelative 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 over-optimistic

Odds ratiosOdds 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 userfriendly from a statistical point of view (ORAvsB=1/ORBvsA), yet also overoptimistic

Risk differences and number Risk differences and number needed to treat/harmneeded 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

RR, OR or RD/NNT?RR, OR or RD/NNT?

OR RR RD/NNT

Communication - + ++

Consistency + ++ -

Mathematics ++ - -

Fixed vs random effectsFixed vs random effects• Statistical pooling may be based on:

– Fixed effect methods (eg Mantel-Haenszel or Peto), if we

can hypothesize studies were are estimating the same

population risk estimate (eg with RR, OR, or RD)

– Random effect methods (eg DerSimonian-Laird),

hypothesize individual studies are estimating different

treatment effects (eg with RR, OR, or RD)

• Additionally, inverse variance weighting (either based on

random or fixed effects) may be used to pool individual point

estimates with pertinent standard errors (even with HR)

Our adviceOur 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 meta-regression (or even refraining from meta-analysis at all) might be the best option

Continous variablesContinous 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

Small study biasSmall 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

Statistical heterogeneityStatistical 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

Statistical inconsistencyStatistical 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

IndexIndex








QUOROMQUOROM

• xxx

QUADASQUADAS

Whithing et al, BMC Med Res Method 2003

Oxman and Guyatt indexOxman and Guyatt indexEvaluates 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 reported4. Was bias in the selection of studies avoided5. 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

criteria7. 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

IndexIndex








Statistical packagesStatistical packages

• EasyMA (http://www.spc.univ-lyon1.fr/easyma.net/)

• RevMan (http://www.cochrane.org)

» For meta-analyses of medical interventions

• Meta-Test ([email protected])

• Meta-DiSc (http://www.hrc.es/investigacion/metadisc.html)

» For meta-analyses of diagnostic tests

• FastPro• NCSS• SAS• SPSS• Stata• WEasyMA

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Revman Revman Review: Late percutaneous coronary intervention for infarct-related artery occlusionComparison: 01 Late percutaneous coronary intervention vs best medical therapy for infarct-related artery occlusion Outcome: 01 Death

Study PCI Medical Rx OR (random) OR (random)or sub-category n/N n/N 95% CI 95% CI O - E Variance

TOPS 0/42 0/45 Not estimable 0.00 0.00 TOMIIS 1/25 1/19 0.75 [0.04, 12.82] 0.00 2.10 Horie 1/44 5/39 0.16 [0.02, 1.42] 0.00 1.25 TOAT 2/32 1/34 2.20 [0.19, 25.52] 0.00 1.56 Zeymer et al 6/145 17/151 0.34 [0.13, 0.89] 0.00 0.24 DECOPI 6/109 7/103 0.80 [0.26, 2.46] 0.00 0.33 BRAVE-2 4/182 8/183 0.49 [0.15, 1.66] 0.00 0.39 Silva et al 0/18 2/18 0.18 [0.01, 3.99] 0.00 2.51

Total (95% CI) 597 592 0.48 [0.28, 0.85]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)

0.1 0.2 0.5 1 2 5 10

Favours PCI Favours medical Rx

Review: Late percutaneous coronary intervention for infarct-related artery occlusionComparison: 01 Late percutaneous coronary intervention vs best medical therapy for infarct-related artery occlusion Outcome: 01 Death

Study PCI Medical Rx OR (fixed) OR (fixed)or sub-category n/N n/N 95% CI 95% CI O - E Variance



0.1 0.2 0.5 1 2 5 10


Revman Revman Review: Late percutaneous coronary intervention for infarct-related artery occlusionComparison: 01 Late percutaneous coronary intervention vs best medical therapy for infarct-related artery occlusion Outcome: 01 Death

Study PCI Medical Rx RR (fixed) RR (fixed)or sub-category n/N n/N 95% CI 95% CI O - E Variance



0.1 0.2 0.5 1 2 5 10



Study PCI Medical Rx RD (fixed) RD (fixed)or sub-category n/N n/N 95% CI 95% CI O - E Variance

TOPS 0/42 0/45 0.00 [-0.04, 0.04] 0.00 0.00 TOMIIS 1/25 1/19 -0.01 [-0.14, 0.11] 0.00 0.00 Horie 1/44 5/39 -0.11 [-0.22, 0.01] 0.00 0.00 TOAT 2/32 1/34 0.03 [-0.07, 0.13] 0.00 0.00 Zeymer et al 6/145 17/151 -0.07 [-0.13, -0.01] 0.00 0.00 DECOPI 6/109 7/103 -0.01 [-0.08, 0.05] 0.00 0.00 BRAVE-2 4/182 8/183 -0.02 [-0.06, 0.01] 0.00 0.00 Silva et al 0/18 2/18 -0.11 [-0.28, 0.06] 0.00 0.01

Total (95% CI) 597 592 -0.04 [-0.06, -0.01]Total events: 20 (PCI), 41 (Medical Rx)Test for heterogeneity: Chi² = 9.12, df = 7 (P = 0.24), I² = 23.2%Test for overall effect: Z = 2.80 (P = 0.005)

-0.5 -0.25 0 0.25 0.5


Funnel plotFunnel plot


0.1 0.2 0.5 1 2 5 10

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SE(log OR)

OR (fixed)

IndexIndex








Meta-analysis of intervention studiesMeta-analysis of intervention studies

Meta-analysis of prognostic studiesMeta-analysis of prognostic studies

Troponin

Meta-analysis of diagnostic studiesMeta-analysis of diagnostic studies

Louvard et al, JACC 2006

Take home messageTake home message• The validity of a meta-analysis refers to the

soundness of the original studies and the

procedures used to combine them

• Several dozens of potential validity leaks have

been identified in these procedures

• Given possible weaknesses, the enterprise may

seem hopeless, yet it’s no worse than that of a

pilot reading a preflight checklist and testing

against possibly disastrous conditions

Take home messageTake home message

• However, the checking and testing makes it possible for airplanes to fly even long distances with only minimal risk of equipment failure

• Systematic reviews and meta-analyses, similarly, succeed when researchers enforce sound validity checklists

A few referencesA 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• Clarke M et al, eds. Cochrane reviewers’ handbook 4.2.0. (www.cochrane.org/resources/handbook/handbook.pdf)• Cooper H et al, eds. The handbook of research synthesis. New York, NY: Russell Sage Foundation, 1994• Cucherat M et al. EasyMA: a program for the meta-analysis of clinical trials. Comput Methods Programs Biomed

1997;53:187- 90• Egger M et al, eds. Systematic reviews in health care: meta-analysis in context. 2nd ed. London: BMJ Publishing Group,

2001• Glass G. Primary, secondary and meta-analysis of research. Educ Res 1976;5:3-8• Glasziou P et al. Systematic reviews in health care. A practical guide. Cambridge: Cambridge University Press, 2001• Guyatt G et al, eds. Users’ guides to the medical literature. A manual for evidence-based clinical practice. Chicago, IL: AMA

Press, 2002• Higgins JPT et al. Measuring inconsistency in meta-analyses. BMJ 2003;327:557 – 60• Lau J et al. Summing up evidence: one answer is not always enough. Lancet 1998;351:123 -7• Moher D et al. Improving the quality of reports of meta-analyses of randomised controlled trials: the QUORUM statement.

Lancet 1999; 354: 1896-900• Petitti DB. Meta-analysis, decision analysis, and cost-effectiveness analysis: methods for quantitative synthesis in medicine.

New York, NY: Oxford University Press, 2000• Song F et al. Validity of indirect comparison for estimating efficacy of competing interventions: empirical evidence from published meta-analysis. BMJ 2003;326:472• Thompson SG et al. How should meta-regression analyses undertaken and interpreted? Stat Med 2002;21:1559-73

meta-analysis principles and practice in cardiovascular research giuseppe biondi zoccai istituto di...

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quantitative review

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specific statistical