meta分析资料全集之Data Analysis in Systematic Reviews-Meta Analysis

DataAnalysisinSystematicReviews-MetaAnalysis

Centralquestionsofinterest

Aretheresultsofthe

studiesfairlysimilar

(consistent)?

Yes

Whatisthecommon,

summaryeffect?

Howpreciseisthe

common,summary

effect?

No

Whatfactorscan

explainthe

dissimilarities

(heterogeneity)inthe

studyresults?

Stepsindataanalysis&presentation

1.Tabulatesummarydata

2.Graphdata

3.Checkforheterogeneity

4.Performameta-analysisifheterogeneityisnotamajorconcern

5.Ifheterogeneityisfound,identifyfactorsthatcanexplainit

6.Evaluatetheimpactofstudyqualityonresults

7.Explorethepotentialforpublicationbias

1.Tabulatesummarydata

Preparetablescomparingstudieswithrespectto:

–Year

–Setting

–Patients

–Intervention

–Comparison

–Outcome(results)

–Quality

Givesa‘firsthand’feelforthedataCanmakesomeassessmentofqualityandheterogeneity

TabulatesummarydataExample:Cochranealbuminreview

Study

Year

Patient

populati

on

Interven

tion

Compari

son

Summary

measure

(RR)

Allocati

on

concealment

Lucasetal.

1978

Trauma

Albumin

No

albumin

13.9

Inadequate

Jelenko

etal.

1979

Burns

Albumin

Ringer’slactate

0.50

Unclear

Rubinetal.

1997

Hypoalbuminemi

a

Albumin

No

albumin

1.9

Adequat

e

CochraneInjuriesGroupAlbuminReviewers.Humanalbuminadministrationincriticallyillpatients:systematicreviewofrandomisedcontrolledtrials.BMJ1998;317:235-40.

2.Graphsummarydata

Efficientwayofpresentingsummaryresults

Forestplot:

–PresentsthepointestimateandCIofeachtrial

–Alsopresentstheoverall,summaryestimate

–Allowsvisualappraisalofheterogeneity

Othergraphs:

–Cumulativemeta-analysis

–Funnelplotforpublicationbias

ForestPlot

Cochrane

albumin

review

BMJ1998;317:235-240

CumulativeMeta-analysisPlot

Passive

smokingand

lungcancer

review

HackshawAKetal.BMJ1997;315:980-88.

3.Checkforheterogeneity

IndicatesthateffectvariesalotacrossstudiesIfheterogeneityispresent,acommon,summarymeasureishardtointerpret

Canbeduetoduetodifferencesin:

–Patientpopulationsstudied

–Interventionsused

–Co-interventions

–Outcomesmeasured

–Studydesignfeatures(eg.lengthoffollow-up)

–Studyquality

–Randomerror

“Averagemenhavinganaveragemeal”

3.Checkforheterogeneity

Howtolookforheterogeneity?

–Visual

•Forestplot:doconfidenceintervalsofstudiesoverlapwitheachotherandthesummaryeffect?

–Statisticaltests:

•Chi-squaretestforheterogeneity(CochranQtest)

–Testswhethertheindividualeffectsarefartherawayfromthecommoneffect,beyondwhatisexpectedbychance

–Haspoorpower

–P-value<0.10indicatessignificantheterogeneity

Visualappraisalofheterogeneity

Zincforcommoncold:

Summaryandincidenceoddsratiosfortheincidenceofanycoldsymptomat1wk

JacksonJL,etal.Zincandthecommoncold:ameta-analysisrevisited.JofNutrition.2000;130:1512S-1515S

PooledSe=0.71

Heterogeneityp<0.001

PooledSp=0.95

Heterogeneityp<0.001

PaiM,etal.Comparisonofdiagnosticaccuracyofcommercialandin-housenucleicacidamplificationtestsfortuberculousmeningitis:ameta-analysis.PosterpresentedattheAmericanSocietyforMicrobiology,2003

3.Checkforheterogeneity

Ifsignificantheterogeneityisfound:

–Findoutwhatfactorsmightexplaintheheterogeneity

–Candecidenottocombinethedata

Ifnoheterogeneity:

–Canperformmeta-analysisandgenerateacommon,summaryeffectmeasure

4.Performmeta-analysis

Decidewhatdatatocombine

Datatypes:

–Continuous

–Dichotomous

Examplesofmeasuresthatcanbecombined:

–Riskratio

–Oddsratio

–Riskdifference

–Effectsize(Zstatistic;standardizedmeandifference)

–P-values

–Correlationcoefficient(R)

–Sensitivity&Specificityofadiagnostictest

4.Performmeta-analysis

Statisticalmodelsforcombiningdata:

–Allmethodsessentiallycomputeweightedaverages

–Weightingfactorisoftenthestudysize

–Models:

•Fixedeffectsmodel

–Inverse-variance,Petomethod,M-Hmethod

•Randomeffectsmodel

–DerSimonian&Lairdmethod

4.Performmeta-analysis

Statisticalmodelsforcombiningdata:

–Fixedeffectsmodel

•itisassumedthatthetrueeffectoftreatmentisthesamevalueineachstudy(fixed);thedifferencesbetweenstudiesissolelyduetorandomerror

–Randomeffectsmodel

•thetreatmenteffectsfortheindividualstudiesareassumedtovaryaroundsomeoverallaveragetreatmenteffect

–Allowsforrandomerrorplusinter-studyvariability

–Resultsinwiderconfidenceintervals(conservative)

–Studiestendtobeweightedmoreequally(relativelymoreweightisgiventosmallerstudies)

4.Performmeta-analysis

MoherDetal.ArchPediatrAdolescMed1998;152:915-20

5.Identifyfactorsthatcanexplainheterogeneity

Ifheterogeneityisfound,usetheseapproachestoidentifyfactorsthatcanexplainit:

–Graphicalmethods

–Subgroupanalysis

–Sensitivityanalysis

–Meta-regression

Ofalltheseapproaches,subgroupanalysisiseasilydoneandinterpreted

Subgroupanalysis:example

Eggeretal.Systematicreviewsinhealthcare.London:BMJbooks,2001.

PooledSe=0.56

Heterogeneityp=0.10

PooledSp=0.98

Heterogeneityp=0.10

SeandSpestimates(withCI)foronlycommercialtests[N=14]

PaiM,etal.Comparisonofdiagnosticaccuracyofcommercialandin-housenucleicacidamplificationtestsfortuberculousmeningitis:ameta-analysis.PosterpresentedattheAmericanSocietyforMicrobiology,2003

PooledSe=0.76Heterogeneityp<0.001

PooledSp=0.92Heterogeneityp<0.001

SeandSpestimates(withCI)foronlyin-housetests[N=35]

PaiM,etal.Comparisonofdiagnosticaccuracyofcommercialandin-housenucleicacidamplificationtestsfortuberculousmeningitis:ameta-analysis.PosterpresentedattheAmericanSocietyforMicrobiology,2003

6.Evaluateimpactofstudyqualityonresults

Narrativediscussionofimpactofqualityonresults

Displaystudyqualityandresultsinatabularformat

Weightthedatabyquality(notrecommended)

Subgroupanalysisbyquality

Includequalityasacovariateinmeta-regression

7.Explorepublicationbias

Studieswithsignificantresultsaremorelikely

–tobepublished

–tobepublishedinEnglish

–tobecitedbyothers

–toproducemultiplepublications

Includingonlypublishe