Background Methods Results So what? A re-analysis of the Cochrane Library data the dangers of unobserved heterogeneity in meta-analyses Evan Kontopantelis 12 David Springate 13 David Reeves 13 1 NIHR School for Primary Care Research 2 Centre for Health Informatics, Institute of Population Health 3 Centre for Biostatistics, Institute of Population Health Centre for Biostatistics, 10 Feb 2014 Kontopantelis A re-analysis of the Cochrane Library data
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BackgroundMethodsResults
So what?
A re-analysis of the Cochrane Library datathe dangers of unobserved heterogeneity in meta-analyses
Evan Kontopantelis12 David Springate13 DavidReeves13
1NIHR School for Primary Care Research
2Centre for Health Informatics, Institute of Population Health
3Centre for Biostatistics, Institute of Population Health
Centre for Biostatistics, 10 Feb 2014
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
Outline
1 Background
2 MethodsDataAnalyses
3 ResultsMethod performanceCochrane data
4 So what?SummaryRelevant and future work
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
Meta-analysis
Synthesising existing evidence to answer clinical questionsRelatively young and dymanic field of researchActivity reflects the importance of MA and potential toprovide conclusive answersIndividual Patient Data meta-analysis is the best option,but considerable cost and access to patient data requiredWhen original data unavailable, evidence combined in atwo stage process
retrieving the relevant summary effect statisticsusing MA model to calculate the overall effect estimate µ
Kontopantelis A re-analysis of the Cochrane Library data
Model selection depends on the heterogeneity estimateIf present usually a random-effects approach is selectedBut a fixed-effects model may be chosen for theoretical orpractical reasonsDifferent approaches for combining study results
Inverse varianceMantel-HaenszelPeto
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
Meta-analysis methods
Inverse variance: fixed- or random-effects & continuous ordichotomous outcome
DerSimonian-Laird, moment based estimatorAlso: ML, REML, PL, Biggerstaff-Tweedie,Follmann-Proschan, Sidik-Jonkman
Mantel-Haenszel: fixed-effect & dichotomous outcomeodds ratio, risk ratio or risk differencedifferent weighting schemelow events numbers or small studies
Peto: fixed-effect & dichotomous outcomePeto odds ratiosmall intervention effects or very rare events
if τ2 > 0 only modelled through inverse variance weighting
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
Random-effects (RE) models
Accurate τ2 important performance driverLarge τ2 leads to wider CIsZero τ2 reduces all methods to fixed-effectThree main approaches to estimating:
DerSimonian-Laird (τ2DL)
Maximum Likelihood (τ2ML)
Restricted Maximum Likelihood (τ2REML)
Many methods use one of these but vary in estimating µIn practice, τ2
DL computed and heterogeneity quantified andreported using Cochran’s Q, I2 or H2
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
Random or fixed?two ‘schools’ of thought
Fixed-effect (FE)‘what is the average result of trials conducted to date’?assumption-free
Random-effects (RE)‘what is the true treatment effect’?various assumptions
normally distributed trial effectsvarying treatment effect across populations although findingslimited since based on observed studies only
more conservative; findings potentially more generalisable
Researchers reassured when τ2 = 0FE often used when low heterogeneity detected
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
Simples!
Start(sort of)
Outcome(s) continuous
Inverse Variance weighting methods (IV)
Yes
Fixed-effect by conviction
Fixed-effect IV model
Yes
No
Detected heterogeneity
No Random-effects IV model
DL VC ML
REMLPL
Yes
Outcome(s) dichotomousNo
Maentel-Haenszel methods (MH)
Fixed-effect by conviction
Fixed-effect MH true model
YesDetected
heterogeneity
NoCombining dichotomous
and continuous outcomes
Transform dichotomous
outsomes to SMD
Feeling adventurous?
Yes
Yes! No!Rare events
Very rare events?
Estimate heterogeneity (τ2)No
No
Random-effects MH-IV hybrid model
Yes
Peto methods (P)
Fixed-effect Peto true model
YesNo
Outcome(s) time-to-eventNo
Fixed-effect Peto O-E true model
Yes
Bayesian?
No
τ2 est
BP
MVaMVb
Yes
Random-effects IV model
DL
τ2 estimation
DL DL2 DLb VC
VC2ML REML PL
Non-zero prior
Yes
τ2 est
B0
No
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
Cochrane Database for Systematic Reviews
Richest resource of meta-analyses in the worldFifty-four active groups responsible for organising, advisingon and publishing systematic reviewsAuthors obliged to use RevMan and submit the data andanalyses file along with the review, contributing to thecreation of a vast data resourceRevMan offers quite a few fixed-effect choices but only theDerSimonian-Laird random-effects method has beenimplemented to quantify and account for heterogeneity
hidden data
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
Software options
RevManEasy to useStreamlined and ‘idiot-proof’Limited model optionsData manipulation generally not possible
MetaEasy for data collection and some manipulationStata offers quite a few packages with advanced optionsand model choices: metan, metaan, metabias etcR similarly very well supported
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
DataAnalyses
‘Real’ DataCochrane Database for Systematic Reviews
Python code to crawl Wiley website for RevMan filesDownloaded 3,845 relevant RevMan files (of 3,984available in Aug 2012) and imported in StataEach file a systematic reviewWithin each file, various research questions might havebeen posed
investigated across various relevant outcomes?variability in intervention or outcome?
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
DataAnalyses
‘Real’ DataCochrane Database for Systematic Reviews
women seeking professional help for problem with perineal repair
Main 4.4.0k=1
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
DataAnalyses
Simulated Data
Generated effect size Yi and within study varianceestimates σ2
i for each simulated meta-analysis studyDistribution for σ2
i based on the χ21 distribution
For Yi (where Yi = θi + ei )assumed ei ∼ N(0, σ2
i )various distributional scenarios for θi : normal, moderateand extreme skew-normal, uniform, bimodalthree τ2 values to capture low (I2 = 15.1%), medium(I2 = 34.9%) and large (I2 = 64.1%) heterogeneity
For each distributional assumption and τ2 value, 10,000meta-analysis cases simulated
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
DataAnalyses
The questions
Investigate the potential bias when assuming τ2 = 0Compare the performance of τ2 estimators in variousscenariosPresent the distribution of τ2 derived from allmeta-analyses in the Cochrane LibraryPresent details on the number of meta-analysed studies,model selection and zero τ2
Assess the sensitivity of results and conclusions usingalternative models
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
DataAnalyses
Between-study variance estimatorsfrequentist, more or less
DerSimonian-Lairdone-step (τ2
DL)two-step (τ2
DL2)non-parametric bootstrap (τ2
DLb)minimum τ2
DL = 0.01 assumed (τ2DLi )
Variance componentsone-step (τ2
VC)two-step (τ2
VC2)Iterative
Maximum likelihood (τ2ML)
Restricted maximum likelihood (τ2REML)
Profile likelihood (τ2PL)
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
DataAnalyses
Between-study variance estimatorsBayesian
Sidik and Jonkman model error variancecrude ratio estimates used as a-priori values (τ2
MVa)VC estimator used to inform a-priori values with minimumvalue of 0.01 (τ2
MVb)Rukhin
prior between-study variance zero (τ2B0)
prior between-study variance non-zero and fixed (τ2BP)
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
DataAnalyses
Assessment criteriain the 10,000 meta-analysis cases for each simulation scenario
Average bias & average absolute bias in τ2
Percentage of zero τ2
Coverage probability for the effect estimateType I errorproportion of 95% CIs for the overall effect estimate thatcontain the true overall effect θi
Error-interval estimation for the effectquantifies accuracy of estimation of the error-intervalaround the point estimateratio of estimated confidence interval for the effect,compared to the interval based on the true τ2
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
Method performanceCochrane data
Which method?
Performance not affected much by effects’ distributionAbsolute bias
B0 (k ≤ 3) and MLCoverage
MVa-BP (k ≤ 3) and DLbError-interval estimation and detecting
DLbDLb seems best method overall, especially in detectingheterogeneity
appears to be a big problem: DL failed to detect high τ2 forover 50% of small meta-analyses
Bayesian methods did well for very small MAs
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
Method performanceCochrane data
Meta-analyses numbers
Of the 3,845 files 2,801 had identified relevant studies andcontained any data98,615 analyses extracted 57,397 of which meta-analyses
32,005 were overall meta-analyses25,392 were subgroup meta-analyses
Estimation of an overall effectPeto method in 4,340 (7.6%)Mantel-Haenszel in 33,184 (57.8%)Inverse variance in 19,873 (34.6%)random-effects more prevalent in inverse variance methodsand larger meta-analyses
34% of meta-analyses on 2 studies (53% k ≤ 3)!
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
Method performanceCochrane data
Meta-analyses by Cochrane group
22
Figures Figure 1: All meta-analyses, including single-study and subgroup meta-analyses
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Single Study Fixed-effect model (by choice or necessity) Random-effects model
Kontopantelis A re-analysis of the Cochrane Library data
Model selection by number of available studies(% of Random‐effects meta‐analyses)
Fixed‐effect (by choice or necessity) Random‐effects
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
Method performanceCochrane data
Meta-analyses by method choice
23
Figure 2: Model selection by number of available studies (and % of random-effects meta-analyses)*
*note that in many cases fixed-effect models were used when heterogeneity was detected
Figure 3: Comparison of zero between-study variance estimates rates in the Cochrane library data and in simulations, using the DerSimonian-Laird method*
*Normal distribution of the effects assumed in the simulations (more extreme distributions produced similar results).
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
Method performanceCochrane data
Comparing Cochrane data with simulated
To assess the validity of a homogeneity assumption wecompared the percentage of zero τ2
DL, in real andsimulated dataCalculated τ2
DL for all Cochrane meta-analysesPercentage of zero τ2
DL was lower in the real data than inthe low and moderate heterogeneity simulated dataSuggests that mean true between-study variance is higherthan generally assumed but fails to be detected; especiallyfor small meta-analyses
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
Method performanceCochrane data
Comparing Cochrane data with simulated
23
Figure 2: Model selection by number of available studies (and % of random-effects meta-analyses)*
*note that in many case fixed-effect models were used when heterogeneity was detected
Figure 3: Comparison of zero between-study variance estimates rates in the Cochrane library data and in simulations, using the DerSimonian-Laird method*
*Normal distribution of the effects assumed in the simulations (more extreme distributions produced similar results).
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
Method performanceCochrane data
Reanalysing the Cochrane data
We applied all methods to all 57,397 meta-analyses toassess τ2 distributions and the sensitivity of the resultsand conclusionsFor simplicity discuss differences between standardmethods and DLb; not a perfect method but one thatperformed well overallAs in simulations, DLb identifies more heterogeneousmeta-analyses; τ2
DL = 0 for 50.5% & τ2DLb = 0 for 31.2%
Distributions of τ2 agree with the hypothesised χ21
Kontopantelis A re-analysis of the Cochrane Library data
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
Method performanceCochrane data
Changes in results and conclusions
Inverse variance with DLbwhen τ2
DL > 0 but ignored, conclusions change for 19.1% ofanalysesin overwhelming majority of changes, effects stopped beingstatistically significant
Findings were similar for Mantel-Haenszel and Petomethods, although the validity of the inverse varianceweighting in these (which is a prerequisite for the use orrandom-effects models) warrants further investigation
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
Method performanceCochrane data
Changes in results and conclusionse.g. inverse variance analyses
Analysis with bootstrap DL rarely changes conclusions (although higher heterogeneity estimates and found in around 20% more
meta-analysis
Conclusions change for:0.9% of analyses
No
Estimated heterogeneity ‘ignored’ by authors and a
fixed-effect model is chosenYes
Analysis with bootstrap DL rarely changes conclusions
Conclusions change for:2.4% of analyses
No
Analysis with bootstrap DL makes a difference in 1 in 5 analyses (as would analysis with standard DL
but to a smaller extent)
Conclusions change for:19.1% of analyses
Yes
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
SummaryRelevant and future work
Findings
Methods often fail to detect τ2 in small MAEven when τ2 > 0, often ignoredMean true heterogeneity higher than assumed orestimated; but standard method fails to detect itNon-parametric DerSimonian-Laird bootstrap seems bestmethod overall, especially in detecting heterogeneityBayesian estimators MVa (Sidik-Jonkman) and BP(Ruhkin) performed very well when k ≤ 319-21% of statistical conclusions change, when τ2
DL > 0but ignored
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
SummaryRelevant and future work
Conclusions
Detecting and accurately estimating τ2 in a small MA isvery difficult; yet for 53% of Cochrane MAs, k ≤ 3τ2 = 0 assumed to lead to a more reliable meta-analysisand high τ2 is alarming and potentially prohibitiveEstimates of zero heterogeneity should also be a concernsince heterogeneity is likely present but undetectedBootstrapped DL leads to a small improvement butproblem largely remains, especially for very small MAsCaution against ignoring heterogeneity when detectedFor full generalisability, random-effects essential?
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
SummaryRelevant and future work
Effect sizes in Randomised Controlled Trials
Most large treatment effects emerge from small studies,and when additional trials are performed, the effect sizesbecome typically much smallerWell validated large effects are uncommon and pertain tononfatal outcomes
ORIGINAL CONTRIBUTION
Empirical Evaluation of Very Large TreatmentEffects of Medical InterventionsTiago V. Pereira, PhDRalph I. Horwitz, MDJohn P. A. Ioannidis, MD, DSc
MOST EFFECTIVE INTERVEN-tions in health care con-fer modest, incrementalbenefits.1,2 Randomized
trials, the gold standard to evaluatemedical interventions, are ideally con-ducted under the principle of equi-poise3: the compared groups are notperceived to have a clear advantage;thus, very large treatment effects areusually not anticipated. However, verylarge treatment effects are observed oc-casionally in some trials. These effectsmay include both anticipated and un-expected treatment benefits, or theymay involve harms.
Large effects are important to docu-ment reliably because in a relative scalethey represent potentially the cases inwhich interventions can have the mostimpressive effect on health outcomesand because they are more likely to beadopted rapidly and with less evi-dence. Consequently, it is important toknow whether, when observed, verylarge effects are reliable and in what sortof experimental outcomes they are com-monly observed. The importance ofvery large effects has drawn attentionmostly in observational studies4,5 buthas not been well studied in random-ized evidence. It is unknown how of-ten very large effects are replicated insubsequent trials of the same compari-son, disease and outcome. If data ob-served in 1 experiment happen to be atthe extreme of a distribution, subse-
For editorial comment see p 1691.
Author Affiliations: Health Technology AssessmentUnit, Institute of Education and Sciences, GermanHospital Oswaldo Cruz, Sao Paulo, Brazil (DrPereira); GlaxoSmithKline, King of Prussia, Pennsyl-vania, and Yale University School of Medicine, NewHaven, Connecticut (Dr Horwitz); and StanfordPrevention Research Center, Departments ofMedicine and Health and Research, and Policy,
Stanford University School of Medicine, andDepartment of Statistics, School of Humanities andSciences, Stanford University, Stanford, California(Dr Ioannidis).Corresponding Author: John P. A. Ioannidis, MD, DSc,Stanford Prevention Research Center, Medical SchoolOffice Bldg, 1265 Welch Rd, Room X306, Stanford,CA 94305 ([email protected]).
Context Most medical interventions have modest effects, but occasionally some clini-cal trials may find very large effects for benefits or harms.
Objective To evaluate the frequency and features of very large effects in medicine.
Data Sources Cochrane Database of Systematic Reviews (CDSR, 2010, issue 7).
Study Selection We separated all binary-outcome CDSR forest plots with com-parisons of interventions according to whether the first published trial, a subsequenttrial (not the first), or no trial had a nominally statistically significant (P� .05) very largeeffect (odds ratio [OR], �5). We also sampled randomly 250 topics from each groupfor further in-depth evaluation.
Data Extraction We assessed the types of treatments and outcomes in trials withvery large effects, examined how often large-effect trials were followed up by othertrials on the same topic, and how these effects compared against the effects of therespective meta-analyses.
Results Among 85 002 forest plots (from 3082 reviews), 8239 (9.7%) had a sig-nificant very large effect in the first published trial, 5158 (6.1%) only after the firstpublished trial, and 71 605 (84.2%) had no trials with significant very large effects.Nominally significant very large effects typically appeared in small trials with mediannumber of events: 18 in first trials and 15 in subsequent trials. Topics with verylarge effects were less likely than other topics to address mortality (3.6% in firsttrials, 3.2% in subsequent trials, and 11.6% in no trials with significant very largeeffects) and were more likely to address laboratory-defined efficacy (10% in firsttrials,10.8% in subsequent, and 3.2% in no trials with significant very largeeffects). First trials with very large effects were as likely as trials with no very largeeffects to have subsequent published trials. Ninety percent and 98% of the verylarge effects observed in first and subsequently published trials, respectively,became smaller in meta-analyses that included other trials; the median odds ratiodecreased from 11.88 to 4.20 for first trials, and from 10.02 to 2.60 for subsequenttrials. For 46 of the 500 selected topics (9.2%; first and subsequent trials) with avery large-effect trial, the meta-analysis maintained very large effects with P� .001when additional trials were included, but none pertained to mortality-related out-comes. Across the whole CDSR, there was only 1 intervention with large beneficialeffects on mortality, P� .001, and no major concerns about the quality of the evi-dence (for a trial on extracorporeal oxygenation for severe respiratory failure innewborns).
Conclusions Most large treatment effects emerge from small studies, and when ad-ditional trials are performed, the effect sizes become typically much smaller. Well-validated large effects are uncommon and pertain to nonfatal outcomes.JAMA. 2012;308(16):1676-1684 www.jama.com
Downloaded From: http://jama.jamanetwork.com/ by The University of Manchester Library, Evan Kontopantelis on 02/07/2014
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
SummaryRelevant and future work
Publication bias
Publication bias was present in a substantial proportion oflarge meta-analyses that were recently published in fourmajor medical journals (BMJ, JAMA, Lancet, and PLOSMedicine between 2008 and 2012).
Publication Bias in Recent Meta-AnalysesMichal Kicinski*
Department of Science, Hasselt University, Hasselt, Belgium
Abstract
Introduction: Positive results have a greater chance of being published and outcomes that are statistically significanthave a greater chance of being fully reported. One consequence of research underreporting is that it may influencethe sample of studies that is available for a meta-analysis. Smaller studies are often characterized by larger effects inpublished meta-analyses, which can be possibly explained by publication bias. We investigated the associationbetween the statistical significance of the results and the probability of being included in recent meta-analyses.Methods: For meta-analyses of clinical trials, we defined the relative risk as the ratio of the probability of includingstatistically significant results favoring the treatment to the probability of including other results. For meta-analyses ofother studies, we defined the relative risk as the ratio of the probability of including biologically plausible statisticallysignificant results to the probability of including other results. We applied a Bayesian selection model for meta-analyses that included at least 30 studies and were published in four major general medical journals (BMJ, JAMA,Lancet, and PLOS Medicine) between 2008 and 2012.Results: We identified 49 meta-analyses. The estimate of the relative risk was greater than one in 42 meta-analyses,greater than two in 16 meta-analyses, greater than three in eight meta-analyses, and greater than five in four meta-analyses. In 10 out of 28 meta-analyses of clinical trials, there was strong evidence that statistically significant resultsfavoring the treatment were more likely to be included. In 4 out of 19 meta-analyses of observational studies, therewas strong evidence that plausible statistically significant outcomes had a higher probability of being included.Conclusions: Publication bias was present in a substantial proportion of large meta-analyses that were recentlypublished in four major medical journals.
Citation: Kicinski M (2013) Publication Bias in Recent Meta-Analyses. PLoS ONE 8(11): e81823. doi:10.1371/journal.pone.0081823
Funding: Michal Kicinski is currently a PhD fellow at the Research Foundation-Flanders (FWO). The funders had no role in study design, data collectionand analysis, decision to publish, or preparation of the manuscript.
Competing interests: The author has declared that no competing interests exist.
When some study outcomes are more likely to be publishedthan other, the literature that is available to doctors, scientists,and policy makers provides misleading information. Thetendency to decide to publish a study based on its results hasbeen long acknowledged as a major threat to the validity ofconclusions from medical research[1,2]. During the past 25years, the phenomenon of research underreporting has beenextensively investigated. It is clear that statistically significantresults supporting the hypothesis of the researcher often havea greater chance of being published and fully reported[3–7].
Meta-analysis, a statistical approach to estimate a parameterof interest based on multiple studies, plays an essential role inmedical research. One consequence of researchunderreporting is that it influences the sample of studies that isavailable for a meta-analysis[8,9]. This causes a bias, unlessthe process of study selection is modeled correctly[10]. Suchmodeling requires strong assumptions about the nature of the
publication bias, especially when the size of a meta-analysis isnot very large and when robust techniques cannot beused[11–13]. As a result, when publication bias occurs, thevalidity of the meta-analysis is uncertain.
It is well-known that smaller studies are often characterizedby larger effects in published meta-analyses[14–16].Publication bias is one of the possible explanations of thisphenomenon[17]. Although a meta-analysis is typicallypreceded by an investigation of the presence of publicationbias, the standard detection methods are characterized by alow power[11,18–22]. Therefore, the sample of includedstudies may be unrepresentative of the population of allconducted studies even when publication bias has not beendetected. In this study, we investigated whether statisticallysignificant outcomes that showed a positive effect of thetreatment (in the case of clinical trials) and plausible statisticallysignificant outcomes (in the case of observational studies andinterventional studies) had a greater probability of beingincluded in recent meta-analyses than other outcomes. We
PLOS ONE | www.plosone.org 1 November 2013 | Volume 8 | Issue 11 | e81823
Kontopantelis A re-analysis of the Cochrane Library data
BackgroundMethodsResults
So what?
SummaryRelevant and future work
Future work
Look for publication biasExamine factors that predict large effect sizes andsignificant findings (e.g. subanalyses)Is model choice (FE or RE) driven by the results? (i.e.‘hope’ for a significant finding?)Update our Stata metaan command to include theBayesian methods (DLb already added)
Kontopantelis A re-analysis of the Cochrane Library data
Appendix Thank you!
A Re-Analysis of the Cochrane Library Data: The Dangersof Unobserved Heterogeneity in Meta-AnalysesEvangelos Kontopantelis1,2,3*, David A. Springate1,2, David Reeves1,2
1 Centre for Primary Care, NIHR School for Primary Care Research, Institute of Population Health, University of Manchester, Manchester, United Kingdom, 2 Centre for
Biostatistics, Institute of Population Health, University of Manchester, Manchester, United Kingdom, 3 Centre for Health Informatics, Institute of Population Health,
University of Manchester, Manchester, United Kingdom
Abstract
Background: Heterogeneity has a key role in meta-analysis methods and can greatly affect conclusions. However, true levelsof heterogeneity are unknown and often researchers assume homogeneity. We aim to: a) investigate the prevalence ofunobserved heterogeneity and the validity of the assumption of homogeneity; b) assess the performance of various meta-analysis methods; c) apply the findings to published meta-analyses.
Methods and Findings: We accessed 57,397 meta-analyses, available in the Cochrane Library in August 2012. Usingsimulated data we assessed the performance of various meta-analysis methods in different scenarios. The prevalence of azero heterogeneity estimate in the simulated scenarios was compared with that in the Cochrane data, to estimate thedegree of unobserved heterogeneity in the latter. We re-analysed all meta-analyses using all methods and assessed thesensitivity of the statistical conclusions. Levels of unobserved heterogeneity in the Cochrane data appeared to be high,especially for small meta-analyses. A bootstrapped version of the DerSimonian-Laird approach performed best in bothdetecting heterogeneity and in returning more accurate overall effect estimates. Re-analysing all meta-analyses with thisnew method we found that in cases where heterogeneity had originally been detected but ignored, 17–20% of thestatistical conclusions changed. Rates were much lower where the original analysis did not detect heterogeneity or took itinto account, between 1% and 3%.
Conclusions: When evidence for heterogeneity is lacking, standard practice is to assume homogeneity and apply a simplerfixed-effect meta-analysis. We find that assuming homogeneity often results in a misleading analysis, since heterogeneity isvery likely present but undetected. Our new method represents a small improvement but the problem largely remains,especially for very small meta-analyses. One solution is to test the sensitivity of the meta-analysis conclusions to assumedmoderate and large degrees of heterogeneity. Equally, whenever heterogeneity is detected, it should not be ignored.
Citation: Kontopantelis E, Springate DA, Reeves D (2013) A Re-Analysis of the Cochrane Library Data: The Dangers of Unobserved Heterogeneity in Meta-Analyses. PLoS ONE 8(7): e69930. doi:10.1371/journal.pone.0069930
Editor: Tim Friede, University Medical Center Gottingen, Germany
Received February 20, 2013; Accepted June 13, 2013; Published July 26, 2013
Copyright: � 2013 Kontopantelis et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: EK was partly supported by a National Institute for Health Research (NIHR) School for Primary Care Research fellowship in primary health care. Thefunders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. No additional external funding received forthis study.
Competing Interests: The authors have declared that no competing interests exist.
Meta-analysis (MA), the methodologies of synthesising existing
evidence to answer a clinical or other research question, is a
relatively young and dynamic area of research. The furore of
methodological activity reflects the clinical importance of meta-
analysis and its potential to provide conclusive answers, rather
than incremental knowledge contributions, much more cheaply
than a new large Randomised Clinical Trial (RCT).
The best analysis approach is an Individual Patient Data (IPD)
meta-analysis, which requires access to patient level data and
considerably more effort (to obtain the datasets mainly). However,
with IPD data, clinical and methodological heterogeneity,
arguably the biggest concern for meta-analysts, can be addressed
through patient-level covariate controlling or subgroup analyses
when covariate data are not available across all studies.
When the original data are unavailable, researchers have to
combine the evidence in a two stage process, retrieving the
relevant summary effects statistics from publications and using a
suitable meta-analysis model to calculate an overall effect estimate
mm. Model selection depends on the estimated heterogeneity, or
between-study variance, and its presence usually leads to the
adoption of a random-effects (RE) model. The alternative, the
fixed-effects model (FE), is used when meta-analysts, for theoretical
or practical reasons, decide not to adjust for heterogeneity, or have
assumed or estimated the between-study variability to be zero.
Different approaches exist for combining individual study results
into an overall estimate of effect under the fixed- or random-effects
assumptions: inverse variance, Mantel-Haenszel and Peto [1].
Inverse variance approaches are the most flexible and are
suitable for continuous or dichotomous data through a fixed-effect
or one of numerous random-effects methods. The DerSimonian
and Laird [2] method (DL), a moment-based estimator, is the
PLOS ONE | www.plosone.org 1 July 2013 | Volume 8 | Issue 7 | e69930
This project was supported by the School for Primary Care Researchwhich is funded by the National Institute for Health Research (NIHR).The views expressed are those of the author(s) and not necessarilythose of the NHS, the NIHR or the Department of Health.