Meta-analysis: Difference between revisions

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imported>Robert Badgett
imported>Robert Badgett
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Cumulative meta-analysis has been used to show that 25 off 33 randomized controlled trials of streptokinase not necessary<ref name="pmid1614465">{{cite journal |author=Lau J, Antman EM, Jimenez-Silva J, Kupelnick B, Mosteller F, Chalmers TC |title=Cumulative meta-analysis of therapeutic trials for myocardial infarction |journal=N. Engl. J. Med. |volume=327 |issue=4 |pages=248–54 |year=1992 |month=July |pmid=1614465 |doi= |url= |issn=}}</ref> and have shown the delay in adoption of evidence by experts<ref name="pmid1535110">{{cite journal |author=Antman EM, Lau J, Kupelnick B, Mosteller F, Chalmers TC |title=A comparison of results of meta-analyses of randomized control trials and recommendations of clinical experts. Treatments for myocardial infarction |journal=JAMA |volume=268 |issue=2 |pages=240–8 |year=1992 |month=July |pmid=1535110 |doi= |url= |issn=}}</ref>.
Cumulative meta-analysis has been used to show that 25 off 33 randomized controlled trials of streptokinase not necessary<ref name="pmid1614465">{{cite journal |author=Lau J, Antman EM, Jimenez-Silva J, Kupelnick B, Mosteller F, Chalmers TC |title=Cumulative meta-analysis of therapeutic trials for myocardial infarction |journal=N. Engl. J. Med. |volume=327 |issue=4 |pages=248–54 |year=1992 |month=July |pmid=1614465 |doi= |url= |issn=}}</ref> and have shown the delay in adoption of evidence by experts<ref name="pmid1535110">{{cite journal |author=Antman EM, Lau J, Kupelnick B, Mosteller F, Chalmers TC |title=A comparison of results of meta-analyses of randomized control trials and recommendations of clinical experts. Treatments for myocardial infarction |journal=JAMA |volume=268 |issue=2 |pages=240–8 |year=1992 |month=July |pmid=1535110 |doi= |url= |issn=}}</ref>.


Cumulative meta-analyses may be prone to false positive results due to repeated tests of [[statistical significance]].<ref name="pmid18083463">{{cite journal| author=Wetterslev J, Thorlund K, Brok J, Gluud C| title=Trial sequential analysis may establish when firm evidence is reached in cumulative meta-analysis. | journal=J Clin Epidemiol | year= 2008 | volume= 61 | issue= 1 | pages= 64-75 | pmid=18083463  
Cumulative meta-analyses may be prone to false positive results due to repeated tests of [[statistical significance]].<ref name="pmid18083463">{{cite journal| author=Wetterslev J, Thorlund K, Brok J, Gluud C| title=Trial sequential analysis may establish when firm evidence is reached in cumulative meta-analysis. | journal=J Clin Epidemiol | year= 2008 | volume= 61 | issue= 1 | pages= 64-75 | pmid=18083463
| url=http://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=clinical.uthscsa.edu/cite&email=badgett@uthscdsa.edu&retmode=ref&cmd=prlinks&id=18083463 | doi=10.1016/j.jclinepi.2007.03.013 }} <!--Formatted by http://sumsearch.uthscsa.edu/cite/--></ref>
| url=http://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=clinical.uthscsa.edu/cite&email=badgett@uthscdsa.edu&retmode=ref&cmd=prlinks&id=18083463 | doi=10.1016/j.jclinepi.2007.03.013 }} <!--Formatted by http://sumsearch.uthscsa.edu/cite/--></ref>


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===Meta-regression===
===Meta-regression===
Meta-regression allows simultaneous comparison of multiple sources of heterogeneity.<ref name="pmid10521860">{{cite journal| author=Thompson SG, Sharp SJ| title=Explaining heterogeneity in meta-analysis: a comparison of methods. | journal=Stat Med | year= 1999 | volume= 18 | issue= 20 | pages= 2693-708 | pmid=10521860  
Meta-regression allows simultaneous comparison of multiple sources of heterogeneity.<ref name="pmid10521860">{{cite journal| author=Thompson SG, Sharp SJ| title=Explaining heterogeneity in meta-analysis: a comparison of methods. | journal=Stat Med | year= 1999 | volume= 18 | issue= 20 | pages= 2693-708 | pmid=10521860
| url=http://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&retmode=ref&cmd=prlinks&id=10521860 }} <!--Formatted by http://sumsearch.uthscsa.edu/cite/--></ref><ref name="pmid12111920">{{cite journal| author=Thompson SG, Higgins JP| title=How should meta-regression analyses be undertaken and interpreted? | journal=Stat Med | year= 2002 | volume= 21 | issue= 11 | pages= 1559-73 | pmid=12111920  
| url=http://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&retmode=ref&cmd=prlinks&id=10521860 }} <!--Formatted by http://sumsearch.uthscsa.edu/cite/--></ref><ref name="pmid12111920">{{cite journal| author=Thompson SG, Higgins JP| title=How should meta-regression analyses be undertaken and interpreted? | journal=Stat Med | year= 2002 | volume= 21 | issue= 11 | pages= 1559-73 | pmid=12111920
| url=http://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&retmode=ref&cmd=prlinks&id=12111920 | doi=10.1002/sim.1187 }} <!--Formatted by http://sumsearch.uthscsa.edu/cite/--></ref><ref name="pmid11836738">{{cite journal| author=van Houwelingen HC, Arends LR, Stijnen T| title=Advanced methods in meta-analysis: multivariate approach and meta-regression. | journal=Stat Med | year= 2002 | volume= 21 | issue= 4 | pages= 589-624 | pmid=11836738  
| url=http://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&retmode=ref&cmd=prlinks&id=12111920 | doi=10.1002/sim.1187 }} <!--Formatted by http://sumsearch.uthscsa.edu/cite/--></ref><ref name="pmid11836738">{{cite journal| author=van Houwelingen HC, Arends LR, Stijnen T| title=Advanced methods in meta-analysis: multivariate approach and meta-regression. | journal=Stat Med | year= 2002 | volume= 21 | issue= 4 | pages= 589-624 | pmid=11836738
| url=http://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&retmode=ref&cmd=prlinks&id=11836738 }} <!--Formatted by http://sumsearch.uthscsa.edu/cite/--></ref><ref name="pmid19408255">{{cite journal| author=Jackson D, White IR, Thompson SG| title=Extending DerSimonian and Laird's methodology to perform multivariate random effects meta-analyses. | journal=Stat Med | year= 2009 | volume=  | issue=  | pages=  | doi="diagnostic disadvantage of having all the facts"|pmid=19408255  
| url=http://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&retmode=ref&cmd=prlinks&id=11836738 }} <!--Formatted by http://sumsearch.uthscsa.edu/cite/--></ref><ref name="pmid19408255">{{cite journal| author=Jackson D, White IR, Thompson SG| title=Extending DerSimonian and Laird's methodology to perform multivariate random effects meta-analyses. | journal=Stat Med | year= 2009 | volume=  | issue=  | pages=  | doi="diagnostic disadvantage of having all the facts"|pmid=19408255
| url=http://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&retmode=ref&cmd=prlinks&id=19408255 | doi=10.1002/sim.3602 }} <!--Formatted by http://sumsearch.uthscsa.edu/cite/--></ref>
| url=http://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&retmode=ref&cmd=prlinks&id=19408255 | doi=10.1002/sim.3602 }} <!--Formatted by http://sumsearch.uthscsa.edu/cite/--></ref>


Meta-regression can also analyze subgroups.<ref  name="Cochrane Handbook 9.6.3.1">Higgins  JPT, Green S  (editors). [http://www.mrc-bsu.cam.ac.uk/cochrane/handbook/chapter_9/9_6_3_1_is_the_effect_different_in_different_subgroups.htm  9.6.3.1  Is the effect different in different  subgroups?] in ''Cochrane Handbook  for Systematic Reviews of  Interventions Version 5.0.2''  [updated  September  2009]. The Cochrane Collaboration, 2009. Available  from http:// www.cochrane-handbook.org. </ref>A permutation test may reduce the chance of a false positive subgroup analysis.<ref name="pmid15160401">{{cite journal| author=Higgins JP, Thompson  SG| title=Controlling the risk of spurious findings from  meta-regression. | journal=Stat Med | year= 2004 | volume= 23 | issue=  11 | pages= 1663-82 | pmid=15160401  
Meta-regression can also analyze subgroups.<ref  name="Cochrane Handbook 9.6.3.1">Higgins  JPT, Green S  (editors). [http://www.mrc-bsu.cam.ac.uk/cochrane/handbook/chapter_9/9_6_3_1_is_the_effect_different_in_different_subgroups.htm  9.6.3.1  Is the effect different in different  subgroups?] in ''Cochrane Handbook  for Systematic Reviews of  Interventions Version 5.0.2''  [updated  September  2009]. The Cochrane Collaboration, 2009. Available  from http:// www.cochrane-handbook.org. </ref>A permutation test may reduce the chance of a false positive subgroup analysis.<ref name="pmid15160401">{{cite journal| author=Higgins JP, Thompson  SG| title=Controlling the risk of spurious findings from  meta-regression. | journal=Stat Med | year= 2004 | volume= 23 | issue=  11 | pages= 1663-82 | pmid=15160401
| url=http://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=clinical.uthscsa.edu/cite&email=badgett@uthscdsa.edu&retmode=ref&cmd=prlinks&id=15160401  | doi=10.1002/sim.1752 }} <!--Formatted by http://sumsearch.uthscsa.edu/cite/--></ref>
| url=http://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=clinical.uthscsa.edu/cite&email=badgett@uthscdsa.edu&retmode=ref&cmd=prlinks&id=15160401  | doi=10.1002/sim.1752 }} <!--Formatted by http://sumsearch.uthscsa.edu/cite/--></ref>


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Examples of meta-regression analysis are:<br/>
Examples of meta-regression analysis are:<br/>
# {{cite journal| author=McAlister FA, Wiebe  N, Ezekowitz JA, Leung AA, Armstrong PW| title=Meta-analysis:  beta-blocker dose, heart rate reduction, and death in patients with  heart failure. | journal=Ann Intern Med | year= 2009 | volume= 150 |  issue= 11 | pages= 784-94 | pmid=19487713 | url=http://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&retmode=ref&cmd=prlinks&id=19487713  }}
# {{cite journal| author=McAlister FA, Wiebe  N, Ezekowitz JA, Leung AA, Armstrong PW| title=Meta-analysis:  beta-blocker dose, heart rate reduction, and death in patients with  heart failure. | journal=Ann Intern Med | year= 2009 | volume= 150 |  issue= 11 | pages= 784-94 | pmid=19487713 | url=http://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&retmode=ref&cmd=prlinks&id=19487713  }}
#{{cite journal| author=Briel M,  Ferreira-Gonzalez I, You JJ, Karanicolas PJ, Akl EA, Wu P et al.|  title=Association between change in high density lipoprotein cholesterol  and cardiovascular disease morbidity and mortality: systematic review  and meta-regression analysis. | journal=BMJ | year= 2009 | volume= 338 |  issue=  | pages= b92 | pmid=19221140  
#{{cite journal| author=Briel M,  Ferreira-Gonzalez I, You JJ, Karanicolas PJ, Akl EA, Wu P et al.|  title=Association between change in high density lipoprotein cholesterol  and cardiovascular disease morbidity and mortality: systematic review  and meta-regression analysis. | journal=BMJ | year= 2009 | volume= 338 |  issue=  | pages= b92 | pmid=19221140
| url=http://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&retmode=ref&cmd=prlinks&id=19221140  | pmc=PMC2645847 | doi=10.1136/bmj.b92 }}
| url=http://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&retmode=ref&cmd=prlinks&id=19221140  | pmc=PMC2645847 | doi=10.1136/bmj.b92 }}
# {{cite journal| author=Emerging Risk  Factors Collaboration. Erqou S, Kaptoge S, Perry PL, Di Angelantonio E,  Thompson A et al.| title=Lipoprotein(a) concentration and the risk of  coronary heart disease, stroke, and nonvascular mortality. |  journal=JAMA | year= 2009 | volume= 302 | issue= 4 | pages= 412-23 |  pmid=19622820  
# {{cite journal| author=Emerging Risk  Factors Collaboration. Erqou S, Kaptoge S, Perry PL, Di Angelantonio E,  Thompson A et al.| title=Lipoprotein(a) concentration and the risk of  coronary heart disease, stroke, and nonvascular mortality. |  journal=JAMA | year= 2009 | volume= 302 | issue= 4 | pages= 412-23 |  pmid=19622820
| url=http://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&retmode=ref&cmd=prlinks&id=19622820  | doi=10.1001/jama.2009.1063 }}
| url=http://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&retmode=ref&cmd=prlinks&id=19622820  | doi=10.1001/jama.2009.1063 }}


===Network meta-analysis===
===Network meta-analysis===
A network meta-analysis<ref name="pmid12210616">{{cite journal |author=Lumley T |title=Network meta-analysis for indirect treatment comparisons |journal=Stat Med |volume=21 |issue=16 |pages=2313–24 |year=2002 |month=August |pmid=12210616 |doi=10.1002/sim.1201 |url=http://dx.doi.org/10.1002/sim.1201 |issn=}}</ref> and Bayesian hierarchical models<ref name="pmid15449338">Lu G, Ades AE. Combination of direct and indirect evidence in mixed treatment comparisons. Stat Med. 2004 Oct 30;23(20):3105-24. PMID 15449338</ref> pool studies in order to compare to treatments that have not been directly compared.<ref name="pmid18378949">{{cite journal |author=Salanti G, Kavvoura FK, Ioannidis JP |title=Exploring the geometry of treatment networks |journal=Ann. Intern. Med. |volume=148 |issue=7 |pages=544–53 |year=2008 |month=April |pmid=18378949 |doi= |url=http://www.annals.org/cgi/pmidlookup?view=long&pmid=18378949 |issn=}}</ref> Network meta-analyses are commonly not well performed<ref name="pmid19346285">{{cite journal |author=Song F, Loke YK, Walsh T, Glenny AM, Eastwood AJ, Altman DG |title=Methodological problems in the use of indirect comparisons for evaluating healthcare interventions: survey of published systematic reviews |journal=BMJ |volume=338 |issue= |pages=b1147 |year=2009 |pmid=19346285 |pmc=2665205 |doi= |url=http://bmj.com/cgi/pmidlookup?view=long&pmid=19346285 |issn=}}</ref>and can have misleading conclusions.<ref name="pmid18753641">{{cite journal |author=Kent DM, Thaler DE |title=Stroke prevention--insights from incoherence |journal=N. Engl. J. Med. |volume=359 |issue=12 |pages=1287–9 |year=2008 |month=September |pmid=18753641 |doi=10.1056/NEJMe0806806 |url=http://content.nejm.org/cgi/pmidlookup?view=short&pmid=18753641 |issn=}}</ref><ref name="pmid18349026">Thijs V, Lemmens R, Fieuws S. Network meta-analysis: simultaneous meta-analysis of common antiplatelet regimens after transient ischaemic attack or stroke. ur Heart J. 2008 May;29(9):1086-92. Epub 2008 Mar 17. PMID 18349026</ref><ref name="pmid19089502">{{Cite journal | doi = 10.1007/s11606-008-0877-5 | volume = 24 | issue = 2 | pages = 178-188 | last = Chou | first = Roger | coauthors = Susan Carson, Benjamin Chan | title = Gabapentin Versus Tricyclic Antidepressants for Diabetic Neuropathy and Post-Herpetic Neuralgia: Discrepancies Between Direct and Indirect Meta-Analyses of Randomized Controlled Trials
A network meta-analysis<ref name="pmid12210616">{{cite journal |author=Lumley T |title=Network meta-analysis for indirect treatment comparisons |journal=Stat Med |volume=21 |issue=16 |pages=2313–24 |year=2002 |month=August |pmid=12210616 |doi=10.1002/sim.1201 |url=http://dx.doi.org/10.1002/sim.1201 |issn=}}</ref> and Bayesian hierarchical models<ref name="pmid15449338">Lu G, Ades AE. Combination of direct and indirect evidence in mixed treatment comparisons. Stat Med. 2004 Oct 30;23(20):3105-24. PMID 15449338</ref> pool studies in order to compare to treatments that have not been directly compared.<ref name="pmid18378949">{{cite journal |author=Salanti G, Kavvoura FK, Ioannidis JP |title=Exploring the geometry of treatment networks |journal=Ann. Intern. Med. |volume=148 |issue=7 |pages=544–53 |year=2008 |month=April |pmid=18378949 |doi= |url=http://www.annals.org/cgi/pmidlookup?view=long&pmid=18378949 |issn=}}</ref> Network meta-analyses are commonly not well performed<ref name="pmid19346285">{{cite journal |author=Song F, Loke YK, Walsh T, Glenny AM, Eastwood AJ, Altman DG |title=Methodological problems in the use of indirect comparisons for evaluating healthcare interventions: survey of published systematic reviews |journal=BMJ |volume=338 |issue= |pages=b1147 |year=2009 |pmid=19346285 |pmc=2665205 |doi= |url=http://bmj.com/cgi/pmidlookup?view=long&pmid=19346285 |issn=}}</ref>and can have misleading conclusions.<ref name="pmid18753641">{{cite journal |author=Kent DM, Thaler DE |title=Stroke prevention--insights from incoherence |journal=N. Engl. J. Med. |volume=359 |issue=12 |pages=1287–9 |year=2008 |month=September |pmid=18753641 |doi=10.1056/NEJMe0806806 |url=http://content.nejm.org/cgi/pmidlookup?view=short&pmid=18753641 |issn=}}</ref><ref name="pmid18349026">Thijs V, Lemmens R, Fieuws S. Network meta-analysis: simultaneous meta-analysis of common antiplatelet regimens after transient ischaemic attack or stroke. ur Heart J. 2008 May;29(9):1086-92. Epub 2008 Mar 17. PMID 18349026</ref><ref name="pmid19089502">{{Cite journal | doi = 10.1007/s11606-008-0877-5 | volume = 24 | issue = 2 | pages = 178-188 | last = Chou | first = Roger | coauthors = Susan Carson, Benjamin Chan | title = Gabapentin Versus Tricyclic Antidepressants for Diabetic Neuropathy and Post-Herpetic Neuralgia: Discrepancies Between Direct and Indirect Meta-Analyses of Randomized Controlled Trials
| journal = Journal of General Internal Medicine | accessdate = 2009-01-26 | date = 2009-02-01 | url = http://dx.doi.org/10.1007/s11606-008-0877-5 |pmid=19089502 }}</ref> Network meta-analyses have been conducted by the [[Cochrane Collaboration]].<ref name="pmid19884297">{{cite journal| author=Singh JA, Christensen R, Wells GA, Suarez-Almazor ME, Buchbinder R, Lopez-Olivo MA et al.| title=A network meta-analysis of randomized controlled trials of biologics for rheumatoid arthritis: a Cochrane overview. | journal=CMAJ | year= 2009 | volume= 181 | issue= 11 | pages= 787-96 | pmid=19884297  
| journal = Journal of General Internal Medicine | accessdate = 2009-01-26 | date = 2009-02-01 | url = http://dx.doi.org/10.1007/s11606-008-0877-5 |pmid=19089502 }}</ref> Network meta-analyses have been conducted by the [[Cochrane Collaboration]].<ref name="pmid19884297">{{cite journal| author=Singh JA, Christensen R, Wells GA, Suarez-Almazor ME, Buchbinder R, Lopez-Olivo MA et al.| title=A network meta-analysis of randomized controlled trials of biologics for rheumatoid arthritis: a Cochrane overview. | journal=CMAJ | year= 2009 | volume= 181 | issue= 11 | pages= 787-96 | pmid=19884297
| url=http://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=clinical.uthscsa.edu/cite&email=badgett@uthscdsa.edu&retmode=ref&cmd=prlinks&id=19884297 | doi=10.1503/cmaj.091391 | pmc=PMC2780484 }} <!--Formatted by http://sumsearch.uthscsa.edu/cite/--></ref><ref name="pmid19821440">{{cite journal| author=Singh JA, Christensen R, Wells GA, Suarez-Almazor ME, Buchbinder R, Lopez-Olivo MA et al.| title=Biologics for rheumatoid arthritis: an overview of Cochrane reviews. | journal=Cochrane Database Syst Rev | year= 2009 | volume=  | issue= 4 | pages= CD007848 | pmid=19821440  
| url=http://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=clinical.uthscsa.edu/cite&email=badgett@uthscdsa.edu&retmode=ref&cmd=prlinks&id=19884297 | doi=10.1503/cmaj.091391 | pmc=PMC2780484 }} <!--Formatted by http://sumsearch.uthscsa.edu/cite/--></ref><ref name="pmid19821440">{{cite journal| author=Singh JA, Christensen R, Wells GA, Suarez-Almazor ME, Buchbinder R, Lopez-Olivo MA et al.| title=Biologics for rheumatoid arthritis: an overview of Cochrane reviews. | journal=Cochrane Database Syst Rev | year= 2009 | volume=  | issue= 4 | pages= CD007848 | pmid=19821440
| url=http://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=clinical.uthscsa.edu/cite&email=badgett@uthscdsa.edu&retmode=ref&cmd=prlinks&id=19821440 | doi=10.1002/14651858.CD007848.pub2 }} <!--Formatted by http://sumsearch.uthscsa.edu/cite/--></ref>
| url=http://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=clinical.uthscsa.edu/cite&email=badgett@uthscdsa.edu&retmode=ref&cmd=prlinks&id=19821440 | doi=10.1002/14651858.CD007848.pub2 }} <!--Formatted by http://sumsearch.uthscsa.edu/cite/--></ref>


Network meta-analyses can be conducted with [http://www.mrc-bsu.cam.ac.uk/bugs/ Bugs] and [http://mathstat.helsinki.fi/openbugs/ OpenBugs] software.
Network meta-analyses can be conducted with [http://www.mrc-bsu.cam.ac.uk/bugs/ Bugs] and [http://mathstat.helsinki.fi/openbugs/ OpenBugs] software.
===Meta-analysis of diagnostic tests===
Standards exists for the meta-analysis of [[diagnostic test]]s.<ref>Diagnostic  Test  Accuracy Working Group (2009) [http://srdta.cochrane.org/handbook-dta-reviews Handbook for DTA Reviews]. Cochrane Collaboration</ref><ref name="pmid19075208">{{cite journal| author=Leeflang MM, Deeks JJ, Gatsonis C, Bossuyt PM, Cochrane Diagnostic Test Accuracy Working Group| title=Systematic reviews of diagnostic test accuracy. | journal=Ann Intern Med | year= 2008 | volume= 149 | issue= 12 | pages= 889-97 | pmid=19075208
| url=http://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=clinical.uthscsa.edu/cite&retmode=ref&cmd=prlinks&id=19075208 }} </ref>


==Factors associated with higher quality meta-analyses==
==Factors associated with higher quality meta-analyses==

Revision as of 22:59, 5 April 2010

This article is developing and not approved.
Main Article
Discussion
Related Articles  [?]
Bibliography  [?]
External Links  [?]
Citable Version  [?]
 
This editable Main Article is under development and subject to a disclaimer.

Meta-analysis is defined as "a quantitative method of combining the results of independent studies (usually drawn from the published literature) and synthesizing summaries and conclusions which may be used to evaluate therapeutic effectiveness, plan new studies, etc., with application chiefly in the areas of research and medicine."[1]

A meta-analyses is a subset of systematic reviews in which the results of the studies are numerically pooled.

Standards for the reporting of meta-analyses exist.[2]

Validity of meta-analysis

Studies on the validity of meta-analyses conflict.[3][4][5] Some of the conflict may be due to the methods used to compare the meta-analyses.[6]

Methods of meta-analysis

Guidelines are available for the conduct[7] and reporting[2] of meta-analyses.

Searching for studies

Meta-analyses vary in the extent of their searches for underlying studies. [8] There is debate on how extensive should be the search for studies as there is are diminishing returns with extensive searching. Some studies suggest limiting searches[9][10][11] while other studies advocate exhaustive searches[12][13][14][15][16][17] including unpublished studies[18][19].

There is not a consensus on what details of searching should be reported in a meta-analysis.[20]

Selecting studies for inclusion

Conflict in selection of trials to be included in the meta-analysis can affect the conclusions of a meta-analysis.[21][22][23]

Although meta-analyses in general are very inclusive, arguments exist for only including the best trials.[24]

Assessing the quality of trials

For more information, see: Randomized controlled trial.


Cochrane bias scale

The Cochrane Collaboration uses a six item tool.[25]

Jadad score

The Jadad score may be used to assess quality and contains three items:[26]

  1. Was the study described as randomized (this includes the use of words such as randomly, random, and randomization)?
  2. Was the study described as double blind?
  3. Was there a description of withdrawals and dropouts?

Each question is scored one point for a yes answer. In addition, for questions and 2, a point is added if the method was appropriate and a point is deducted if the method is not appropriate (e.g. not effectively randomized or not effectively double-blinded).

Statistical methods

Studies are usually statistically combined by a method such as the DerSimonian and Laird.[27] The DerSimonian and Laird weight for pooling studies is a type of inverse variance weight and creates a random effect model.

Statistical packages are available from the Cochrane Collaboration (http://www.cc-ims.net/revman) and for R (programming language) (rmeta and HSAUR2).

Studies with groups having zero events

Excluding studies with zero events total events (zero-total-event trials) or zero events in one treatment group (zero-event trials) may exaggerate effect sizes.[28][29] An alternative is to use a continuity correction.[30] Rather than using a constant continuity correction, less bias may occur by correcting with either[31]

  • "empirical estimate of the pooled effect size from the remaining studies in the meta-analysis."
  • "a function of the reciprocal of the opposite group arm size"

For an example of continuity correction using the second method above:[28]

  • S is the sum of corrections for event and no event cells (usually S=1 in a zero-event trial and S=2 in a zero-total-event trial)
  • R is the ratio of group sizes (R=1 if both groups are the same)
  • For a zero-event trial with equal group sizes
    • The correction in the larger experimental group is R/S*(R + 1). This becomes 1/1*(1 + 1) = 1
    • The correction in the smaller experimental group is 1/S*(R + 1). This becomes 1/1*(1 + 1) = 1
  • For a zero-event-total trial with equal group sizes
    • The correction in the larger experimental group is R/S*(R + 1). This becomes 1/2*(1 + 1) = 0.5
    • The correction in the smaller experimental group is 1/S*(R + 1). This becomes 1/2*(1 + 1) = 0.5

Subgroup analysis

There are two types of interactions:[32]

  • Qualitative interaction interaction exists if the direction of effect is reversed in subgroups.
  • Quantitative interaction is when the size of the effect varies but not the direction.

If the subgrouping accounts for all heterogeneity, interaction can be sought using an inverse-variance method for a fixed-effect model.[33]

If the subgrouping does not account for all heterogeneity, interaction can be tested with meta-regression to avoid false-positive results.[33][34] Metagression is detailed in a section below.

Software

Software available for meta-analysis includes:[35]

Displaying results

Study results may be grouped and displayed with a Forest plot.

(CC) Photo: Robert Badgett
Forest Plot showing meta-analysis of randomized controlled trials of differing target glucose control and mortality for diabetes mellitus type 2. Note the heterogeneity (P<0.05 and high I2 in circled in red) due to increased death when the glycosylated hemoglobin A (Hb A1c) target was 6.0% in the ACCORD trial[37]

Measuring consistency of study results

Consistency can be statistically tested using either the Cochran's Q or I2.[38][39] The I2 is the "percentage of total variation across studies that is due to heterogeneity rather than chance."[38] These numbers are usually displayed for each group of studies on a Forest plot.

In interpreting of the Cochran's Q, heterogeneity exists if its p-value is < 0.05 or possibly if < 0.10[40][41].

The following has been proposed for interpreting I2:[38]

  • Low heterogeneity is I2 = 25%
  • Moderate heterogeneity is I2 = 50%
  • High heterogeneity is I2 = 75%

or according to the Handbook of the Cochrane Collaboration:[42]

  • 0%-40%: might not be important
  • 30%-60%: may represent moderate heterogeneity
  • 50%-90%: may represent substantial heterogeneity
  • 75%-100%: considerable heterogeneity

Statistical methods exist for assessing the importance of subgroups.[43]

Variations on meta-analysis

Cumulative meta-analysis

Cumulative meta-analysis has been used to show that 25 off 33 randomized controlled trials of streptokinase not necessary[44] and have shown the delay in adoption of evidence by experts[45].

Cumulative meta-analyses may be prone to false positive results due to repeated tests of statistical significance.[46]

Individual patient data meta-analysis

An individual patient data meta-analysis is "where analyses are done using original data and outcomes for each person enrolled in relevant studies; these results are then pooled in one analysis as if patients were in a single large study."[47]

Individual patient data meta-analysis (IPD meta-analysis) may have more long lasting results than other meta-analyses.[48]

Meta-regression

Meta-regression allows simultaneous comparison of multiple sources of heterogeneity.[49][50][51][52]

Meta-regression can also analyze subgroups.[33]A permutation test may reduce the chance of a false positive subgroup analysis.[34]

When analyzing a meta-regression of dichotomous independent variables, the "results of meta-regression analyses are most usefully expressed as ratios of odds ratios (or risk ratios)."[7]

Meta-regression can be performed with the rmeta package[53] of the R programming language as described by Everitt and Hothorn[54][55].

Examples of meta-regression analysis are:

  1. McAlister FA, Wiebe N, Ezekowitz JA, Leung AA, Armstrong PW (2009). "Meta-analysis: beta-blocker dose, heart rate reduction, and death in patients with heart failure.". Ann Intern Med 150 (11): 784-94. PMID 19487713.
  1. Briel M, Ferreira-Gonzalez I, You JJ, Karanicolas PJ, Akl EA, Wu P et al. (2009). "Association between change in high density lipoprotein cholesterol and cardiovascular disease morbidity and mortality: systematic review and meta-regression analysis.". BMJ 338: b92. DOI:10.1136/bmj.b92. PMID 19221140. PMC PMC2645847. Research Blogging.
  1. Emerging Risk Factors Collaboration. Erqou S, Kaptoge S, Perry PL, Di Angelantonio E, Thompson A et al. (2009). "Lipoprotein(a) concentration and the risk of coronary heart disease, stroke, and nonvascular mortality.". JAMA 302 (4): 412-23. DOI:10.1001/jama.2009.1063. PMID 19622820. Research Blogging.


Network meta-analysis

A network meta-analysis[56] and Bayesian hierarchical models[57] pool studies in order to compare to treatments that have not been directly compared.[58] Network meta-analyses are commonly not well performed[59]and can have misleading conclusions.[60][61][62] Network meta-analyses have been conducted by the Cochrane Collaboration.[63][64]

Network meta-analyses can be conducted with Bugs and OpenBugs software.

Meta-analysis of diagnostic tests

Standards exists for the meta-analysis of diagnostic tests.[65][66]

Factors associated with higher quality meta-analyses

Meta-analyses by the Cochrane Collaboration tend to be of higher quality.[67]

Individual data meta-analyses, in which the records from individual patients are pooled together into one dataset, tend to have more stable conclusions.[48]

Factors associated with lower quality meta-analyses

About a third of meta-analyses that happen to precede large randomized controlled trials will conflict with the results of the trial.[3]

Conflict of interest

Meta-analyses produced with a conflict of interest are more likely to interpret results as positive.[68]

Publication bias

Publication bias against negative studies may threaten the validity of meta-analyses that are positive and all the studies included within the meta-analysis are small.[69][70]

In performing a meta-analysis, a file drawer[71]or a funnel plot analysis[70][72] may help detect underlying publication bias among the studies in the meta-analysis.

Outcome reporting bias

Meta-analyses in which a smaller proportion of included trials provide raw data for inclusion in the meta-analysis are more likely to be positive.[73] This may be due a bias against reporting negative results.[74]

Problems with meta-analyses

Obsolescence

The conclusions of meta-analyses may be mitigated by research published after the search date of the meta-analysis. This may occur by the time the meta-analysis has been published.[75][76] Strategies have been developed for updating meta-analyses.[77]

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