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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 }}</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 }}</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 }}</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 }}</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 examine relationships between predictor and outcome variables including non-linear relationships.<ref name="pmid1824642">{{cite journal| author=Farnett L, Mulrow CD, Linn WD, Lucey CR, Tuley MR| title=The J-curve phenomenon and the treatment of hypertension. Is there a point beyond which pressure reduction is dangerous? | journal=JAMA | year= 1991 | volume= 265 | issue= 4 | pages= 489-95 | pmid=1824642 | doi= | pmc= | url= }} </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>
 
Meta-regression can 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 }}</ref>


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)."<ref name="CochraneHandbook>The Cochrane Collaboration. [http://www.cochrane-handbook.org/ Cochrane Handbook]</ref>
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)."<ref name="CochraneHandbook>The Cochrane Collaboration. [http://www.cochrane-handbook.org/ Cochrane Handbook]</ref>

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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

Measuring consistency of study results

Consistency can be statistically tested using either the Cochran's Q or I2.[27][28] The I2 is the "percentage of total variation across studies that is due to heterogeneity rather than chance."[27] 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[29][30].

The following has been proposed for interpreting I2:[27]

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

or according to the Handbook of the Cochrane Collaboration:[31]

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

However, I2, even when the value is 0%, can be misleading if the confidence intervals around the value are not provided.[32][33]

Statistical methods exist for assessing the importance of subgroups.[34]

Comparing rates of dichotomous outcomes

Studies are usually statistically combined by a method such as the DerSimonian and Laird.[35] 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.[36][37] An alternative is to use a continuity correction.[38] Rather than using a constant continuity correction, less bias may occur by correcting with either[39]

  • "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:[36]

  • 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

Comparing rates of continuous outcomes

The standardized mean difference (SMD) is used. In the interpretation of SMD, 0.2 represents a small effect, 0.5 a moderate effect, and 0.8 a large effect.[40][41]

Subgroup analysis

There are two types of interactions:[42]

  • 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.[43]

If the subgrouping does not account for all heterogeneity, interaction can be tested with meta-regression to avoid false-positive results.[43][44] Metagression is detailed in a section below.

Software

Software available for meta-analysis includes:[45]

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[47]

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[48] and have shown the delay in adoption of evidence by experts[49].

Cumulative meta-analyses may be prone to false positive results due to repeated tests of statistical significance.[50]

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."[51]

Standards exist for conduct and reporting.[52]

Individual patient data meta-analysis (IPD meta-analysis) may have more long lasting results than other meta-analyses.[53]

Meta-regression

Meta-regression allows simultaneous comparison of multiple sources of heterogeneity.[54][55][56][57]

Meta-regression can examine relationships between predictor and outcome variables including non-linear relationships.[58]

Meta-regression can analyze subgroups.[43]A permutation test may reduce the chance of a false positive subgroup analysis.[44]

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[59] of the R programming language as described by Everitt and Hothorn[60][61].

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[62] and Bayesian hierarchical models[63] pool studies in order to compare to treatments that have not been directly compared.[64] Network meta-analyses are commonly not well performed[65]and can have misleading conclusions.[66][67][68] Network meta-analyses have been conducted by the Cochrane Collaboration.[69][70]

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.[71][72] The traditional summary receiver operating characteristic curve (SROC curve) should be replaced by either the hierarchical summary receiver operating characteristic curve(HSROC curve).[72][73] or bivariate random-effects model.[74] Discussions of HSROC and bivariate random-effects meta-analysis are available.[75][74] An example of a meta-analysis using bivariate mixed-effects binomial regression model is available.[76] Examples of using the HSROC and diagnostic odds ratio are available.[77]

Factors associated with higher quality meta-analyses

Meta-analyses by the Cochrane Collaboration tend to be of higher quality.[78]

Individual data meta-analyses, in which the records from individual patients are pooled together into one dataset, tend to have more stable conclusions.[53]

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.[79]

Small study effect and publication bias

The small study effect is the observation that small studies tend to report more positive results.[80][81] This is especially a threat when the original studies in a meta-analysis are less than 50 patients in size.[82]

Publication bias against negative studies is part of the small study effect and may threaten the validity of meta-analyses that are positive and all the studies included within the meta-analysis are small.[83][84]

In performing a meta-analysis, a file drawer[85]or a funnel plot analysis[84][86] 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.[87] This may be due a bias against reporting negative results.[88]

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.[89][90] Strategies have been developed for updating meta-analyses.[91]

References

  1. ↑ National Library of Medicine. Meta-analysis. Retrieved on 2007-12-06.
  2. ↑ 2.0 2.1 Moher D, Liberati A, Tetzlaff J, Altman DG (July 2009). "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement". Ann. Intern. Med.. PMID 19622511. [e] Cite error: Invalid <ref> tag; name "pmid19622511" defined multiple times with different content
  3. ↑ 3.0 3.1 LeLorier J, Grégoire G, Benhaddad A, Lapierre J, Derderian F (August 1997). "Discrepancies between meta-analyses and subsequent large randomized, controlled trials". N. Engl. J. Med. 337 (8): 536–42. PMID 9262498. [e] Cite error: Invalid <ref> tag; name "pmid9262498" defined multiple times with different content
  4. ↑ Villar J, Carroli G, Belizán JM (March 1995). "Predictive ability of meta-analyses of randomised controlled trials". Lancet 345 (8952): 772–6. PMID 7891492. [e]
  5. ↑ Cappelleri JC, Ioannidis JP, Schmid CH, et al (1996). "Large trials vs meta-analysis of smaller trials: how do their results compare?". JAMA 276 (16): 1332–8. PMID 8861993. [e]
  6. ↑ Ioannidis JP, Cappelleri JC, Lau J (April 1998). "Issues in comparisons between meta-analyses and large trials". JAMA 279 (14): 1089–93. PMID 9546568. [e]
  7. ↑ 7.0 7.1 The Cochrane Collaboration. Cochrane Handbook
  8. ↑ Royle P, Milne R (2003). "Literature searching for randomized controlled trials used in Cochrane reviews: rapid versus exhaustive searches.". Int J Technol Assess Health Care 19 (4): 591-603. PMID 15095765.
  9. ↑ Stevinson C, Lawlor DA (2004). "Searching multiple databases for systematic reviews: added value or diminishing returns?". Complement Ther Med 12 (4): 228-32. DOI:10.1016/j.ctim.2004.09.003. PMID 15649836. Research Blogging.
  10. ↑ Fergusson D, Laupacis A, Salmi LR, McAlister FA, Huet C (2000). "What should be included in meta-analyses? An exploration of methodological issues using the ISPOT meta-analyses.". Int J Technol Assess Health Care 16 (4): 1109-19. PMID 11155831.
  11. ↑ Hopewell S, Clarke M, Lefebvre C, Scherer R (2007). "Handsearching versus electronic searching to identify reports of randomized trials.". Cochrane Database Syst Rev (2): MR000001. DOI:10.1002/14651858.MR000001.pub2. PMID 17443625. Research Blogging.
  12. ↑ Lemeshow AR, Blum RE, Berlin JA, Stoto MA, Colditz GA (2005). "Searching one or two databases was insufficient for meta-analysis of observational studies.". J Clin Epidemiol 58 (9): 867-73. DOI:10.1016/j.jclinepi.2005.03.004. PMID 16085190. Research Blogging.
  13. ↑ Avenell A, Handoll HH, Grant AM (2001). "Lessons for search strategies from a systematic review, in The Cochrane Library, of nutritional supplementation trials in patients after hip fracture.". Am J Clin Nutr 73 (3): 505-10. PMID 11237924.
  14. ↑ Crumley ET, Wiebe N, Cramer K, Klassen TP, Hartling L (2005). "Which resources should be used to identify RCT/CCTs for systematic reviews: a systematic review.". BMC Med Res Methodol 5: 24. DOI:10.1186/1471-2288-5-24. PMID 16092960. PMC PMC1232852. Research Blogging.
  15. ↑ Wilkins T, Gillies RA, Davies K (2005). "EMBASE versus MEDLINE for family medicine searches: can MEDLINE searches find the forest or a tree?". Can Fam Physician 51: 848-9. PMID 16926954. PMC PMC1479531.
  16. ↑ Savoie I, Helmer D, Green CJ, Kazanjian A (2003). "Beyond Medline: reducing bias through extended systematic review search.". Int J Technol Assess Health Care 19 (1): 168-78. PMID 12701949.
  17. ↑ Whiting P, Westwood M, Burke M, Sterne J, Glanville J (2008). "Systematic reviews of test accuracy should search a range of databases to identify primary studies.". J Clin Epidemiol 61 (4): 357-364. DOI:10.1016/j.jclinepi.2007.05.013. PMID 18313560. Research Blogging.
  18. ↑ Hopewell S, McDonald S, Clarke M, Egger M (2007). "Grey literature in meta-analyses of randomized trials of health care interventions.". Cochrane Database Syst Rev (2): MR000010. DOI:10.1002/14651858.MR000010.pub3. PMID 17443631. Research Blogging.
  19. ↑ McAuley L, Pham B, Tugwell P, Moher D (2000). "Does the inclusion of grey literature influence estimates of intervention effectiveness reported in meta-analyses?". Lancet 356 (9237): 1228-31. DOI:10.1016/S0140-6736(00)02786-0. PMID 11072941. Research Blogging.
  20. ↑ Sampson M, McGowan J, Tetzlaff J, Cogo E, Moher D (2008). "No consensus exists on search reporting methods for systematic reviews.". J Clin Epidemiol 61 (8): 748-54. DOI:10.1016/j.jclinepi.2007.10.009. PMID 18586178. Research Blogging.
  21. ↑ Egger M, Smith GD (1998). "Bias in location and selection of studies.". BMJ 316 (7124): 61-6. PMID 9451274. PMC PMC2665334.
  22. ↑ Cook DJ, Reeve BK, Guyatt GH, Heyland DK, Griffith LE, Buckingham L et al. (1996 Jan 24-31). "Stress ulcer prophylaxis in critically ill patients. Resolving discordant meta-analyses.". JAMA 275 (4): 308-14. PMID 8544272.
  23. ↑ Goodman SN (2002). "The mammography dilemma: a crisis for evidence-based medicine?". Ann Intern Med 137 (5 Part 1): 363-5. PMID 12204023.
  24. ↑ Slavin RE (January 1995). "Best evidence synthesis: an intelligent alternative to meta-analysis". J Clin Epidemiol 48 (1): 9–18. PMID 7853053. [e]
  25. ↑ Higgins JPT, Green S (editors). Table 8.5.a: The Cochrane Collaboration's Tool for assessing risk of bias. Cochrane Handbook for Systematic Reviews of Interventions Version 5.0.1 [updated September 2008]. The Cochrane Collaboration, 2008. Available from www.cochrane-handbook.org.
  26. ↑ Jadad AR, Moore RA, Carroll D, et al (1996). "Assessing the quality of reports of randomized clinical trials: is blinding necessary?". Control Clin Trials 17 (1): 1–12. DOI:10.1016/0197-2456(95)00134-4. PMID 8721797. Research Blogging.
  27. ↑ 27.0 27.1 27.2 Higgins JP, Thompson SG, Deeks JJ, Altman DG (September 2003). "Measuring inconsistency in meta-analyses". BMJ 327 (7414): 557–60. DOI:10.1136/bmj.327.7414.557. PMID 12958120. PMC 192859. Research Blogging.
  28. ↑ Higgins JP, Thompson SG (2002). "Quantifying heterogeneity in a meta-analysis.". Stat Med 21 (11): 1539-58. DOI:10.1002/sim.1186. PMID 12111919. Research Blogging.
  29. ↑ Fleiss JL (December 1986). "Analysis of data from multiclinic trials". Control Clin Trials 7 (4): 267–75. PMID 3802849. [e]
  30. ↑ Dickersin K, Berlin JA (1992). "Meta-analysis: state-of-the-science". Epidemiol Rev 14: 154–76. PMID 1289110. [e]
  31. ↑ Higgins JPT, Green S:Cochrane Handbook for Systematic Reviews of Interventions. Cochrane Collaboration (2008). Retrieved on 2008-10-23.
  32. ↑ Ioannidis JP, Patsopoulos NA, Evangelou E (2007). "Uncertainty in heterogeneity estimates in meta-analyses.". BMJ 335 (7626): 914-6. DOI:10.1136/bmj.39343.408449.80. PMID 17974687. Research Blogging.
  33. ↑ Ioannidis JP (2008). "Interpretation of tests of heterogeneity and bias in meta-analysis.". J Eval Clin Pract 14 (5): 951-7. DOI:10.1111/j.1365-2753.2008.00986.x. PMID 19018930. Research Blogging.
  34. ↑ Altman DG, Bland JM (2003). "Interaction revisited: the difference between two estimates.". BMJ 326 (7382): 219. PMID 12543843. PMC PMC1125071.
  35. ↑ DerSimonian R, Laird N (1986). "Meta-analysis in clinical trials.". Control Clin Trials 7 (3): 177-88. DOI:10.1016/0197-2456(86)90046-2. PMID 3802833. Research Blogging.
  36. ↑ 36.0 36.1 Diamond GA, Bax L, Kaul S (October 2007). "Uncertain effects of rosiglitazone on the risk for myocardial infarction and cardiovascular death". Ann. Intern. Med. 147 (8): 578–81. PMID 17679700. [e]
  37. ↑ Friedrich JO, Adhikari NK, Beyene J (2007). "Inclusion of zero total event trials in meta-analyses maintains analytic consistency and incorporates all available data.". BMC Med Res Methodol 7: 5. DOI:10.1186/1471-2288-7-5. PMID 17244367. PMC PMC1783664. Research Blogging.
  38. ↑ Bradburn MJ, Deeks JJ, Berlin JA, Russell Localio A (2007). "Much ado about nothing: a comparison of the performance of meta-analytical methods with rare events.". Stat Med 26 (1): 53-77. DOI:10.1002/sim.2528. PMID 16596572. Research Blogging.
  39. ↑ Sweeting MJ, Sutton AJ, Lambert PC (2004). "What to add to nothing? Use and avoidance of continuity corrections in meta-analysis of sparse data.". Stat Med 23 (9): 1351-75. DOI:10.1002/sim.1761. PMID 15116347. Research Blogging.
  40. ↑ 12.6.2 Re-expressing SMDs using rules of thumb for effect sizes in Cochrane Handbook
  41. ↑ Cohen J. Statistical Power Analysis in the Behavioral Sciences (2nd edition). Hillsdale (NJ): Lawrence Erlbaum Associates, Inc., 1988. isbn 0805802835
  42. ↑ Higgins JPT, Green S (editors). 9.6.2 What are subgroup analyses? 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.
  43. ↑ 43.0 43.1 43.2 Higgins JPT, Green S (editors). 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. Cite error: Invalid <ref> tag; name "Cochrane Handbook 9.6.3.1" defined multiple times with different content Cite error: Invalid <ref> tag; name "Cochrane Handbook 9.6.3.1" defined multiple times with different content
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