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METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

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METHODS

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

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7 (259%) 18 (667%)

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106 (50%) 101 (476%)

5 (24%)

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Nostrud: N Y

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Meta-analysis overviewA practical guide

Summary

Software and model selection challenges inmeta-analysis

MetaEasy, model assumptions and homogeneity

Evan Kontopantelis David Reeves

Health Sciences Primary Care Research CentreUniversity of Manchester

RSS Primary Care Study GroupErrol Street, 2 July 2012

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

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Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewA practical guide

Summary

Outline

1 Meta-analysis overviewThe heterogeneity issueMore challenges

2 A practical guidethe MetaEasy add-inmetaeff & metaanMethods and performanceτ̂2 = 0

3 Summary

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

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RESULTS

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CONCLUSIONS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

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Lorem

106 (50%) 101 (476%)

5 (24%)

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Nostrud: N Y

Unknown

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Meta-analysis overviewA practical guide

Summary

The heterogeneity issueMore challenges

HeterogeneityThe big bad wolf

When the effect of the intervention varies significantly fromone study to another.It can be attributed to clinical and/or methodologicaldiversity.

Clinical: variability that arises from different populations,interventions, outcomes and follow-up times.Methodological: relates to differences in trial design andquality.

Detecting quantifying and dealing with heterogeneity canbe very hard.

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

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RESULTS

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CONCLUSIONS

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

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Meta-analysis overviewA practical guide

Summary

The heterogeneity issueMore challenges

Absence of heterogeneity

Assumes that thetrue effects of thestudies are allequal anddeviations occurbecause ofimprecision ofresults.Analysed with thefixed-effectsmethod.

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewA practical guide

Summary

The heterogeneity issueMore challenges

Presence of heterogeneity

Assumes thatthere is variation inthe size of the trueeffect amongstudies (in additionto the imprecisionof results).Analysed withrandom-effectsmethods.

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

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ABSTRACT TITLE: [Add text here.]

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METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

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METHODS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewA practical guide

Summary

The heterogeneity issueMore challenges

Challenges with meta-analysis

Heterogeneity is common and the fixed-effect model isunder fire.Methods are asymptotic: accuracy improves as studiesincrease. But what if we only have a handful, as is usuallythe case?Almost all random-effects models (except ProfileLikelihood) do not take into account the uncertainty in τ̂2.Is this, practically, a problem?DerSimonian-Laird is the most common method ofanalysis, since it is easy to implement and widely available,but is it the best?

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

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CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewA practical guide

Summary

The heterogeneity issueMore challenges

Challenges with meta-analysis...continued

Can be difficult to organise since...outcomes likely to have been disseminated using a varietyof statistical parametersappropriate transformations to a common format requiredtedious task, requiring at least some statistical adeptness

Parametric random-effects models assume that both theeffects and errors are normally distributed. Are methodsrobust?Sometimes heterogeneity is estimated to be zero,especially when the number of studies is small. Goodnews?

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

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METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

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Meta-analysis overviewA practical guide

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceτ̂2 = 0

Based on our original work...

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

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METHODS: [Add text here.]

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RESULTS

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Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

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Meta-analysis overviewA practical guide

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceτ̂2 = 0

Organising

Data initially collected using data extraction forms.A spreadsheet is the next logical step to summarise thereported study outcomes and identify missing data.Since in most cases MS Excel will be used we developedan add-in that can help with most processes involved inmeta-analysis.More useful when the need to combine differently reportedoutcomes arises.

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

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METHODS: [Add text here.]

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

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RESULTS

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Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

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Meta-analysis overviewA practical guide

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceτ̂2 = 0

What it can do

Help with the data collection using pre-formattedworksheets.Its unique feature, which can be supplementary to othermeta-analysis software, is implementation of methods forcalculating effect sizes (& SEs) from different input types.For each outcome of each study...

it identifies which methods can be usedcalculates an effect size and its standard errorselects the most precise method for each outcome

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

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METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

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RESULTS

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CONCLUSIONS

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RESULTS

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

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Meta-analysis overviewA practical guide

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceτ̂2 = 0

What it can do...continued

Creates a forest plot that summarises all the outcomes,organised by study.Uses a variety of standard and advanced meta-analysismethods to calculate an overall effect.

a variety of options is available for selecting whichoutcome(s) are to be meta-analysed from each study

Plots the results in a second forest plot.Reports a variety of heterogeneity measures, includingCochran’s Q, I2, HM

2 and τ̂2 (and its estimated confidenceinterval under the Profile Likelihood method).

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

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METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

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RESULTS

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CONCLUSIONS

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

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Meta-analysis overviewA practical guide

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceτ̂2 = 0

Advantages

Free (provided Microsoft Excel is available).Easy to use and time saving.Extracted data from each study are easily accessible, canbe quickly edited or corrected and analysis repeated.Choice of many meta-analysis models, including someadvanced methods not currently available in other softwarepackages (e.g. Permutations, Profile Likelihood, REML).Unique forest plot that allows multiple outcomes per study.Effect sizes and standard errors can be exported for use inother meta-analysis software packages.

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

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RESULTS

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CONCLUSIONS

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RESULTS

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

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106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

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Meta-analysis overviewA practical guide

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceτ̂2 = 0

Installing

Latest version available from www.statanalysis.co.ukCompatible with Excel 2003, 2007 and 2010.Manual provided but also described in:

Kontopantelis E and Reeves D.MetaEasy: A Meta-Analysis Add-In for Microsoft Excel.Journal of Statistical Software, 30(7):1-25, 2009.

Play video clip

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

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METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

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CONCLUSIONS

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RESULTS

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Excepteur Sint Lkl

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(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

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2 (09%) 0

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5 (24%)

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Nostrud: N Y

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Meta-analysis overviewA practical guide

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceτ̂2 = 0

Stata implementation

MetaEasy methods implemented in Stata under:metaeff, which uses the different study input to provideeffect sizes and SEsmetaan, which meta-analyses the study effects with afixed-effect or one of five available random-effects models

To install, type in Stata:ssc install <command name>help <command name>

Described in:Kontopantelis E and Reeves D.metaan: Random-effects meta-analysis.The Stata Journal, 10(3):395-407, 2010.

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

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METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

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RESULTS

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CONCLUSIONS

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RESULTS

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(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewA practical guide

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceτ̂2 = 0

Many random-effects methodswhich to use?

DerSimonian-Laird (DL): Moment-based estimator of bothwithin and between-study variance.Maximum Likelihood (ML): Improves the variance estimateusing iteration.Restricted Maximum Likelihood (REML): an ML variationthat uses a likelihood function calculated from atransformed set of data.Profile Likelihood (PL): A more advanced version of MLthat uses nested iterations for converging.Permutations method (PE): Simulates the distribution ofthe overall effect using the observed data.

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

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RESULTS

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CONCLUSIONS

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewA practical guide

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceτ̂2 = 0

Performance evaluationour approach

Simulated various distributions for the trueeffects:

Normal.Skew-Normal.Uniform.Bimodal.

Created datasets of 10,000meta-analyses for various numbers ofstudies and different degrees ofheterogeneity, for each distribution.

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

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RESULTS

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CONCLUSIONS

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[Graphic title]

RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewA practical guide

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceτ̂2 = 0

Performance evaluationour approach

Compared all methods in terms of:Coverage, the rate of true negatives when the overall trueeffect is zero.Power, the rate of true positives when the true overall effectis non-zero.Confidence Interval performance, a measure of how widethe (estimated around the effect) CI is, compared to its truewidth.

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

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RESULTS

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CONCLUSIONS

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[Graphic title]

RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewA practical guide

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceτ̂2 = 0

HomogeneityZero between study variance

0.75

0.80

0.85

0.90

0.95

1.00

%

2 5 8 11 14 17 20 23 26 29 32 35number of studies

FE DL MLREML PL PE

zero between study varianceCoverage

0.50

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0.90

1.00

%

2 5 8 11 14 17 20 23 26 29 32 35number of studies

FE DL MLREML PL PE

zero between study variancePower (25th centile)

0.40

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1.00

1.20

1.40

1.60

1.80

2.00

2 5 8 11 14 17 20 23 26 29 32 35number of studies

FE DL MLREML PL PE

zero between study varianceOverall estimation performance

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

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RESULTS

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[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

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[Graphic title]

RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewA practical guide

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceτ̂2 = 0

Coverage performanceSmall and large heterogeneity under various distributional assumptions

0.75

0.80

0.85

0.90

0.95

1.00

%

2 5 8 11 14 17 20 23 26 29 32 35number of studies

FE DL MLREML PL PE

sk=0, ku=3

H²=1.18 − I²=15% (normal distribution)Coverage

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1.00

%

2 5 8 11 14 17 20 23 26 29 32 35number of studies

FE DL MLREML PL PE

sk=2, ku=9

H²=1.18 − I²=15% (skew−normal distribution)Coverage

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0.95

1.00

%

2 5 8 11 14 17 20 23 26 29 32 35number of studies

FE DL MLREML PL PE

sk1=0, ku1=3, var1=.1, sk2=0, ku2=3, var2=.1

H=1.18 − I=15% (bimodal distribution)Coverage

0.75

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1.00

%

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FE DL MLREML PL PE

H=1.18 − I=15% (uniform distribution)Coverage

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1.00

%

2 5 8 11 14 17 20 23 26 29 32 35number of studies

FE DL MLREML PL PE

sk=0, ku=3

H²=2.78 − I²=64% (normal distribution)Coverage

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1.00

%

2 5 8 11 14 17 20 23 26 29 32 35number of studies

FE DL MLREML PL PE

sk=2, ku=9

H²=2.78 − I²=64% (skew−normal distribution)Coverage

0.75

0.80

0.85

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1.00

%

2 5 8 11 14 17 20 23 26 29 32 35number of studies

FE DL MLREML PL PE

sk1=0, ku1=3, var1=.1, sk2=0, ku2=3, var2=.1

H=2.78 − I=64% (bimodal distribution)Coverage

0.75

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0.95

1.00

%

2 5 8 11 14 17 20 23 26 29 32 35number of studies

FE DL MLREML PL PE

H=2.78 − I=64% (uniform distribution)Coverage

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

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RESULTS

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CONCLUSIONS

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

[Add title, if necessary.]

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Meta-analysis overviewA practical guide

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceτ̂2 = 0

Power performanceSmall and large heterogeneity under various distributional assumptions

0.50

0.60

0.70

0.80

0.90

1.00

%

2 5 8 11 14 17 20 23 26 29 32 35number of studies

FE DL MLREML PL PE

sk=0, ku=3

H²=1.18 − I²=15% (normal distribution)Power (25th centile)

0.50

0.60

0.70

0.80

0.90

1.00

%

2 5 8 11 14 17 20 23 26 29 32 35number of studies

FE DL MLREML PL PE

sk=2, ku=9

H²=1.18 − I²=15% (skew−normal distribution)Power (25th centile)

0.50

0.60

0.70

0.80

0.90

1.00

%

2 5 8 11 14 17 20 23 26 29 32 35number of studies

FE DL MLREML PL PE

sk1=0, ku1=3, var1=.1, sk2=0, ku2=3, var2=.1

H=1.18 − I=15% (bimodal distribution)Power (25th centile)

0.50

0.60

0.70

0.80

0.90

1.00

%

2 5 8 11 14 17 20 23 26 29 32 35number of studies

FE DL MLREML PL PE

H=1.18 − I=15% (uniform distribution)Power (25th centile)

0.50

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0.70

0.80

0.90

1.00

%

2 5 8 11 14 17 20 23 26 29 32 35number of studies

FE DL MLREML PL PE

sk=0, ku=3

H²=2.78 − I²=64% (normal distribution)Power (25th centile)

0.50

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0.90

1.00

%

2 5 8 11 14 17 20 23 26 29 32 35number of studies

FE DL MLREML PL PE

sk=2, ku=9

H²=2.78 − I²=64% (skew−normal distribution)Power (25th centile)

0.50

0.60

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0.80

0.90

1.00

%

2 5 8 11 14 17 20 23 26 29 32 35number of studies

FE DL MLREML PL PE

sk1=0, ku1=3, var1=.1, sk2=0, ku2=3, var2=.1

H=2.78 − I=64% (bimodal distribution)Power (25th centile)

0.50

0.60

0.70

0.80

0.90

1.00

%

2 5 8 11 14 17 20 23 26 29 32 35number of studies

FE DL MLREML PL PE

H=2.78 − I=64% (uniform distribution)Power (25th centile)

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

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[Sub-bullet] [Sub-bullet]

[Add title, if necessary.]

[Add key point.]

[Sub-bullet] [Sub-bullet]

[Add key point.]

[Sub-bullet] [Sub-bullet]

RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Graphic title]

RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

[Add title, if necessary.]

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Meta-analysis overviewA practical guide

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceτ̂2 = 0

CI performanceSmall and large heterogeneity under various distributional assumptions

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1.80

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2 5 8 11 14 17 20 23 26 29 32 35number of studies

FE DL MLREML PL PE

sk=0, ku=3

H²=1.18 − I²=15% (normal distribution)Confidence Interval performance

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FE DL MLREML PL PE

sk=2, ku=9

H²=1.18 − I²=15% (skew−normal distribution)Confidence Interval performance

0.40

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0.80

1.00

1.20

1.40

1.60

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2.00

2 5 8 11 14 17 20 23 26 29 32 35number of studies

FE DL MLREML PL PE

sk1=0, ku1=3, var1=.1, sk2=0, ku2=3, var2=.1

H=1.18 − I=15% (bimodal distribution)Overall estimation performance (medians)

0.40

0.60

0.80

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1.40

1.60

1.80

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2 5 8 11 14 17 20 23 26 29 32 35number of studies

FE DL MLREML PL PE

H=1.18 − I=15% (uniform distribution)Overall estimation performance

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1.80

2.00

2 5 8 11 14 17 20 23 26 29 32 35number of studies

FE DL MLREML PL PE

sk=0, ku=3

H²=2.78 − I²=64% (normal distribution)Confidence Interval performance

0.40

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0.80

1.00

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1.80

2.00

2 5 8 11 14 17 20 23 26 29 32 35number of studies

FE DL MLREML PL PE

sk=2, ku=9

H²=2.78 − I²=64% (skew−normal distribution)Confidence Interval performance

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

2.00

2 5 8 11 14 17 20 23 26 29 32 35number of studies

FE DL MLREML PL PE

sk1=0, ku1=3, var1=.1, sk2=0, ku2=3, var2=.1

H=2.78 − I=64% (bimodal distribution)Overall estimation performance (medians)

0.40

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FE DL MLREML PL PE

H=2.78 − I=64% (uniform distribution)Overall estimation performance

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

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RESULTS

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[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

[Add key point.]

[Add key point.]

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[Graphic title]

RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewA practical guide

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceτ̂2 = 0

Coverage by methodLarge heterogeneity across various between-study variance distributions

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1.00

%

2 5 8 11 14 17 20 23 26 29 32 35number of studies

Zero variance Normal Skew−normal

Bimodal Uniform

H=2.78 − I=64%FE − Coverage

0.80

0.85

0.90

0.95

1.00

%

2 5 8 11 14 17 20 23 26 29 32 35number of studies

Zero variance Normal Skew−normal

Bimodal Uniform

H=2.78 − I=64%DL − Coverage

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1.00

%2 5 8 11 14 17 20 23 26 29 32 35

number of studies

Zero variance Normal Skew−normal

Bimodal Uniform

H=2.78 − I=64%ML − Coverage

0.80

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0.90

0.95

1.00

%

2 5 8 11 14 17 20 23 26 29 32 35number of studies

Zero variance Normal Skew−normal

Bimodal Uniform

H=2.78 − I=64%REML − Coverage

0.80

0.85

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0.95

1.00

%

2 5 8 11 14 17 20 23 26 29 32 35number of studies

Zero variance Normal Skew−normal

Bimodal Uniform

H=2.78 − I=64%PL − Coverage

0.90

0.95

1.00

%

2 5 8 11 14 17 20 23 26 29 32 35number of studies

Zero variance Normal Skew−normal

Bimodal Uniform

H=2.78 − I=64%PE − Coverage

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

Heal

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cien

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Grou

p

[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

[Add key point.]

[Sub-bullet] [Sub-bullet]

[Add title, if necessary.]

[Add key point.]

[Sub-bullet] [Sub-bullet]

[Add key point.]

[Sub-bullet] [Sub-bullet]

RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Graphic title]

RESULTS

[Graphic title] [Graphic title]

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewA practical guide

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceτ̂2 = 0

Power by methodLarge heterogeneity across various between-study variance distributions

0.20

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%

2 5 8 11 14 17 20 23 26 29 32 35number of studies

Zero variance Normal Skew−normal

Bimodal Uniform

H=2.78 − I=64%FE − Power (25th centile)

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Zero variance Normal Skew−normal

Bimodal Uniform

H=2.78 − I=64%DL − Power (25th centile)

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%2 5 8 11 14 17 20 23 26 29 32 35

number of studies

Zero variance Normal Skew−normal

Bimodal Uniform

H=2.78 − I=64%ML − Power (25th centile)

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%

2 5 8 11 14 17 20 23 26 29 32 35number of studies

Zero variance Normal Skew−normal

Bimodal Uniform

H=2.78 − I=64%REML − Power (25th centile)

0.00

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Zero variance Normal Skew−normal

Bimodal Uniform

H=2.78 − I=64%PL − Power (25th centile)

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2 5 8 11 14 17 20 23 26 29 32 35number of studies

Zero variance Normal Skew−normal

Bimodal Uniform

H=2.78 − I=64%PE − Power (25th centile)

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

Heal

th S

cien

ces P

rimar

y Ca

re R

esea

rch

Grou

p

[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

[Add key point.]

[Sub-bullet] [Sub-bullet]

[Add title, if necessary.]

[Add key point.]

[Sub-bullet] [Sub-bullet]

[Add key point.]

[Sub-bullet] [Sub-bullet]

RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Graphic title]

RESULTS

[Graphic title] [Graphic title]

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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]

For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

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[Add title, if necessary.]

[Add key point.]

[Sub-bullet] [Sub-bullet]

[Add key point.]

[Sub-bullet] [Sub-bullet]

Meta-analysis overviewA practical guide

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceτ̂2 = 0

CI performance by methodLarge heterogeneity across various between-study variance distributions

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

2.00

2 5 8 11 14 17 20 23 26 29 32 35number of studies

Zero variance Normal Skew−normal

Bimodal Uniform

H=2.78 − I=64%FE − Overall estimation performance

0.40

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0.80

1.00

1.20

1.40

1.60

1.80

2.00

2 5 8 11 14 17 20 23 26 29 32 35number of studies

Zero variance Normal Skew−normal

Bimodal Uniform

H=2.78 − I=64%DL − Overall estimation performance

0.40

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1.00

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2.00

2 5 8 11 14 17 20 23 26 29 32 35number of studies

Zero variance Normal Skew−normal

Bimodal Uniform

H=2.78 − I=64%ML − Overall estimation performance

0.40

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1.00

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2.00

2 5 8 11 14 17 20 23 26 29 32 35number of studies

Zero variance Normal Skew−normal

Bimodal Uniform

H=2.78 − I=64%REML − Overall estimation performance

0.40

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2 5 8 11 14 17 20 23 26 29 32 35number of studies

Zero variance Normal Skew−normal

Bimodal Uniform

H=2.78 − I=64%PL − Overall estimation performance

0.40

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2 5 8 11 14 17 20 23 26 29 32 35number of studies

Zero variance Normal Skew−normal

Bimodal Uniform

H=2.78 − I=64%PE − Overall estimation performance

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

Heal

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p

[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

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[Sub-bullet] [Sub-bullet]

[Add title, if necessary.]

[Add key point.]

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[Add key point.]

[Sub-bullet] [Sub-bullet]

RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Graphic title]

RESULTS

[Graphic title] [Graphic title]

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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]

For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

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[Add key point.]

[Sub-bullet] [Sub-bullet]

[Add key point.]

[Sub-bullet] [Sub-bullet]

Meta-analysis overviewA practical guide

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceτ̂2 = 0

Which method then?

Within any given method, the results were consistentacross all types of distribution shape.Therefore methods are highly robust against even severeviolations of the assumption of normality.Choose PE if the priority is an accurate Type I error rate(false positive).But low power makes it a poor choice when control of theType II error rate (false negative) is also important and itcannot be used with less than 6 studies.

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

Heal

th S

cien

ces P

rimar

y Ca

re R

esea

rch

Grou

p

[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

[Add key point.]

[Sub-bullet] [Sub-bullet]

[Add title, if necessary.]

[Add key point.]

[Sub-bullet] [Sub-bullet]

[Add key point.]

[Sub-bullet] [Sub-bullet]

RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Graphic title]

RESULTS

[Graphic title] [Graphic title]

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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]

For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

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Meta-analysis overviewA practical guide

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceτ̂2 = 0

Which method then?

For very small study numbers (≤5) only PL gives coverage>90% and an acccurate CI.PL has a ‘reasonable’ coverage in most situations,especially for moderate and large heterogeneiry, giving itan edge over other methods.REML and DL perform similarly and better than PL onlywhen heterogeneity is low (I2 < 15%)The computational complexity of REML is not justified.

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

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Excepteur Sint Lkl

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(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

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106 (50%) 101 (476%)

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Meta-analysis overviewA practical guide

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceτ̂2 = 0

Bring on the champagne?

Does not necessarily mean homogeneity.Most methods use biased estimators and not uncommonto get a negative τ̂2 which is set to 0 by the model.We identified a large percentage of cases where theestimators failed to identify existing heterogeneity.In our simulations, for 5 studies and I2 ≈ 29%:

30% of the meta-analyses were erroneously estimated tobe homogeneous under the DL method.32% for REML and 48% for ML-PL.

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

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7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

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Lorem

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5 (24%)

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Unknown

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Meta-analysis overviewA practical guide

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceτ̂2 = 0

What does it mean?

In these cases coverage was substandard and was over10% lower than in cases where τ̂2 > 0, on average.The problem becomes less profound as the number ofstudies and the level of heterogeneity increase.Better estimators are needed.There might be a large number of meta-analyses of‘homogeneous’ studies which have reached a wrongconclusion.

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

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[Poster title]

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

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7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

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Lorem

106 (50%) 101 (476%)

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Nostrud: N Y

Unknown

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26 (963%) 0

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Meta-analysis overviewA practical guide

Summary

What to take home

MetaEasy can help you organise your meta-analysis andcan be especially useful if you need to combine continuousand binary outcomes.Methods implemented in Stata under metaeff and metaan.A zero τ̂2 is a reason to worry. Heterogeneity might bethere but we cannot measure or account for in the model.If τ̂2 > 0, even if very small, use a random-effects model.The DL method works reasonably well, under alldistributions, especially for low levels of heterogeneity.Profile likelihood, which takes into account the uncertaintlyin τ̂2, works better when I2 ≥ 15%.

Kontopantelis, Reeves Software and model selection challenges in meta-analysis

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

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26 (963%) 0

1 (37%)

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AppendixThank you!Key references

Comments, suggestions:e.kontopantelis@manchester.ac.uk

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

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Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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AppendixThank you!Key references

References

Kontopantelis E, Reeves D.MetaEasy: A Meta-Analysis Add-In for Microsoft Excel.Journal of Statistical Software, 30(7):1-25, 2009.

Kontopantelis E, Reeves D.metaan: Random-effects meta-analysis.The Stata Journal, 10(3):395-407, 2010.

Kontopantelis E, Reeves D.Performance of statistical methods for meta-analysis whentrue study effects are non-normally distributed: A simulationstudy.Stat Methods Med Res, published online Dec 9 2010.

Kontopantelis, Reeves Software and model selection challenges in meta-analysis