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Meta-analysis overview A practical guide ipdforest Summary A short guide and a forest plot command for one-stage meta-analysis in Stata the ipdforest command Evan Kontopantelis David Reeves Centre for Primary Care Institute of Population Health Faculty of Medicine University of Manchester RSS Annual Conference Telford, 5 Sep 2012 Kontopantelis, Reeves ipdforest

RSS 2012 - ipdforest

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A short guide and a forest plot command for one-stage meta-analysis in Stata: the ipdforest command

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Page 1: RSS 2012 - ipdforest

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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

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METHODS

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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)

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

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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

ipdforestSummary

A short guide and a forest plot command forone-stage meta-analysis in Stata

the ipdforest command

Evan Kontopantelis David Reeves

Centre for Primary CareInstitute of Population Health

Faculty of MedicineUniversity of Manchester

RSS Annual ConferenceTelford, 5 Sep 2012

Kontopantelis, Reeves ipdforest

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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

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METHODS

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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%)

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

ipdforestSummary

Outline

1 Meta-analysis overview

2 A practical guide

3 ipdforestmethodsexample

4 Summary

Kontopantelis, Reeves ipdforest

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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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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%)

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

ipdforestSummary

Timeline

‘Meta’ is a Greek preposition meaning ‘after’, someta-analysis =⇒ post-analysisEfforts to pool results from individual studies back as far as1904The first attempt that assessed a therapeutic interventionwas published in 1955In 1976 Glass first used the term to describe a "statisticalanalysis of a large collection of analysis results fromindividual studies for the purpose of integrating thefindings"

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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)

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

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Lorem

106 (50%) 101 (476%)

5 (24%)

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Nostrud: N Y

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1 (37%)

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Meta-analysis overviewA practical guide

ipdforestSummary

Meta-analysing reported study results

A two-stage processthe relevant summary effect statistics are extracted frompublished papers on the included studiesthese are then combined into an overall effect estimateusing a suitable meta-analysis model

However, problems often arisepapers do not report all the statistical information requiredas inputpapers report a statistic other than the effect size whichneeds to be transformed with a loss of precisiona study might be too different to be included (populationclinically heterogeneous)

Kontopantelis, Reeves ipdforest

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[Poster title]

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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.]

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

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

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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

ipdforestSummary

Individual Patient DataIPD

These problems can be avoided when IPD from eachstudy are available

outcomes can be easily standardisedclinical heterogeneity can be addressed with subgroupanalyses and patient-level covariate controlling

Can be analysed in a single- or two-stage processmixed-effects regression models can be used to combineinformation across studies in a single stagethis is currently the best approach, with the two-stagemethod being at best equivalent in certain scenarios

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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.]

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

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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

ipdforestSummary

Forest plot

One advantage of two-stage meta-analysis is the ability toconvey information graphically through a forest plot

study effects available after the first stage of the process,and can be used to demonstrate the relative strength of theintervention in each study and across allinformative, easy to follow and particularly useful forreaders with little or no methodological experiencekey feature of meta-analysis and always presented whentwo-stage meta-analyses are performed

In one-stage meta-analysis, only the overall effect iscalculated and creating a forest-plot is not straightforward

Enter ipdforest

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[Poster title]

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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.]

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

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

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

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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

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

26 (963%) 0

1 (37%)

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Meta-analysis overviewA practical guide

ipdforestSummary

The hypothetical study

Individual patient data from randomised controlled trialsFor each trial we have

a binary control/intervention membership variablebaseline and follow-up data for the continuous outcomecovariates

Assume measurements consistent across trials andstandardisation is not requiredWe will explore linear random-effects models with thextmixed command; application to the logistic case usingxtmelogit should be straightforwardIn the models that follow, in general, we denote fixedeffects with ‘γ’s and random effects with ‘β’s

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OBJECTIVE

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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

ipdforestSummary

Model 1fixed common intercept; random treatment effect; fixed effect for baseline

Yi j = γ0 + β1jgroupi j + γ2Ybi j + εi j εi j ∼ N(0, σ2j )

β1j = γ1 + u1j u1j ∼ N(0, τ12)

i : the patientj : the trialYi j : the outcomeγ0: fixed common interceptβ1j : random treatmenteffect for trial jγ1: mean treatment effectgroupi j : group membership

γ2: fixed baseline effectYbi j : baseline scoreu1j : random treatmenteffect for trial jτ1

2: between trial varianceεi j : error termσ2

j : within trial variance fortrial j

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[Poster title]

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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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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

ipdforestSummary

Model 1fixed common intercept; random treatment effect; fixed effect for baseline

Possibly the simplest approachIn Stata it can be expressed as

xtmixed Y i.group Yb || studyid:group,noconswherestudyid, the trial identifiergroup, control/intervention membershipY and Yb, endpoint and baseline scoresnote that the nocons option suppresses estimation of theintercept as a random effect

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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.]

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

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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

ipdforestSummary

Model 2fixed trial specific intercepts; random treatment effect; fixed trial-specific effects forbaseline

Common intercept & fixed baseline difficult to justifyA more accepted model allows for different fixed interceptsand fixed baseline effects for each trial:

Yi j = γ0 j + β1 jgroupi j + γ2 jYbi j + εi jβ1 j = γ1 + u1 jwhereγ0 j the fixed intercept for trial jγ2 j the fixed baseline effect for trial j

In Stata expressed as:xtmixed Y i.group i.studyid Yb1 Yb2 Yb3 Yb4|| studyid:group, noconswhere Yb‘i’=Yb if studyid=‘i’ and zero otherwise

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[Poster title]

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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.]

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

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

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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

ipdforestSummary

Model 3random trial intercept; random treatment effect; fixed trial-specific effects for baseline

Another possibility, althought contentious, is to assume trialintercepts are random (e.g. multi-centre trial):

Yi j = β0 j + β1 jgroupi j + γ2 jYbi j + εi jβ0 j = γ0 + u0 jβ1 j = γ1 + u1 jwiser to assume random effects correlation ρ 6= 0:εi j ∼ N(0, σ2

j ) u0 j ∼ N(0, τ20 )

u1 j ∼ N(0, τ21 ) cov(u0 j ,u1 j) = ρτ0τ1

In Stata expressed as:xtmixed Y i.group Yb1 Yb2 Yb3 Yb4 ||studyid:group, cov(uns)cov(uns): allows for distinct estimation of all REvariance-covariance components

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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.]

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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

ipdforestSummary

Model 4random trial intercept; random treatment effect; random effects for baseline

The baseline could also have been modelled as arandom-effect:

Yi j = β0 j + β1 jgroupi j + β2 jYbi j + εi jβ0 j = γ0 + u0 jβ1 j = γ1 + u1 jβ2 j = γ2 + u2 jas before, non-zero random effects correlations:

u0 j ∼ N(0, τ20 ) u1 j ∼ N(0, τ2

1 )u2 j ∼ N(0, τ2

2 ) cov(u0 j ,u1 j) = ρ1τ0τ1cov(u0 j ,u2 j) = ρ2τ0τ2 cov(u1 j ,u2 j) = ρ3τ1τ2

In Stata expressed as:xtmixed Y i.group Yb || studyid:group Yb,cov(uns)

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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.]

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RESULTS

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CONCLUSIONS

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RESULTS

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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%)

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Meta-analysis overviewA practical guide

ipdforestSummary

Model 5Interactions and covariates

A covariate or an interaction term can be modelled as afixed or random effectAssuming continuous and standardised variable age wecan expand Model 2 to include fixed effects for both ageand its interaction with the treatment:

Yi j = γ0 j +β1 jgroupi j +γ2 jYbi j +γ3agei j +γ4groupi jagei j +εi jβ1 j = γ1 + u1 j

In Stata expressed as:xtmixed Y i.group i.studyid Yb1 Yb2 Yb3 Yb4age i.group#c.age || studyid:group, nocons

If modelled as a random effect, non-convergence issuesmore likely to be encountered

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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

[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.]

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RESULTS

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CONCLUSIONS

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RESULTS

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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%)

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Meta-analysis overviewA practical guide

ipdforestSummary

methodsexample

General

ipdforest is issued following an IPD meta-analysis thatuses mixed effects two-level regression, with patientsnested within trials and a

linear model (xtmixed)orlogistic model (xtmelogit)

Provides a meta-analysis summary table and a forest plotTrial effects are calculated within ipdforest

Can calculate and report both main and interaction effectsOverall effect(s) and variance estimates are extracted fromthe preceding regression

Kontopantelis, Reeves ipdforest

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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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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

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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

ipdforestSummary

methodsexample

Process

ipdforest estimates individual trial effects and theirstandard errors using one-level linear or logisticregressionsFollowing xtmixed, regress is used and followingxtmelogit, logit is used, for each trialipdforest controls these regressions for fixed- orrandom-effects covariates that were specified in thepreceding two-level regressionUser has full control over included covariates in thecommand (e.g. specification as fixed- or random-effects)But we strongly recommend using the same specifications

Kontopantelis, Reeves ipdforest

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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.]

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RESULTS

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CONCLUSIONS

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

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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%)

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

ipdforestSummary

methodsexample

Estimation details

In the estimation of individual trial effects, ipdforestcontrols for a random-effects covariate (i.e. allowing theregression coefficient to vary by trial) by including thecovariate as an independent variable in each regressionControl for a fixed-effect covariate (regression coefficientassumed constant across trials and given by the coefficientestimated under two-level model) is a little more complex.

Not possible to specify a fixed value for a regressioncoefficient under regress and the continuous outcomevariable is adjusted by subtracting the contribution of thefixed covariates to its values in a first step prior to analysisFor a binary outcome the equivalent is achieved throughuse of the offset option in logit

Kontopantelis, Reeves ipdforest

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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.]

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RESULTS

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CONCLUSIONS

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

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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

ipdforestSummary

methodsexample

Heterogeneitypart I

Between-trial variability τ2 in the treatment effect, knownas heterogeneity, arises from differences in trial design,quality, outcomes or populationsFor continuous outcomes, ipdforest reports, I2 and H2

M ,based on the xtmixed outputFor binary outcomes, an estimate of the within-trialvariance is not reported under xtmelogit and henceheterogeneity measures cannot be computedBetween-trial variability estimate τ̂2 and its confidenceinterval is reported under both models.

Kontopantelis, Reeves ipdforest

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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

[Add title, if necessary.]

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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

ipdforestSummary

methodsexample

Heterogeneitypart II

We are not calculating an IPD version of Cochran’s Q, theorthodox χ2

k−1 homogeneity test, considering its poorperformance when the number of trials k is smallBesides, taking into account even low levels of τ2 byadopting a random-effects model is a more conservativeapproach than the fixed-effect oneWhen between-trial variance is estimated to be close tozero, results with the two approaches converge

Kontopantelis, Reeves ipdforest

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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

[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.]

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[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

ipdforestSummary

methodsexample

Depression intervention

We apply the ipdforest command to a dataset of 4depression intervention trialsComplete information in terms of age, gender,control/intervention group membership, continuousoutcome baseline and endpoint values for 518 patientsResults not published yet; we use fake author names andgenerated random continuous & binary outcome variables,while keeping the covariates at their actual valuesIntroduced correlation between baseline and endpointscores and between-trial variabilityLogistic IPD meta-analysis, followed by ipdforest

Kontopantelis, Reeves ipdforest

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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

[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.]

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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

ipdforestSummary

methodsexample

Dataset

. use ipdforest_example.dta,

. describe

Contains data from ipdforest_example.dtaobs: 518vars: 17 6 Feb 2012 11:14size: 20,202

storage display valuevariable name type format label variable label

studyid byte %22.0g stid Study identifierpatid int %8.0g Patient identifiergroup byte %20.0g grplbl Intervention/control groupsex byte %10.0g sexlbl Genderage float %10.0g Age in yearsdepB byte %9.0g Binary outcome, endpointdepBbas byte %9.0g Binary outcome, baselinedepBbas1 byte %9.0g Bin outcome baseline, trial 1depBbas2 byte %9.0g Bin outcome baseline, trial 2depBbas5 byte %9.0g Bin outcome baseline, trial 5depBbas9 byte %9.0g Bin outcome baseline, trial 9depC float %9.0g Continuous outcome, endpointdepCbas float %9.0g Continuous outcome, baselinedepCbas1 float %9.0g Cont outcome baseline, trial 1depCbas2 float %9.0g Cont outcome baseline, trial 2depCbas5 float %9.0g Cont outcome baseline, trial 5depCbas9 float %9.0g Cont outcome baseline, trial 9

Sorted by: studyid patid

Kontopantelis, Reeves ipdforest

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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

[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.]

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RESULTS

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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%)

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Meta-analysis overviewA practical guide

ipdforestSummary

methodsexample

Mixed effects logistic regression modelfixed trial intercepts; fixed trial effects for baseline; random treatment and age effects

. xtmelogit depB group agec sex i.studyid depBbas1 depBbas2 depBbas5 depBbas9 i> .group#c.agec || studyid:group agec, var nocons or

Mixed-effects logistic regression Number of obs = 518Group variable: studyid Number of groups = 4

Obs per group: min = 42avg = 129.5max = 214

Integration points = 7 Wald chi2(11) = 42.06Log likelihood = -326.55747 Prob > chi2 = 0.0000

depB Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]

group 1.840804 .3666167 3.06 0.002 1.245894 2.71978agec .9867902 .0119059 -1.10 0.270 .9637288 1.010403sex .7117592 .1540753 -1.57 0.116 .4656639 1.087912

studyid2 1.050007 .5725516 0.09 0.929 .3606166 3.0573035 .8014551 .5894511 -0.30 0.763 .189601 3.3877999 1.281413 .6886057 0.46 0.644 .4469619 3.673735

depBbas1 3.152908 1.495281 2.42 0.015 1.244587 7.987253depBbas2 4.480302 1.863908 3.60 0.000 1.982385 10.12574depBbas5 2.387336 1.722993 1.21 0.228 .5802064 9.823007depBbas9 1.881203 .7086507 1.68 0.093 .8990569 3.936262

group#c.agec1 1.011776 .0163748 0.72 0.469 .9801858 1.044385

_cons .5533714 .2398342 -1.37 0.172 .2366472 1.293993

Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]

studyid: Independentvar(group) 8.86e-21 2.43e-11 0 .var(agec) 5.99e-18 4.40e-11 0 .

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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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Meta-analysis overviewA practical guide

ipdforestSummary

methodsexample

ipdforestmodelling main effect and interaction

. ipdforest group, fe(sex) re(agec) ia(agec) or

One-stage meta-analysis results using xtmelogit (ML method) and ipdforestMain effect (group)

Study Effect [95% Conf. Interval] % Weight

Hart 2005 2.118 0.942 4.765 19.88Richards 2004 2.722 1.336 5.545 30.69Silva 2008 2.690 0.748 9.676 8.11Kompany 2009 1.895 0.969 3.707 41.31

Overall effect 1.841 1.246 2.720 100.00

One-stage meta-analysis results using xtmelogit (ML method) and ipdforestInteraction effect (group x agec)

Study Effect [95% Conf. Interval] % Weight

Hart 2005 0.972 0.901 1.049 19.88Richards 2004 0.995 0.937 1.055 30.69Silva 2008 0.987 0.888 1.098 8.11Kompany 2009 1.077 1.015 1.144 41.31

Overall effect 1.012 0.980 1.044 100.00

Heterogeneity Measures

value [95% Conf. Interval]

I^2 (%) .H^2 .tau^2 est 0.000 0.000 .

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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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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%)

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Nostrud: N Y

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Meta-analysis overviewA practical guide

ipdforestSummary

methodsexample

Forest plotsmain effect and interaction

Overall effect

Kompany 2009

Silva 2008

Richards 2004

Hart 2005

Stu

dies

0 2 3 4 5 6 7 8 9 101Effect sizes and CIs (ORs)

Main effect (group)

Overall effect

Kompany 2009

Silva 2008

Richards 2004

Hart 2005

Stu

dies

0 .2 .4 .6 .8 1.2 1.41Effect sizes and CIs (ORs)

Interaction effect (group x agec)

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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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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

ipdforestSummary

What to take home

A few different approaches exist for conducting one-stageIPD meta-analysisStata can cope through the xtmixed and the xtmelogitcommandsThe ipdforest command aims to help meta-analysts

calculate trial effectsdisplay results in standard meta-analysis tablesproduce familiar and ‘expected’ forest-plots

It can deal with main and all types of interaction effects(binary×continous, binary×binary, binary×categorical)

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OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

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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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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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Appendix Thank you!

Comments, suggestions:[email protected]

Kontopantelis, Reeves ipdforest