Post on 10-May-2015
description
Heal
th S
cien
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rimar
y Ca
re R
esea
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Grou
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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
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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
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
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
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[Add key point.] [Add description of key point.]
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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
ipdforestSummary
Outline
1 Meta-analysis overview
2 A practical guide
3 ipdforestmethodsexample
4 Summary
Kontopantelis, Reeves ipdforest
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.]
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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
[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%)
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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"
Kontopantelis, Reeves ipdforest
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.]
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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
[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%)
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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
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.]
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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
[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%)
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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
Kontopantelis, Reeves ipdforest
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.]
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RESULTS
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CONCLUSIONS
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[Graphic title]
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
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
Kontopantelis, Reeves ipdforest
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.]
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RESULTS
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CONCLUSIONS
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[Graphic title]
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
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
Kontopantelis, Reeves ipdforest
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.]
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RESULTS
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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
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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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
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
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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 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
Kontopantelis, Reeves ipdforest
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Grou
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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
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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
Kontopantelis, Reeves ipdforest
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Grou
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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.]
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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)
Kontopantelis, Reeves ipdforest
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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.]
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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
Kontopantelis, Reeves ipdforest
Heal
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y Ca
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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.]
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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
Heal
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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.]
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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
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
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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
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.]
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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
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.]
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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
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.]
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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
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.]
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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
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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Grou
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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
[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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[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 .
Kontopantelis, Reeves ipdforest
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OBJECTIVE
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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
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 .
Kontopantelis, Reeves ipdforest
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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
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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
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)
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.]
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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
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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)
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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[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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Appendix Thank you!
Comments, suggestions:e.kontopantelis@manchester.ac.uk
Kontopantelis, Reeves ipdforest