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

1. [Poster title]
[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]
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

2. [Poster title]
[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]
Meta-analysis overview
A practical guide
ipdforest
Summary
Outline
1 Meta-analysis overview
2 A practical guide
3 ipdforest
methods
example
4 Summary
Kontopantelis, Reeves ipdforest

3. [Poster title]
[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]
Meta-analysis overview
A practical guide
ipdforest
Summary
Timeline
‘Meta’ is a Greek preposition meaning ‘after’, so
meta-analysis =⇒ post-analysis
Efforts to pool results from individual studies back as far as
1904
The first attempt that assessed a therapeutic intervention
was published in 1955
In 1976 Glass first used the term to describe a "statistical
analysis of a large collection of analysis results from
individual studies for the purpose of integrating the
findings"
Kontopantelis, Reeves ipdforest

4. [Poster title]
[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]
Meta-analysis overview
A practical guide
ipdforest
Summary
Meta-analysing reported study results
A two-stage process
the relevant summary effect statistics are extracted from
published papers on the included studies
these are then combined into an overall effect estimate
using a suitable meta-analysis model
However, problems often arise
papers do not report all the statistical information required
as input
papers report a statistic other than the effect size which
needs to be transformed with a loss of precision
a study might be too different to be included (population
clinically heterogeneous)
Kontopantelis, Reeves ipdforest

5. [Poster title]
[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]
Meta-analysis overview
A practical guide
ipdforest
Summary
Individual Patient Data
IPD
These problems can be avoided when IPD from each
study are available
outcomes can be easily standardised
clinical heterogeneity can be addressed with subgroup
analyses and patient-level covariate controlling
Can be analysed in a single- or two-stage process
mixed-effects regression models can be used to combine
information across studies in a single stage
this is currently the best approach, with the two-stage
method being at best equivalent in certain scenarios
Kontopantelis, Reeves ipdforest

6. [Poster title]
[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]
Meta-analysis overview
A practical guide
ipdforest
Summary
Forest plot
One advantage of two-stage meta-analysis is the ability to
convey 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 the
intervention in each study and across all
informative, easy to follow and particularly useful for
readers with little or no methodological experience
key feature of meta-analysis and always presented when
two-stage meta-analyses are performed
In one-stage meta-analysis, only the overall effect is
calculated and creating a forest-plot is not straightforward
Enter ipdforest
Kontopantelis, Reeves ipdforest

7. [Poster title]
[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]
Meta-analysis overview
A practical guide
ipdforest
Summary
The hypothetical study
Individual patient data from randomised controlled trials
For each trial we have
a binary control/intervention membership variable
baseline and follow-up data for the continuous outcome
covariates
Assume measurements consistent across trials and
standardisation is not required
We will explore linear random-effects models with the
xtmixed command; application to the logistic case using
xtmelogit should be straightforward
In the models that follow, in general, we denote fixed
effects with ‘γ’s and random effects with ‘β’s
Kontopantelis, Reeves ipdforest

8. [Poster title]
[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]
Meta-analysis overview
A practical guide
ipdforest
Summary
Model 1
fixed common intercept; random treatment effect; fixed effect for baseline
Yij = γ0 + β1jgroupij + γ2Ybij + ij ij ∼ N(0, σ2
j )
β1j = γ1 + u1j u1j ∼ N(0, τ1
2)
i: the patient
j: the trial
Yij: the outcome
γ0: fixed common intercept
β1j: random treatment
effect for trial j
γ1: mean treatment effect
groupij: group membership
γ2: fixed baseline effect
Ybij: baseline score
u1j: random treatment
effect for trial j
τ1
2: between trial variance
ij: error term
σ2
j : within trial variance for
trial j
Kontopantelis, Reeves ipdforest

9. [Poster title]
[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]
Meta-analysis overview
A practical guide
ipdforest
Summary
Model 1
fixed common intercept; random treatment effect; fixed effect for baseline
Possibly the simplest approach
In Stata it can be expressed as
xtmixed Y i.group Yb || studyid:group,
nocons
where
studyid, the trial identifier
group, control/intervention membership
Y and Yb, endpoint and baseline scores
note that the nocons option suppresses estimation of the
intercept as a random effect
Kontopantelis, Reeves ipdforest

10. [Poster title]
[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]
Meta-analysis overview
A practical guide
ipdforest
Summary
Model 2
fixed trial specific intercepts; random treatment effect; fixed trial-specific effects for
baseline
Common intercept & fixed baseline difficult to justify
A more accepted model allows for different fixed intercepts
and fixed baseline effects for each trial:
Yi j = γ0j + β1j groupi j + γ2j Ybi j + i j
β1j = γ1 + u1j
where
γ0j the fixed intercept for trial j
γ2j the fixed baseline effect for trial j
In Stata expressed as:
xtmixed Y i.group i.studyid Yb1 Yb2 Yb3 Yb4
|| studyid:group, nocons
where Yb‘i’=Yb if studyid=‘i’ and zero otherwise
Kontopantelis, Reeves ipdforest

11. [Poster title]
[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]
Meta-analysis overview
A practical guide
ipdforest
Summary
Model 3
random trial intercept; random treatment effect; fixed trial-specific effects for baseline
Another possibility, althought contentious, is to assume trial
intercepts are random (e.g. multi-centre trial):
Yi j = β0j + β1j groupi j + γ2j Ybi j + i j
β0j = γ0 + u0j
β1j = γ1 + u1j
wiser to assume random effects correlation ρ = 0:
i j ∼ N(0, σ2
j ) u0j ∼ N(0, τ2
0 )
u1j ∼ N(0, τ2
1 ) cov(u0j , u1j ) = 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 RE
variance-covariance components
Kontopantelis, Reeves ipdforest

12. [Poster title]
[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]
Meta-analysis overview
A practical guide
ipdforest
Summary
Model 4
random trial intercept; random treatment effect; random effects for baseline
The baseline could also have been modelled as a
random-effect:
Yi j = β0j + β1j groupi j + β2j Ybi j + i j
β0j = γ0 + u0j
β1j = γ1 + u1j
β2j = γ2 + u2j
as before, non-zero random effects correlations:
u0j ∼ N(0, τ2
0 ) u1j ∼ N(0, τ2
1 )
u2j ∼ N(0, τ2
2 ) cov(u0j , u1j ) = ρ1τ0τ1
cov(u0j , u2j ) = ρ2τ0τ2 cov(u1j , u2j ) = ρ3τ1τ2
In Stata expressed as:
xtmixed Y i.group Yb || studyid:group Yb,
cov(uns)
Kontopantelis, Reeves ipdforest

13. [Poster title]
[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]
Meta-analysis overview
A practical guide
ipdforest
Summary
Model 5
Interactions and covariates
A covariate or an interaction term can be modelled as a
fixed or random effect
Assuming continuous and standardised variable age we
can expand Model 2 to include fixed effects for both age
and its interaction with the treatment:
Yi j = γ0j +β1j groupi j +γ2j Ybi j +γ3agei j +γ4groupi j agei j + i j
β1j = γ1 + u1j
In Stata expressed as:
xtmixed Y i.group i.studyid Yb1 Yb2 Yb3 Yb4
age i.group#c.age || studyid:group, nocons
If modelled as a random effect, non-convergence issues
more likely to be encountered
Kontopantelis, Reeves ipdforest

14. [Poster title]
[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]
Meta-analysis overview
A practical guide
ipdforest
Summary
methods
example
General
ipdforest is issued following an IPD meta-analysis that
uses mixed effects two-level regression, with patients
nested within trials and a
linear model (xtmixed)
or
logistic model (xtmelogit)
Provides a meta-analysis summary table and a forest plot
Trial effects are calculated within ipdforest
Can calculate and report both main and interaction effects
Overall effect(s) and variance estimates are extracted from
the preceding regression
Kontopantelis, Reeves ipdforest

15. [Poster title]
[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]
Meta-analysis overview
A practical guide
ipdforest
Summary
methods
example
Process
ipdforest estimates individual trial effects and their
standard errors using one-level linear or logistic
regressions
Following xtmixed, regress is used and following
xtmelogit, logit is used, for each trial
ipdforest controls these regressions for fixed- or
random-effects covariates that were specified in the
preceding two-level regression
User has full control over included covariates in the
command (e.g. specification as fixed- or random-effects)
But we strongly recommend using the same specifications
Kontopantelis, Reeves ipdforest

16. [Poster title]
[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]
Meta-analysis overview
A practical guide
ipdforest
Summary
methods
example
Estimation details
In the estimation of individual trial effects, ipdforest
controls for a random-effects covariate (i.e. allowing the
regression coefficient to vary by trial) by including the
covariate as an independent variable in each regression
Control for a fixed-effect covariate (regression coefficient
assumed constant across trials and given by the coefficient
estimated under two-level model) is a little more complex.
Not possible to specify a fixed value for a regression
coefficient under regress and the continuous outcome
variable is adjusted by subtracting the contribution of the
fixed covariates to its values in a first step prior to analysis
For a binary outcome the equivalent is achieved through
use of the offset option in logit
Kontopantelis, Reeves ipdforest

17. [Poster title]
[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]
Meta-analysis overview
A practical guide
ipdforest
Summary
methods
example
Heterogeneity
part I
Between-trial variability τ2 in the treatment effect, known
as heterogeneity, arises from differences in trial design,
quality, outcomes or populations
For continuous outcomes, ipdforest reports, I2 and H2
M,
based on the xtmixed output
For binary outcomes, an estimate of the within-trial
variance is not reported under xtmelogit and hence
heterogeneity measures cannot be computed
Between-trial variability estimate ˆτ2 and its confidence
interval is reported under both models.
Kontopantelis, Reeves ipdforest

18. [Poster title]
[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]
Meta-analysis overview
A practical guide
ipdforest
Summary
methods
example
Heterogeneity
part II
We are not calculating an IPD version of Cochran’s Q, the
orthodox χ2
k−1 homogeneity test, considering its poor
performance when the number of trials k is small
Besides, taking into account even low levels of τ2 by
adopting a random-effects model is a more conservative
approach than the fixed-effect one
When between-trial variance is estimated to be close to
zero, results with the two approaches converge
Kontopantelis, Reeves ipdforest

19. [Poster title]
[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]
Meta-analysis overview
A practical guide
ipdforest
Summary
methods
example
Depression intervention
We apply the ipdforest command to a dataset of 4
depression intervention trials
Complete information in terms of age, gender,
control/intervention group membership, continuous
outcome baseline and endpoint values for 518 patients
Results not published yet; we use fake author names and
generated random continuous & binary outcome variables,
while keeping the covariates at their actual values
Introduced correlation between baseline and endpoint
scores and between-trial variability
Logistic IPD meta-analysis, followed by ipdforest
Kontopantelis, Reeves ipdforest

20. [Poster title]
[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]
Meta-analysis overview
A practical guide
ipdforest
Summary
methods
example
Dataset
. use ipdforest_example.dta,
. describe
Contains data from ipdforest_example.dta
obs: 518
vars: 17 6 Feb 2012 11:14
size: 20,202
storage display value
variable name type format label variable label
studyid byte %22.0g stid Study identifier
patid int %8.0g Patient identifier
group byte %20.0g grplbl Intervention/control group
sex byte %10.0g sexlbl Gender
age float %10.0g Age in years
depB byte %9.0g Binary outcome, endpoint
depBbas byte %9.0g Binary outcome, baseline
depBbas1 byte %9.0g Bin outcome baseline, trial 1
depBbas2 byte %9.0g Bin outcome baseline, trial 2
depBbas5 byte %9.0g Bin outcome baseline, trial 5
depBbas9 byte %9.0g Bin outcome baseline, trial 9
depC float %9.0g Continuous outcome, endpoint
depCbas float %9.0g Continuous outcome, baseline
depCbas1 float %9.0g Cont outcome baseline, trial 1
depCbas2 float %9.0g Cont outcome baseline, trial 2
depCbas5 float %9.0g Cont outcome baseline, trial 5
depCbas9 float %9.0g Cont outcome baseline, trial 9
Sorted by: studyid patid
Kontopantelis, Reeves ipdforest

21. [Poster title]
[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]
Meta-analysis overview
A practical guide
ipdforest
Summary
methods
example
Mixed effects logistic regression model
fixed 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 = 518
Group variable: studyid Number of groups = 4
Obs per group: min = 42
avg = 129.5
max = 214
Integration points = 7 Wald chi2(11) = 42.06
Log 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 2.71978
agec .9867902 .0119059 -1.10 0.270 .9637288 1.010403
sex .7117592 .1540753 -1.57 0.116 .4656639 1.087912
studyid
2 1.050007 .5725516 0.09 0.929 .3606166 3.057303
5 .8014551 .5894511 -0.30 0.763 .189601 3.387799
9 1.281413 .6886057 0.46 0.644 .4469619 3.673735
depBbas1 3.152908 1.495281 2.42 0.015 1.244587 7.987253
depBbas2 4.480302 1.863908 3.60 0.000 1.982385 10.12574
depBbas5 2.387336 1.722993 1.21 0.228 .5802064 9.823007
depBbas9 1.881203 .7086507 1.68 0.093 .8990569 3.936262
group#c.agec
1 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: Independent
var(group) 8.86e-21 2.43e-11 0 .
var(agec) 5.99e-18 4.40e-11 0 .
Kontopantelis, Reeves ipdforest

22. [Poster title]
[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]
Meta-analysis overview
A practical guide
ipdforest
Summary
methods
example
ipdforest
modelling main effect and interaction
. ipdforest group, fe(sex) re(agec) ia(agec) or
One-stage meta-analysis results using xtmelogit (ML method) and ipdforest
Main effect (group)
Study Effect [95% Conf. Interval] % Weight
Hart 2005 2.118 0.942 4.765 19.88
Richards 2004 2.722 1.336 5.545 30.69
Silva 2008 2.690 0.748 9.676 8.11
Kompany 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 ipdforest
Interaction effect (group x agec)
Study Effect [95% Conf. Interval] % Weight
Hart 2005 0.972 0.901 1.049 19.88
Richards 2004 0.995 0.937 1.055 30.69
Silva 2008 0.987 0.888 1.098 8.11
Kompany 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

23. [Poster title]
[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]
Meta-analysis overview
A practical guide
ipdforest
Summary
methods
example
Forest plots
main effect and interaction
Overall effect
Kompany 2009
Silva 2008
Richards 2004
Hart 2005
Studies
0 2 3 4 5 6 7 8 9 101
Effect sizes and CIs (ORs)
Main effect (group)
Overall effect
Kompany 2009
Silva 2008
Richards 2004
Hart 2005
Studies
0 .2 .4 .6 .8 1.2 1.41
Effect sizes and CIs (ORs)
Interaction effect (group x agec)
Kontopantelis, Reeves ipdforest

24. [Poster title]
[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]
Meta-analysis overview
A practical guide
ipdforest
Summary
What to take home
A few different approaches exist for conducting one-stage
IPD meta-analysis
Stata can cope through the xtmixed and the xtmelogit
commands
The ipdforest command aims to help meta-analysts
calculate trial effects
display results in standard meta-analysis tables
produce 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

25. [Poster title]
[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]
Appendix Thank you!
Comments, suggestions:
e.kontopantelis@manchester.ac.uk
Kontopantelis, Reeves ipdforest