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RSS 2012 - ipdforest

Evangelos Kontopantelis, profile picture
Evangelos Kontopantelis

This poster presents an overview of individual patient data (IPD) meta-analysis and introduces the ipdforest command in Stata. IPD meta-analysis involves pooling raw data from multiple studies and can address issues like inconsistent reporting across studies. The poster outlines three mixed-effects regression models for IPD meta-analysis with varying assumptions about fixed and random effects. It also demonstrates how ipdforest can be used to generate forest plots for one-stage IPD meta-analyses in Stata, overcoming limitations of standard two-stage approaches.

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[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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

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RSS 2012 - ipdforest

  • 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