Re: [R] metafor and meta-analysis at arm-level
Dear Angelo, This is (currently) not possible. Best, -- Wolfgang Viechtbauerhttp://www.wvbauer.com/ Department of Methodology and StatisticsTel: +31 (0)43 388-2277 School for Public Health and Primary Care Office Location: Maastricht University, P.O. Box 616 Room B2.01 (second floor) 6200 MD Maastricht, The Netherlands Debyeplein 1 (Randwyck) Original Message From: Angelo Franchini [mailto:angelo.franch...@bristol.ac.uk] Sent: Sunday, August 08, 2010 13:09 To: Viechtbauer Wolfgang (STAT) Cc: r-help@r-project.org Subject: RE: [R] metafor and meta-analysis at arm-level Dear Wolfgang, Is there any way for rma to add random effects only to each treatment arm, but not to the control one? Many thanks, Angelo On Thu, August 5, 2010 6:21 pm, Viechtbauer Wolfgang (STAT) wrote: Dear Angelo, rma(yi=o, sei=se, mods=~s+t-1, method=REML) is *a* way to run the arm-based pairwise meta-analysis. Whether it is the *correct* way is a question I cannot answer. lme(o~s+t-1, random=~t-1 | s, weights=(~ se^2)) is a different model. First of all, it adds a random effect only to each treatment arm within each study, while the rma model above gives a random effect to each observation. Moreover, the lme model assumes that the sampling variances are only known up to a proportionality constant, while the rma model assumes that they are known exactly. Similarly, lm(formula = o ~ s + t - 1, weights = 1/se.o^2) assumes that the sampling variances are only known up to a proportionality constant, while rma (with method=FE) assumes that they are known exactly. For the same reason will rma(yi=e, sei=se, method=REML) lme(e~1, random=~1 | s, weights=(~ se.e^2)) and rma(yi=e, sei=se.e, method=FE) lm(e~1, weights = 1/se.e^2) not give you the same results. Best, -- Wolfgang Viechtbauerhttp://www.wvbauer.com/ Department of Methodology and StatisticsTel: +31 (0)43 388-2277 School for Public Health and Primary Care Office Location: Maastricht University, P.O. Box 616 Room B2.01 (second floor) 6200 MD Maastricht, The Netherlands Debyeplein 1 (Randwyck) Original Message From: Angelo Franchini [mailto:angelo.franch...@bristol.ac.uk] Sent: Wednesday, August 04, 2010 16:26 To: Viechtbauer Wolfgang (STAT) Cc: 'Angelo Franchini'; r-help@r-project.org Subject: RE: [R] metafor and meta-analysis at arm-level Hello Wolfgang. I'd appreciate if you could help me check whether I am doing the proper thing to do an arm-level meta-analysis with metafor and what differences there might be in trying to do the same with lme and lm. I am following the arm based model described in section 3.2 of the Salanti's paper that you mentioned in your previous e-mail, namely: theta = B*eta + X*mu + W*beta where: theta = vector of parameter for outcomes in treatment arms (theta_ij for study i, treat. arm j) eta= vector of parameter for outcomes in control arms (eta_i for study i) mu = vector of effects (treat. vs cont.) (mu_ij for study i, treat. arm j) beta = vector of random effects (beta_ij for study i, treat. arm j) In my specific case with a pairwise meta-analysis, I had my data arranged as in columns for the following variables: s t o se with s as study/trial identifier t as 0/1 for control/treatment arm o as observed outcome in control or treatment arm se as standard error of that outcome measure I then ran metafor as: rma(yi=o, sei=se, mods=~s+t-1, method=REML) for random effects, and REML replaced by FE for fixed effects. Is that the correct way to run the arm-based pairwise meta-analysis? Shouldn't I be able to obtain similar results with LME for random-effects by using the command: lme(o~s+t-1, random=~t-1 | s, weights=(~ se^2)) and for fixed-effects with: lm(formula = o ~ s + t - 1, weights = 1/se.o^2) For the trial-based pairwise meta-analysis I used: data arranged as: s e se with: s study e effect se standard error and commands: rma(yi=e, sei=se, method=REML) or lme(e~1, random=~1 | s, weights=(~ se.e^2)) for random-effects, while for fixed-effects: rma(yi=e, sei=se.e, method=FE) lm(e~1, weights = 1/se.e^2) Does that make sense? Many thanks for any comment/advice on this matter. Best regards, Angelo __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] metafor and meta-analysis at arm-level
Dear Wolfgang, Is there any way for rma to add random effects only to each treatment arm, but not to the control one? Many thanks, Angelo On Thu, August 5, 2010 6:21 pm, Viechtbauer Wolfgang (STAT) wrote: Dear Angelo, rma(yi=o, sei=se, mods=~s+t-1, method=REML) is *a* way to run the arm-based pairwise meta-analysis. Whether it is the *correct* way is a question I cannot answer. lme(o~s+t-1, random=~t-1 | s, weights=(~ se^2)) is a different model. First of all, it adds a random effect only to each treatment arm within each study, while the rma model above gives a random effect to each observation. Moreover, the lme model assumes that the sampling variances are only known up to a proportionality constant, while the rma model assumes that they are known exactly. Similarly, lm(formula = o ~ s + t - 1, weights = 1/se.o^2) assumes that the sampling variances are only known up to a proportionality constant, while rma (with method=FE) assumes that they are known exactly. For the same reason will rma(yi=e, sei=se, method=REML) lme(e~1, random=~1 | s, weights=(~ se.e^2)) and rma(yi=e, sei=se.e, method=FE) lm(e~1, weights = 1/se.e^2) not give you the same results. Best, -- Wolfgang Viechtbauerhttp://www.wvbauer.com/ Department of Methodology and StatisticsTel: +31 (0)43 388-2277 School for Public Health and Primary Care Office Location: Maastricht University, P.O. Box 616 Room B2.01 (second floor) 6200 MD Maastricht, The Netherlands Debyeplein 1 (Randwyck) Original Message From: Angelo Franchini [mailto:angelo.franch...@bristol.ac.uk] Sent: Wednesday, August 04, 2010 16:26 To: Viechtbauer Wolfgang (STAT) Cc: 'Angelo Franchini'; r-help@r-project.org Subject: RE: [R] metafor and meta-analysis at arm-level Hello Wolfgang. I'd appreciate if you could help me check whether I am doing the proper thing to do an arm-level meta-analysis with metafor and what differences there might be in trying to do the same with lme and lm. I am following the arm based model described in section 3.2 of the Salanti's paper that you mentioned in your previous e-mail, namely: theta = B*eta + X*mu + W*beta where: theta = vector of parameter for outcomes in treatment arms (theta_ij for study i, treat. arm j) eta= vector of parameter for outcomes in control arms (eta_i for study i) mu = vector of effects (treat. vs cont.) (mu_ij for study i, treat. arm j) beta = vector of random effects (beta_ij for study i, treat. arm j) In my specific case with a pairwise meta-analysis, I had my data arranged as in columns for the following variables: s t o se with s as study/trial identifier t as 0/1 for control/treatment arm o as observed outcome in control or treatment arm se as standard error of that outcome measure I then ran metafor as: rma(yi=o, sei=se, mods=~s+t-1, method=REML) for random effects, and REML replaced by FE for fixed effects. Is that the correct way to run the arm-based pairwise meta-analysis? Shouldn't I be able to obtain similar results with LME for random-effects by using the command: lme(o~s+t-1, random=~t-1 | s, weights=(~ se^2)) and for fixed-effects with: lm(formula = o ~ s + t - 1, weights = 1/se.o^2) For the trial-based pairwise meta-analysis I used: data arranged as: s e se with: s study e effect se standard error and commands: rma(yi=e, sei=se, method=REML) or lme(e~1, random=~1 | s, weights=(~ se.e^2)) for random-effects, while for fixed-effects: rma(yi=e, sei=se.e, method=FE) lm(e~1, weights = 1/se.e^2) Does that make sense? Many thanks for any comment/advice on this matter. Best regards, Angelo -- NIHR Research Methods Training Fellow, Department of Community Based Medicine University of Bristol 25 Belgrave Road Bristol BS8 2AA Tel. 0779 265-6552 __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] metafor and meta-analysis at arm-level
Dear Angelo, rma(yi=o, sei=se, mods=~s+t-1, method=REML) is *a* way to run the arm-based pairwise meta-analysis. Whether it is the *correct* way is a question I cannot answer. lme(o~s+t-1, random=~t-1 | s, weights=(~ se^2)) is a different model. First of all, it adds a random effect only to each treatment arm within each study, while the rma model above gives a random effect to each observation. Moreover, the lme model assumes that the sampling variances are only known up to a proportionality constant, while the rma model assumes that they are known exactly. Similarly, lm(formula = o ~ s + t - 1, weights = 1/se.o^2) assumes that the sampling variances are only known up to a proportionality constant, while rma (with method=FE) assumes that they are known exactly. For the same reason will rma(yi=e, sei=se, method=REML) lme(e~1, random=~1 | s, weights=(~ se.e^2)) and rma(yi=e, sei=se.e, method=FE) lm(e~1, weights = 1/se.e^2) not give you the same results. Best, -- Wolfgang Viechtbauerhttp://www.wvbauer.com/ Department of Methodology and StatisticsTel: +31 (0)43 388-2277 School for Public Health and Primary Care Office Location: Maastricht University, P.O. Box 616 Room B2.01 (second floor) 6200 MD Maastricht, The Netherlands Debyeplein 1 (Randwyck) Original Message From: Angelo Franchini [mailto:angelo.franch...@bristol.ac.uk] Sent: Wednesday, August 04, 2010 16:26 To: Viechtbauer Wolfgang (STAT) Cc: 'Angelo Franchini'; r-help@r-project.org Subject: RE: [R] metafor and meta-analysis at arm-level Hello Wolfgang. I'd appreciate if you could help me check whether I am doing the proper thing to do an arm-level meta-analysis with metafor and what differences there might be in trying to do the same with lme and lm. I am following the arm based model described in section 3.2 of the Salanti's paper that you mentioned in your previous e-mail, namely: theta = B*eta + X*mu + W*beta where: theta = vector of parameter for outcomes in treatment arms (theta_ij for study i, treat. arm j) eta= vector of parameter for outcomes in control arms (eta_i for study i) mu = vector of effects (treat. vs cont.) (mu_ij for study i, treat. arm j) beta = vector of random effects (beta_ij for study i, treat. arm j) In my specific case with a pairwise meta-analysis, I had my data arranged as in columns for the following variables: s t o se with s as study/trial identifier t as 0/1 for control/treatment arm o as observed outcome in control or treatment arm se as standard error of that outcome measure I then ran metafor as: rma(yi=o, sei=se, mods=~s+t-1, method=REML) for random effects, and REML replaced by FE for fixed effects. Is that the correct way to run the arm-based pairwise meta-analysis? Shouldn't I be able to obtain similar results with LME for random-effects by using the command: lme(o~s+t-1, random=~t-1 | s, weights=(~ se^2)) and for fixed-effects with: lm(formula = o ~ s + t - 1, weights = 1/se.o^2) For the trial-based pairwise meta-analysis I used: data arranged as: s e se with: s study e effect se standard error and commands: rma(yi=e, sei=se, method=REML) or lme(e~1, random=~1 | s, weights=(~ se.e^2)) for random-effects, while for fixed-effects: rma(yi=e, sei=se.e, method=FE) lm(e~1, weights = 1/se.e^2) Does that make sense? Many thanks for any comment/advice on this matter. Best regards, Angelo __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] metafor and meta-analysis at arm-level
Hello Wolfgang. I'd appreciate if you could help me check whether I am doing the proper thing to do an arm-level meta-analysis with metafor and what differences there might be in trying to do the same with lme and lm. I am following the arm based model described in section 3.2 of the Salanti's paper that you mentioned in your previous e-mail, namely: theta = B*eta + X*mu + W*beta where: theta = vector of parameter for outcomes in treatment arms (theta_ij for study i, treat. arm j) eta= vector of parameter for outcomes in control arms (eta_i for study i) mu = vector of effects (treat. vs cont.) (mu_ij for study i, treat. arm j) beta = vector of random effects (beta_ij for study i, treat. arm j) In my specific case with a pairwise meta-analysis, I had my data arranged as in columns for the following variables: s t o se with s as study/trial identifier t as 0/1 for control/treatment arm o as observed outcome in control or treatment arm se as standard error of that outcome measure I then ran metafor as: rma(yi=o, sei=se, mods=~s+t-1, method=REML) for random effects, and REML replaced by FE for fixed effects. Is that the correct way to run the arm-based pairwise meta-analysis? Shouldn't I be able to obtain similar results with LME for random-effects by using the command: lme(o~s+t-1, random=~t-1 | s, weights=(~ se^2)) and for fixed-effects with: lm(formula = o ~ s + t - 1, weights = 1/se.o^2) For the trial-based pairwise meta-analysis I used: data arranged as: s e se with: s study e effect se standard error and commands: rma(yi=e, sei=se, method=REML) or lme(e~1, random=~1 | s, weights=(~ se.e^2)) for random-effects, while for fixed-effects: rma(yi=e, sei=se.e, method=FE) lm(e~1, weights = 1/se.e^2) Does that make sense? Many thanks for any comment/advice on this matter. Best regards, Angelo On Fri, July 9, 2010 11:55 am, Viechtbauer Wolfgang (STAT) wrote: With appropriate design matrix, I mean the X matrix in the mixed-effects model y = Xb + u + e, where y is the vector of outcomes, u is a vector of (possibly correlated) random effects, and e is a vector of (possibly) random errors. The X matrix is specified via the 'mods' argument in the rma() function. If y consists of arm-level outcomes, then you need appropriate dummy variables in X to code what type of arm the outcome corresponds to. Have you read, for example: Salanti, G., Higgins, J. P. T., Ades, A. E., Ioannidis, J. P. A. (2008). Evaluation of networks of randomized trials. Statistical Methods in Medical Research, 17(3), 279-301. This article may be helpful. Best, -- Wolfgang Viechtbauerhttp://www.wvbauer.com/ Department of Methodology and StatisticsTel: +31 (0)43 388-2277 School for Public Health and Primary Care Office Location: Maastricht University, P.O. Box 616 Room B2.01 (second floor) 6200 MD Maastricht, The Netherlands Debyeplein 1 (Randwyck) Original Message From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of Angelo Franchini Sent: Tuesday, July 06, 2010 10:37 To: Wolfgang Viechtbauer Cc: r-help@r-project.org; Angelo Franchini Subject: Re: [R] metafor and meta-analysis at arm-level Hello Wolfgang, Thank you very much for your response. When you mentionthe appropriate design matrix, do you mean by that the 'n1i, n2i, m1i, m2i, sd1i, sd2i' arguments of the rma function, or am I missing something? I read the documentation on metafor (introduction), rma/rma.uni and escalc, and that was the only way that I could find for the package to use information at the arm-level rather than the trial one. As for the complexity of possible correlations between effects, that is something to be considered for the network analysis case, correct? Many thanks. Best regards, Angelo On Sun, July 4, 2010 6:06 am, Wolfgang Viechtbauer wrote: Hello Angelo, You can either supply the arm-level outcomes and corresponding sampling variances directly (via the yi and vi arguments) or supply the necessary information so that the escalc() or rma() functions can calculate an appropriate arm-level outcome (such as the log odds). See the documentation of the escalc() function and in particular the part about proportions and tranaformations thereof as possible outcome measures. This is the easy part. Then you need to set up an appropriate design matrix to code what arm each observed outcome corresponds to. And finally comes the tricky/problematic part. The rma() function assumes independent sampling errors and independent random effects for each observed outcome. Independent sampling errors is (usually) ok when using arm-level outcomes, but the independent random errors part may not be appropriate. This is why I am working on functions that do not make this independence assumption. With those functions, you can then carry out multivariate and network-type meta-analyses. These functions will become part of the metafor
Re: [R] metafor and meta-analysis at arm-level
With appropriate design matrix, I mean the X matrix in the mixed-effects model y = Xb + u + e, where y is the vector of outcomes, u is a vector of (possibly correlated) random effects, and e is a vector of (possibly) random errors. The X matrix is specified via the 'mods' argument in the rma() function. If y consists of arm-level outcomes, then you need appropriate dummy variables in X to code what type of arm the outcome corresponds to. Have you read, for example: Salanti, G., Higgins, J. P. T., Ades, A. E., Ioannidis, J. P. A. (2008). Evaluation of networks of randomized trials. Statistical Methods in Medical Research, 17(3), 279-301. This article may be helpful. Best, -- Wolfgang Viechtbauerhttp://www.wvbauer.com/ Department of Methodology and StatisticsTel: +31 (0)43 388-2277 School for Public Health and Primary Care Office Location: Maastricht University, P.O. Box 616 Room B2.01 (second floor) 6200 MD Maastricht, The Netherlands Debyeplein 1 (Randwyck) Original Message From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of Angelo Franchini Sent: Tuesday, July 06, 2010 10:37 To: Wolfgang Viechtbauer Cc: r-help@r-project.org; Angelo Franchini Subject: Re: [R] metafor and meta-analysis at arm-level Hello Wolfgang, Thank you very much for your response. When you mentionthe appropriate design matrix, do you mean by that the 'n1i, n2i, m1i, m2i, sd1i, sd2i' arguments of the rma function, or am I missing something? I read the documentation on metafor (introduction), rma/rma.uni and escalc, and that was the only way that I could find for the package to use information at the arm-level rather than the trial one. As for the complexity of possible correlations between effects, that is something to be considered for the network analysis case, correct? Many thanks. Best regards, Angelo On Sun, July 4, 2010 6:06 am, Wolfgang Viechtbauer wrote: Hello Angelo, You can either supply the arm-level outcomes and corresponding sampling variances directly (via the yi and vi arguments) or supply the necessary information so that the escalc() or rma() functions can calculate an appropriate arm-level outcome (such as the log odds). See the documentation of the escalc() function and in particular the part about proportions and tranaformations thereof as possible outcome measures. This is the easy part. Then you need to set up an appropriate design matrix to code what arm each observed outcome corresponds to. And finally comes the tricky/problematic part. The rma() function assumes independent sampling errors and independent random effects for each observed outcome. Independent sampling errors is (usually) ok when using arm-level outcomes, but the independent random errors part may not be appropriate. This is why I am working on functions that do not make this independence assumption. With those functions, you can then carry out multivariate and network-type meta-analyses. These functions will become part of the metafor package in the future. Best, -- Wolfgang Viechtbauer http://www.wvbauer.com Angelo Franchini angelo.franch...@bristol.ac.uk wrote: Hi, I have been looking for an R package which allowed to do meta-analysis (both pairwise and network/mixed-treatment) at arm-level rather than at trial-level, the latter being the common way in which meta-analysis is done. By arm-level meta-analysis I mean one that accounts for data provided at the level of the individual arms of each trial and that does not simply derive the difference between arms and do the meta-analysis on that. I am not sure metafor can do that, but hopefully someone more experienced on it can clarify that to me. I can see that it can take data in both forms, arm and trial level, but it looks as if the arm-level information would be converted into trial one through escalc and the latter then used for the meta-analysis. Is that right? Many thanks. Angelo -- NIHR Research Methods Training Fellow, Department of Community Based Medicine University of Bristol 25 Belgrave Road Bristol BS8 2AA Tel. 0779 265-6552 __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] metafor and meta-analysis at arm-level
Hello Wolfgang, Thank you very much for your response. When you mentionthe appropriate design matrix, do you mean by that the 'n1i, n2i, m1i, m2i, sd1i, sd2i' arguments of the rma function, or am I missing something? I read the documentation on metafor (introduction), rma/rma.uni and escalc, and that was the only way that I could find for the package to use information at the arm-level rather than the trial one. As for the complexity of possible correlations between effects, that is something to be considered for the network analysis case, correct? Many thanks. Best regards, Angelo On Sun, July 4, 2010 6:06 am, Wolfgang Viechtbauer wrote: Hello Angelo, You can either supply the arm-level outcomes and corresponding sampling variances directly (via the yi and vi arguments) or supply the necessary information so that the escalc() or rma() functions can calculate an appropriate arm-level outcome (such as the log odds). See the documentation of the escalc() function and in particular the part about proportions and tranaformations thereof as possible outcome measures. This is the easy part. Then you need to set up an appropriate design matrix to code what arm each observed outcome corresponds to. And finally comes the tricky/problematic part. The rma() function assumes independent sampling errors and independent random effects for each observed outcome. Independent sampling errors is (usually) ok when using arm-level outcomes, but the independent random errors part may not be appropriate. This is why I am working on functions that do not make this independence assumption. With those functions, you can then carry out multivariate and network-type meta-analyses. These functions will become part of the metafor package in the future. Best, -- Wolfgang Viechtbauer http://www.wvbauer.com Angelo Franchini angelo.franch...@bristol.ac.uk wrote: Hi, I have been looking for an R package which allowed to do meta-analysis (both pairwise and network/mixed-treatment) at arm-level rather than at trial-level, the latter being the common way in which meta-analysis is done. By arm-level meta-analysis I mean one that accounts for data provided at the level of the individual arms of each trial and that does not simply derive the difference between arms and do the meta-analysis on that. I am not sure metafor can do that, but hopefully someone more experienced on it can clarify that to me. I can see that it can take data in both forms, arm and trial level, but it looks as if the arm-level information would be converted into trial one through escalc and the latter then used for the meta-analysis. Is that right? Many thanks. Angelo -- NIHR Research Methods Training Fellow, Department of Community Based Medicine University of Bristol 25 Belgrave Road Bristol BS8 2AA Tel. 0779 265-6552 __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. -- NIHR Research Methods Training Fellow, Department of Community Based Medicine University of Bristol 25 Belgrave Road Bristol BS8 2AA Tel. 0779 265-6552 __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] metafor and meta-analysis at arm-level
Hello Angelo, You can either supply the arm-level outcomes and corresponding sampling variances directly (via the yi and vi arguments) or supply the necessary information so that the escalc() or rma() functions can calculate an appropriate arm-level outcome (such as the log odds). See the documentation of the escalc() function and in particular the part about proportions and tranaformations thereof as possible outcome measures. This is the easy part. Then you need to set up an appropriate design matrix to code what arm each observed outcome corresponds to. And finally comes the tricky/problematic part. The rma() function assumes independent sampling errors and independent random effects for each observed outcome. Independent sampling errors is (usually) ok when using arm-level outcomes, but the independent random errors part may not be appropriate. This is why I am working on functions that do not make this independence assumption. With those functions, you can then carry out multivariate and network-type meta-analyses. These functions will become part of the metafor package in the future. Best, -- Wolfgang Viechtbauer http://www.wvbauer.com Angelo Franchini angelo.franch...@bristol.ac.uk wrote: Hi, I have been looking for an R package which allowed to do meta-analysis (both pairwise and network/mixed-treatment) at arm-level rather than at trial-level, the latter being the common way in which meta-analysis is done. By arm-level meta-analysis I mean one that accounts for data provided at the level of the individual arms of each trial and that does not simply derive the difference between arms and do the meta-analysis on that. I am not sure metafor can do that, but hopefully someone more experienced on it can clarify that to me. I can see that it can take data in both forms, arm and trial level, but it looks as if the arm-level information would be converted into trial one through escalc and the latter then used for the meta-analysis. Is that right? Many thanks. Angelo -- NIHR Research Methods Training Fellow, Department of Community Based Medicine University of Bristol 25 Belgrave Road Bristol BS8 2AA Tel. 0779 265-6552 __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] metafor and meta-analysis at arm-level
Hi, I have been looking for an R package which allowed to do meta-analysis (both pairwise and network/mixed-treatment) at arm-level rather than at trial-level, the latter being the common way in which meta-analysis is done. By arm-level meta-analysis I mean one that accounts for data provided at the level of the individual arms of each trial and that does not simply derive the difference between arms and do the meta-analysis on that. I am not sure metafor can do that, but hopefully someone more experienced on it can clarify that to me. I can see that it can take data in both forms, arm and trial level, but it looks as if the arm-level information would be converted into trial one through escalc and the latter then used for the meta-analysis. Is that right? Many thanks. Angelo -- NIHR Research Methods Training Fellow, Department of Community Based Medicine University of Bristol 25 Belgrave Road Bristol BS8 2AA Tel. 0779 265-6552 __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
