metafor modeling within-study variance The Next CEO of Stack Overflowmetafor: Including data in forest plot but not meta-analysis modelMeta-analysis: Forest plot of summary estimates using metafor packageMeta-regression with metafor: Where has the variance gone?interpreting meta-regression outputs from metaforAnnual rate of change effect size Meta-analysis in MetaforMeta-analysis in RHow can I^2 ==0 (heterogeneity measure) in RMA random effects meta analysis in R?specify fixed effect two-level variable rma.mvCreate a forest plot for weighted mean effect size for each study in meta-analysisHow can I use a glmer output for rma.glmm input in mixed model logistic regression meta-analysis?
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metafor modeling within-study variance
The Next CEO of Stack Overflowmetafor: Including data in forest plot but not meta-analysis modelMeta-analysis: Forest plot of summary estimates using metafor packageMeta-regression with metafor: Where has the variance gone?interpreting meta-regression outputs from metaforAnnual rate of change effect size Meta-analysis in MetaforMeta-analysis in RHow can I^2 ==0 (heterogeneity measure) in RMA random effects meta analysis in R?specify fixed effect two-level variable rma.mvCreate a forest plot for weighted mean effect size for each study in meta-analysisHow can I use a glmer output for rma.glmm input in mixed model logistic regression meta-analysis?
I am preparing a correlational meta-analysis. Some studies in my sample provide multiple effect sizes based on various different measures for the predictor, the criterion variable, or both. To avoid that I over- or underestimate the precision of the final estimate, I wanted to model both within-study variance and between-study variance. Therefore, I entered several effect sizes for study 1 and several effect sizes for further studies.
Here is a sample of my data:
> study<-c(1,1,1,2,3,3)
> number<-c(1:6)
> yi<-c(.12,.18,.05,.14,.19,.24)
> vi<-c(.0005,.0005,.0005,.0012,.0009,.0008)
> example<-data.frame(study, number, yi,vi)
> head(example)
study number yi vi
1 1 1 0.12 0.0005
2 1 2 0.18 0.0005
3 1 3 0.05 0.0005
4 2 4 0.14 0.0012
5 3 5 0.19 0.0009
6 3 6 0.24 0.0008
I followed recommendations outlined here to fit a three-level meta-analysis: http://www.metafor-project.org/doku.php/analyses:konstantopoulos2011
> res.mv <- rma.mv(yi, vi, random = ~ factor(number) | study, data=example)
> print(res.mv, digits=3)
Multivariate Meta-Analysis Model (k = 6; method: REML)
Variance Components:
outer factor: study (nlvls = 3)
inner factor: factor(number) (nlvls = 6)
estim sqrt fixed
tau^2 0.004 0.064 no
rho 0.384 no
Test for Heterogeneity:
Q(df = 5) = 35.299, p-val < .001
Model Results:
estimate se zval pval ci.lb ci.ub
0.156 0.034 4.608 <.001 0.090 0.222 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
However, I later found out that the model used above assumes that the sampling errors of the effect size estimates are independent, which is obviously not the case here, as multiple estimates are obtained from the same group of people. Is there anything I can do to account for this dependency in the model?
r hierarchical multi-level metafor
add a comment |
I am preparing a correlational meta-analysis. Some studies in my sample provide multiple effect sizes based on various different measures for the predictor, the criterion variable, or both. To avoid that I over- or underestimate the precision of the final estimate, I wanted to model both within-study variance and between-study variance. Therefore, I entered several effect sizes for study 1 and several effect sizes for further studies.
Here is a sample of my data:
> study<-c(1,1,1,2,3,3)
> number<-c(1:6)
> yi<-c(.12,.18,.05,.14,.19,.24)
> vi<-c(.0005,.0005,.0005,.0012,.0009,.0008)
> example<-data.frame(study, number, yi,vi)
> head(example)
study number yi vi
1 1 1 0.12 0.0005
2 1 2 0.18 0.0005
3 1 3 0.05 0.0005
4 2 4 0.14 0.0012
5 3 5 0.19 0.0009
6 3 6 0.24 0.0008
I followed recommendations outlined here to fit a three-level meta-analysis: http://www.metafor-project.org/doku.php/analyses:konstantopoulos2011
> res.mv <- rma.mv(yi, vi, random = ~ factor(number) | study, data=example)
> print(res.mv, digits=3)
Multivariate Meta-Analysis Model (k = 6; method: REML)
Variance Components:
outer factor: study (nlvls = 3)
inner factor: factor(number) (nlvls = 6)
estim sqrt fixed
tau^2 0.004 0.064 no
rho 0.384 no
Test for Heterogeneity:
Q(df = 5) = 35.299, p-val < .001
Model Results:
estimate se zval pval ci.lb ci.ub
0.156 0.034 4.608 <.001 0.090 0.222 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
However, I later found out that the model used above assumes that the sampling errors of the effect size estimates are independent, which is obviously not the case here, as multiple estimates are obtained from the same group of people. Is there anything I can do to account for this dependency in the model?
r hierarchical multi-level metafor
add a comment |
I am preparing a correlational meta-analysis. Some studies in my sample provide multiple effect sizes based on various different measures for the predictor, the criterion variable, or both. To avoid that I over- or underestimate the precision of the final estimate, I wanted to model both within-study variance and between-study variance. Therefore, I entered several effect sizes for study 1 and several effect sizes for further studies.
Here is a sample of my data:
> study<-c(1,1,1,2,3,3)
> number<-c(1:6)
> yi<-c(.12,.18,.05,.14,.19,.24)
> vi<-c(.0005,.0005,.0005,.0012,.0009,.0008)
> example<-data.frame(study, number, yi,vi)
> head(example)
study number yi vi
1 1 1 0.12 0.0005
2 1 2 0.18 0.0005
3 1 3 0.05 0.0005
4 2 4 0.14 0.0012
5 3 5 0.19 0.0009
6 3 6 0.24 0.0008
I followed recommendations outlined here to fit a three-level meta-analysis: http://www.metafor-project.org/doku.php/analyses:konstantopoulos2011
> res.mv <- rma.mv(yi, vi, random = ~ factor(number) | study, data=example)
> print(res.mv, digits=3)
Multivariate Meta-Analysis Model (k = 6; method: REML)
Variance Components:
outer factor: study (nlvls = 3)
inner factor: factor(number) (nlvls = 6)
estim sqrt fixed
tau^2 0.004 0.064 no
rho 0.384 no
Test for Heterogeneity:
Q(df = 5) = 35.299, p-val < .001
Model Results:
estimate se zval pval ci.lb ci.ub
0.156 0.034 4.608 <.001 0.090 0.222 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
However, I later found out that the model used above assumes that the sampling errors of the effect size estimates are independent, which is obviously not the case here, as multiple estimates are obtained from the same group of people. Is there anything I can do to account for this dependency in the model?
r hierarchical multi-level metafor
I am preparing a correlational meta-analysis. Some studies in my sample provide multiple effect sizes based on various different measures for the predictor, the criterion variable, or both. To avoid that I over- or underestimate the precision of the final estimate, I wanted to model both within-study variance and between-study variance. Therefore, I entered several effect sizes for study 1 and several effect sizes for further studies.
Here is a sample of my data:
> study<-c(1,1,1,2,3,3)
> number<-c(1:6)
> yi<-c(.12,.18,.05,.14,.19,.24)
> vi<-c(.0005,.0005,.0005,.0012,.0009,.0008)
> example<-data.frame(study, number, yi,vi)
> head(example)
study number yi vi
1 1 1 0.12 0.0005
2 1 2 0.18 0.0005
3 1 3 0.05 0.0005
4 2 4 0.14 0.0012
5 3 5 0.19 0.0009
6 3 6 0.24 0.0008
I followed recommendations outlined here to fit a three-level meta-analysis: http://www.metafor-project.org/doku.php/analyses:konstantopoulos2011
> res.mv <- rma.mv(yi, vi, random = ~ factor(number) | study, data=example)
> print(res.mv, digits=3)
Multivariate Meta-Analysis Model (k = 6; method: REML)
Variance Components:
outer factor: study (nlvls = 3)
inner factor: factor(number) (nlvls = 6)
estim sqrt fixed
tau^2 0.004 0.064 no
rho 0.384 no
Test for Heterogeneity:
Q(df = 5) = 35.299, p-val < .001
Model Results:
estimate se zval pval ci.lb ci.ub
0.156 0.034 4.608 <.001 0.090 0.222 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
However, I later found out that the model used above assumes that the sampling errors of the effect size estimates are independent, which is obviously not the case here, as multiple estimates are obtained from the same group of people. Is there anything I can do to account for this dependency in the model?
r hierarchical multi-level metafor
r hierarchical multi-level metafor
asked Mar 8 at 19:01
Saja01Saja01
63
63
add a comment |
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