tips:comp_two_independent_estimates
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Both sides previous revisionPrevious revisionNext revision | Previous revisionNext revisionBoth sides next revision | ||
tips:comp_two_independent_estimates [2020/07/03 10:32] – Wolfgang Viechtbauer | tips:comp_two_independent_estimates [2022/08/03 11:31] – Wolfgang Viechtbauer | ||
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</ | </ | ||
- | We can now compare the two estimates (i.e., the estimated average log risk ratios) by feeding them back to the '' | + | We can now compare the two estimates (i.e., the estimated average log risk ratios) by feeding them back to the '' |
<code rsplus> | <code rsplus> | ||
rma(estimate, | rma(estimate, | ||
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Fixed-Effects with Moderators Model (k = 2) | Fixed-Effects with Moderators Model (k = 2) | ||
- | Test for Residual Heterogeneity: | + | Test for Residual Heterogeneity: |
QE(df = 0) = 0.000, p-val = 1.000 | QE(df = 0) = 0.000, p-val = 1.000 | ||
- | Test of Moderators (coefficient(s) 2): | + | Test of Moderators (coefficient(s) 2): |
QM(df = 1) = 1.946, p-val = 0.163 | QM(df = 1) = 1.946, p-val = 0.163 | ||
Model Results: | Model Results: | ||
- | estimate | + | estimate |
intrcpt | intrcpt | ||
- | metarandom | + | metarandom |
--- | --- | ||
Line 78: | Line 78: | ||
</ | </ | ||
<code output> | <code output> | ||
- | zval | + | zval |
-1.395 | -1.395 | ||
</ | </ | ||
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R^2 (amount of heterogeneity accounted for): 0.00% | R^2 (amount of heterogeneity accounted for): 0.00% | ||
- | Test for Residual Heterogeneity: | + | Test for Residual Heterogeneity: |
QE(df = 11) = 138.511, p-val < .001 | QE(df = 11) = 138.511, p-val < .001 | ||
- | Test of Moderators (coefficient(s) 2): | + | Test of Moderators (coefficient(s) 2): |
QM(df = 1) = 1.833, p-val = 0.176 | QM(df = 1) = 1.833, p-val = 0.176 | ||
Model Results: | Model Results: | ||
- | | + | |
intrcpt | intrcpt | ||
- | allocrandom | + | allocrandom |
--- | --- | ||
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Multivariate Meta-Analysis Model (k = 13; method: REML) | Multivariate Meta-Analysis Model (k = 13; method: REML) | ||
- | Variance Components: | + | Variance Components: |
outer factor: trial (nlvls = 13) | outer factor: trial (nlvls = 13) | ||
Line 135: | Line 135: | ||
tau^2.2 | tau^2.2 | ||
- | Test for Residual Heterogeneity: | + | Test for Residual Heterogeneity: |
QE(df = 11) = 138.511, p-val < .001 | QE(df = 11) = 138.511, p-val < .001 | ||
- | Test of Moderators (coefficient(s) 2): | + | Test of Moderators (coefficient(s) 2): |
QM(df = 1) = 1.946, p-val = 0.163 | QM(df = 1) = 1.946, p-val = 0.163 | ||
Model Results: | Model Results: | ||
- | | + | |
intrcpt | intrcpt | ||
- | allocrandom | + | allocrandom |
--- | --- | ||
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A discussion/ | A discussion/ | ||
- | Rubio-Aparicio, | + | Rubio-Aparicio, |
We can also do a likelihood ratio test (LRT) to examine whether there are significant differences in the $\tau^2$ values across subsets. This can be done with: | We can also do a likelihood ratio test (LRT) to examine whether there are significant differences in the $\tau^2$ values across subsets. This can be done with: | ||
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</ | </ | ||
<code output> | <code output> | ||
- | df | + | df |
- | Full 4 29.2959 30.8875 35.9626 -10.6480 | + | Full 4 29.2959 30.8875 35.9626 -10.6480 |
Reduced | Reduced | ||
</ | </ | ||
So in this example, we would not reject the null hypothesis $H_0: \tau^2_1 = \tau^2_2$ ($p = .58$). | So in this example, we would not reject the null hypothesis $H_0: \tau^2_1 = \tau^2_2$ ($p = .58$). |
tips/comp_two_independent_estimates.txt · Last modified: 2024/04/18 11:36 by Wolfgang Viechtbauer