Description Usage Arguments Value See Also Examples
A function to fit a random effects meta-tree
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formula |
A formula, with a response variable (usually the effect size) and the potential moderator variables but no interaction terms. |
data |
A data frame of a meta-analytic data set, including the study effect sizes, sampling variance, and the potential moderators. |
vi |
sampling variance of the effect size. |
c |
A non-negative scalar.The pruning parameter to prune the initial tree by the "c*standard-error" rule. |
maxL |
the maximum number of splits |
minsplit |
the minimum number of studies in a parent node before splitting |
cp |
the stopping rule for the decrease of between-subgroups Q. Any split that does not decrease the between-subgroups Q is not attempted. |
minbucket |
the minimum number of the studies in a terminal node |
xval |
the number of folds to perform the cross-validation |
lookahead |
an argument indicating whether to apply the "look-ahead" strategy when fitting the tree |
... |
Additional arguments to be passed. |
If (a) moderator effect(s) is(are) detected, the function will return a list including the following objects:
tree: A data frame that represents the tree, with the Q-between and the residual heterogeneity (tau^2) after each split.
n: The number of the studies in each subgroup
moderators: the names of identified moderators
Qb: The between-subgroups Q-statistic
tau2: The estimate of the residual heterogeneity
df: The degrees of freedom of the between-subgroups Q test
pval.Qb: The p-value of the between-subgroups Q test
g: The subgroup summary effect size, based on Hedges'g
se: The standard error of subgroup summary effect size
zval: The test statistic of the subgroup summary effect size
pval: The p-value of the test statistic of the subgroup summary effect size
ci.lb: The lower bound of the confidence interval
ci.ub: The upper bound of the confidence interval
call: The matched call
cv.res: The cross-validation table
data: the data set subgrouped by the fitted tree
If no moderator effect is detected, the function will return a list including the following objects:
n: The total number of the studies
Q: The Q-statistics for the heterogeneity test
df: The degree of freedoms of the heterogeneity test
pval.Q: The p-value for the heterogeneity test
g: The summary effect size for all studies (i.e., the overall effect size)
se: The standard error of the summary effect size
zval: The test statistic of the summary effect size
pval: The p-value for the test statistic of the summary effect size
ci.lb: The lower bound of the confidence interval for the summary effect size
ci.ub: The upper bound of the confidence interval for the summary effect size
call: The matched call
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Loading required package: ggplot2
Loading required package: gridExtra
Loading required package: rpart
Random Effects meta-tree (K = 106 studies);
REmrt(formula = g ~ T1 + T2 + T4 + T25, data = dat.BCT2009, vi = vi,
c = 0)
A tree with 4 terminal nodes was detected
Moderators were detected as: T1, T4, T2
Test for Between-Subgroups Heterogeneity under RE assumption:
Qb = 15.902 (df = 3), p-value 0.0011874;
The estimate for the residual heterogeneity tau2 = 0.017;
Subgroup Meta-analysis Results:
K g se zval pval ci.lb ci.ub
2 69 0.241 0.025 9.829 0.000 0.193 0.289 ***
5 22 0.429 0.050 8.590 0.000 0.331 0.527 ***
6 5 0.076 0.093 0.810 0.418 -0.107 0.259
7 10 0.255 0.063 4.050 0.000 0.132 0.379 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
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