REmrt: Random effects meta-tree

In metacart: Meta-CART: A Flexible Approach to Identify Moderators in Meta-Analysis

Description

A function to fit a random effects meta-tree

Usage

REmrt(
  formula,
  data,
  vi,
  c = 1,
  maxL = 5,
  minsplit = 6,
  cp = 1e-05,
  minbucket = 3,
  xval = 10,
  lookahead = FALSE,
  ...
)

Arguments

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.

Value

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

See Also

summary.REmrt, plot.REmrt

Examples

data(dat.BCT2009)
library(Rcpp)
REtree <- REmrt(g ~ T1 + T2+ T4 +T25, vi = vi, data = dat.BCT2009, c = 0)
summary(REtree)
plot(REtree)

Example output

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 

metacart documentation built on July 10, 2020, 5:06 p.m.