This R package boot.heterogeneity provides functions for testing between-study heterogeneity in meta-analysis of standardized mean differences (d), Fisher-transformed Pearson’s correlations (r), and natural-logarithm-transformed odds ratio (OR).
In the following examples, we describe how to use our package boot.heterogeneity to test the between-study heterogeneity for each of the three effect sizes (d, r, OR). Datasets, R codes, and Output are provided for each example so that applied researchers can easily replicate the examples and modify the codes for their own datasets.
The three example datasets are internal datasets in our package, and researchers can load the datasets using boot.heterogeneity:::[dataset name]. In each of the example datasets, the rows correspond to studies in meta-analysis, and the columns correspond to required input for that study, which includes, but is not limited to effect size, sample size(s), and moderators.
The example R codes adopt the default value for some of the arguments (e.g., default nominal alpha level is 0.05). To change the defaults, use help() to see more details for each of the functions.
The output are formatted to have the same layout across the examples.
In the main text of the article, an “Empirical Illustration” section is included to discuss the three examples in more detail.
Inclusion of moderators is an option for researchers who are interested in measuring the between-study heterogeneity per se and exploring factors that can explain the systematic between-study heterogeneity.
Install the released version of boot.heterogeneity from CRAN with:
Or install the development version from GitHub with:
#install.packages("devtools")
library(devtools)
devtools::install_github("gabriellajg/boot.heterogeneity", force = TRUE, build_vignettes = TRUE)
library(boot.heterogeneity)boot.d() is the function to test the between-study heterogeneity in meta-analysis of standardized mean differences (d).
Load the example dataset selfconcept first:
selfconcept consists of 18 studies in which the effect of open versus traditional education on students’ self-concept was studied (Hedges et al., 1981). The columns of selfconcept are: sample sizes of the two groups (n1 and n2), Hedges’s g, Cohen’s d, and a moderator X (X not used in the current example).
head(selfconcept, 3)
#> n1 n2 g d X
#> 1 100 180 0.100 0.09972997 0.100
#> 2 131 138 -0.162 -0.16154452 -0.162
#> 3 40 40 -0.090 -0.08913183 -0.091Extract the required arguments from selfconcept:
# n1 and n2 are lists of samples sizes in two groups
n1 <- selfconcept$n1
n2 <- selfconcept$n2
# g is a list of effect sizes
g <- selfconcept$gIf g is a list of biased estimates of standardized mean differences in the meta-analytical study, a small-sample adjustment must be applied:
cm <- (1-3/(4*(n1+n2-2)-1)) #correct factor to compensate for small sample bias (Hedges, 1981)
d <- cm*gRun the heterogeneity test using function boot.d() and adjusted effect size d:
Alternatively, such an adjustment can be performed on unadjusted effect size g by specifying adjust = TRUE:
boot.run and boot.run2 will return the same results:
boot.run
#> stat p_value Heterogeneity
#> Qtest 23.391659 0.136929 n.s
#> boot.Qtest 23.391659 0.127800 n.s
#> boot.REML 2.037578 0.053100 n.sboot.run2
#> stat p_value Heterogeneity
#> Qtest 23.391659 0.136929 n.s
#> boot.Qtest 23.391659 0.127800 n.s
#> boot.REML 2.037578 0.053100 n.sThe first line presents the results of Q-test of a random-effects model. The Q-statistic is Q(df = 17) = 23.39 and the associated p-value is 0.137. Using a cutoff alpha level (i.e., nominal alpha level) of either 0.05 or 0.1, this statistic is n.s (not significant). The homogeneity assumption is not rejected.
The second line presents the results of bootstrap-based Q test. The Q-statistic is still 23.39 but the bootstrap-based p-value is 0.128. This statistic is n.s (not significant) and the assumption of homogeneity is not rejected with an alpha level of 0.05.
The third line presents the results of MC-REML-LRT. The REML-LRT statistic is 2.04 and the bootstrap-based p-value is 0.053. Similar to the results of MC-ML-LR, the assumption of homogeneity is not rejected with an alpha level of 0.05 but will be rejected at an alpha level of 0.1.
Load an hypothetical dataset hypo_moder first:
Three moderators (cov.z1, cov.z2, cov.z3) are included:
head(hypo_moder)
#> n1 n2 d cov.z1 cov.z2 cov.z3
#> 1 59 65 0.8131324 -0.005767173 0.80418951 1.2383041
#> 2 166 165 1.0243732 2.404653389 -0.05710677 -0.2793463
#> 3 68 68 1.5954236 0.763593461 0.50360797 1.7579031
#> 4 44 31 0.6809888 -0.799009249 1.08576936 0.5607461
#> 5 98 95 -1.3017946 -1.147657009 -0.69095384 -0.4527840
#> 6 44 31 -1.9398508 -0.289461574 -1.28459935 -0.8320433Again, run the heterogeneity test using boot.d() with all moderators included in a matrix mods and model type specified as model = 'mixed':
boot.run3 <- boot.d(n1 = hypo_moder$n1,
n2 = hypo_moder$n2,
est = hypo_moder$d,
model = 'mixed',
mods = cbind(hypo_moder$cov.z1, hypo_moder$cov.z2, hypo_moder$cov.z3),
p_cut = 0.05)The results in boot.run3 will in the same format as boot.run and boot.run2:
boot.run3
#> stat p_value Heterogeneity
#> Qtest 31.849952 0.000806 sig
#> boot.Qtest 31.849952 0.000600 sig
#> boot.REML 9.283428 0.000400 sigIn the presence of moderators, the function above tests whether the variability in the true standardized mean differences after accounting for the moderators included in the model is larger than sampling variability alone (Viechtbauer, 2010).
In the first line, the Q-statistic is Q(df = 11) = 31.85 and the associated p-value is 0.0008. This statistic is significant (sig) at an alpha level of 0.05, meaning that the true effect sizes after accounting for the moderators are heterogeneous.
In the second line, the Q-statistic is still 31.85 but the bootstrap-based p-value is 0.0006. This means that the true effect sizes after accounting for the moderators are heterogeneous at an alpha level of 0.05.
In the third line, the REML-LRT statistic is 9.28 and the bootstrap-based p-value is 0.0004. This means that the true effect sizes after accounting for the moderators are heterogeneous at an alpha level of 0.05.
boot.fcor() is the function to test the between-study heterogeneity in meta-analysis of Fisher-transformed Pearson’s correlations (r).
Load the example dataset sensation first:
Extract the required arguments from sensation:
# n is a list of samples sizes
n <- sensation$n
# Pearson's correlation
r <- sensation$r
# Fisher's Transformation
z <- 1/2*log((1+r)/(1-r))Run the heterogeneity test using boot.fcor():
The test of between-study heterogeneity has the following results:
boot.run.cor
#> stat p_value Heterogeneity
#> Qtest 29.060970 0.00385868 sig
#> boot.Qtest 29.060970 0.00240072 sig
#> boot.REML 6.133111 0.00400882 sigIn the first line, the Q-statistic is Q(df = 12) = 29.06 and the associated p-value is 0.004. This statistic is significant (sig) at an alpha level of 0.05, meaning that the true effect sizes are heterogeneous.
In the second line, the Q-statistic is still 29.06 but the bootstrap-based p-value is 0.0024. This means that the true effect sizes are heterogeneous at an alpha level of 0.05.
In the third line, the REML-LRT statistic is 6.13 and the bootstrap-based p-value is 0.004. This means that the true effect sizes are heterogeneous at an alpha level of 0.05.
boot.lnOR() is the function to test the between-study heterogeneity in meta-analysis of Natural-logarithm-transformed odds ratio (OR).
Load the example dataset smoking from R package HSAUR2:
Extract the required arguments from smoking:
# Y1: receive treatment; Y2: stop smoking
n_00 <- smoking$tc - smoking$qc # not receive treatement yet not stop smoking
n_01 <- smoking$qc # not receive treatement but stop smoking
n_10 <- smoking$tt - smoking$qt # receive treatement but not stop smoking
n_11 <- smoking$qt # receive treatement and stop smokingThe log odds ratios can be computed, but they are not needed by boot.lnOR():
lnOR <- log(n_11*n_00/n_01/n_10)
lnOR
#> [1] 0.6151856 -0.0235305 0.5658078 0.4274440 1.0814445 0.9109288
#> [7] 0.9647431 0.7103890 1.0375520 -0.1407277 0.7747272 1.7924180
#> [13] 1.2021192 0.3607987 0.2876821 0.2110139 1.2591392 0.1549774
#> [19] 1.3411739 0.2963470 0.6116721 0.3786539 0.5389965 0.7532417
#> [25] 0.5653138 0.3786539Run the heterogeneity test using boot.lnOR():
The test of between-study heterogeneity has the following results:
boot.run.lnOR
#> stat p_value Heterogeneity
#> Qtest 34.873957 0.09050857 n.s
#> boot.Qtest 34.873957 0.08826793 n.s
#> boot.REML 3.071329 0.03706729 sigIn the first line, the Q-statistic is Q(df = 25) = 34.87 and the associated p-value is 0.091. This statistic is not significant (n.s) at an alpha level of 0.05, meaning that the assumption of homogeneity cannot be rejected.
In the second line, the Q-statistic is still 34.87 but the bootstrap-based p-value is 0.088. This means that the assumption of homogeneity marginally significant but not rejected at an alpha level of 0.05.
In the third line, the REML-LRT statistic is 3.07 and the bootstrap-based p-value is 0.037. This means that the assumption of homogeneity is rejected and the true effect sizes are heterogeneous at an alpha level of 0.05.
sessionInfo()
#> R version 3.6.3 (2020-02-29)
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#> Running under: macOS Catalina 10.15.4
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#> attached base packages:
#> [1] tools stats graphics grDevices utils datasets methods
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#> other attached packages:
#> [1] HSAUR2_1.1-17 boot.heterogeneity_0.1.0
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#> [5] nlme_3.1-147 metafor_2.4-0 magrittr_1.5 evaluate_0.14
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