The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
The CausalMetaR package provides robust and efficient
methods for estimating causal effects in a target population using a
multi-source dataset. The multi-source data can be a collection of
trials, observational studies, or a combination of both, which have the
same data structure (outcome, treatment, and covariates). The target
population can be based on an internal dataset or an external dataset
where only covariate information is available. The causal estimands
available are average treatment effects (ATEs) and subgroup treatment
effects (STEs).
You can install the released version of CausalMetaR from
CRAN
with:
install.packages("CausalMetaR")You can install the development version of CausalMetaR
from GitHub with:
# install.packages("devtools")
devtools::install_github("ly129/CausalMetaR")Refer to Wang et
al. (2025) for a detailed guide on using the package. Below, we
include the code used in the examples in the paper. The examples are
based on the multi-source dataset dat_multisource included
in the package.
Loading the package:
library(CausalMetaR)Specifying the working models:
outcome_model_args <- list(family = gaussian(),
SL.library = c("SL.glmnet", "SL.nnet", "SL.glm"))
treatment_model_args <- list(family = binomial(),
SL.library = c("SL.glmnet", "SL.nnet", "SL.glm"))
external_model_args = list(family = binomial(),
SL.library = c("SL.glmnet", "SL.nnet", "SL.glm"))Setting a random number seed for reproducibility:
set.seed(1234)The examples below estimate ATEs and STEs in external and internal target populations. Each example may take a few minutes to run on standard laptop.
result_ae <- ATE_external(
Y = dat_multisource$Y,
S = dat_multisource$S,
A = dat_multisource$A,
X = dat_multisource[, 1:10],
X_external = dat_external[, 1:10],
outcome_model_args = outcome_model_args,
treatment_model_args = treatment_model_args,
external_model_args = external_model_args,
cross_fitting = TRUE,
replications = 5)
#> Loading required package: nnls
#> Loading required namespace: nnet
result_ae
#> AVERAGE TREATMENT EFFECT ESTIMATES IN AN EXTERNAL POPULATION
#>
#> Treatment effect (mean difference) estimates:
#> ---------------------------------------------
#> Estimate SE Lower 95% CI Upper 95% CI
#> 6.6294 0.1535 5.8616 7.3972result_ai <- ATE_internal(
Y = dat_multisource$Y,
S = dat_multisource$S,
A = dat_multisource$A,
X = dat_multisource[, 1:10],
outcome_model_args = outcome_model_args,
treatment_model_args = treatment_model_args,
cross_fitting = TRUE,
replications = 5)
result_ai
#> AVERAGE TREATMENT EFFECT ESTIMATES IN INTERNAL POPULATIONS
#>
#> Treatment effect (mean difference) estimates:
#> ---------------------------------------------
#> Source Estimate SE Lower 95% CI Upper 95% CI
#> A 6.5874 0.1903 6.2145 6.9603
#> B 7.7556 0.2577 7.2506 8.2606
#> C 7.2916 0.3594 6.5872 7.9960result_se <- STE_external(
Y = dat_multisource$Y,
S = dat_multisource$S,
A = dat_multisource$A,
X = dat_multisource[, 2:10],
EM = dat_multisource$EM,
X_external = dat_external[, 2:10],
EM_external = dat_external$EM,
outcome_model_args = outcome_model_args,
treatment_model_args = treatment_model_args,
external_model_args = external_model_args,
cross_fitting = TRUE,
replications = 5)
result_se
#> SUBGROUP TREATMENT EFFECT ESTIMATES IN AN EXTERNAL POPULATION
#>
#> Treatment effect (mean difference) estimates:
#> ---------------------------------------------
#> Subgroup Estimate SE Lower 95% CI Upper 95% CI Lower 95% SCB Upper 95% SCB
#> a 7.0787 0.3563 5.9088 8.2485 5.5453 8.6121
#> b 5.5207 0.2321 4.5764 6.4650 4.2830 6.7585
#> c 7.5709 0.1805 6.7382 8.4037 6.4794 8.6625
#> d 6.5748 0.2253 5.6446 7.5051 5.3556 7.7941
#> e 5.3741 0.3382 4.2343 6.5139 3.8802 6.8681result_si <- STE_internal(
Y = dat_multisource$Y,
S = dat_multisource$S,
A = dat_multisource$A,
X = dat_multisource[, 2:10],
EM = dat_multisource$EM,
outcome_model_args = outcome_model_args,
treatment_model_args = treatment_model_args,
cross_fitting = TRUE,
replications = 5)
result_si
#> SUBGROUP TREATMENT EFFECT ESTIMATES IN INTERNAL POPULATIONS
#>
#> Treatment effect (mean difference) estimates:
#> ---------------------------------------------
#> Source Subgroup Estimate SE Lower 95% CI Upper 95% CI Lower 95% SCB
#> A a 6.9197 0.5001 5.9395 7.8999 5.6345
#> b 5.4340 0.3681 4.7126 6.1555 4.4880
#> c 7.5452 0.3097 6.9383 8.1522 6.7493
#> d 6.5053 0.3630 5.7939 7.2168 5.5724
#> e 5.4595 0.5215 4.4373 6.4816 4.1192
#> B a 8.2134 0.7554 6.7328 9.6939 6.2720
#> b 6.8396 0.5381 5.7850 7.8942 5.4567
#> c 8.7995 0.4232 7.9699 9.6290 7.7118
#> d 7.7098 0.4529 6.8223 8.5974 6.5460
#> e 6.4405 0.6975 5.0734 7.8076 4.6479
#> C a 8.0544 1.2049 5.6929 10.4159 4.9579
#> b 6.2151 0.6711 4.8998 7.5304 4.4904
#> c 8.2620 0.6426 7.0026 9.5215 6.6106
#> d 7.1427 0.6538 5.8612 8.4242 5.4624
#> e 6.0910 0.8718 4.3823 7.7997 3.8504
#> Upper 95% SCB
#> 8.2050
#> 6.3801
#> 8.3411
#> 7.4382
#> 6.7998
#> 10.1547
#> 8.2225
#> 9.8872
#> 8.8737
#> 8.2332
#> 11.1509
#> 7.9398
#> 9.9135
#> 8.8231
#> 8.3316To cite this package, use:
Wang G, McGrath S, Lian Y. CausalMetaR: An R package for performing causally interpretable meta-analyses. Research Synthesis Methods. In press.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.