Description
Causally Interpretable Meta-Analysis.
Description
Provides robust and efficient methods for estimating causal effects in a target population using a multi-source dataset, including those of Dahabreh et al. (2019) <doi:10.1111/biom.13716>, Robertson et al. (2021) <doi:10.48550/arXiv.2104.05905>, and Wang et al. (2024) <doi:10.48550/arXiv.2402.02684>. 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 and subgroup treatment effects. See Wang et al. (2024) <doi:10.48550/arXiv.2402.04341> for a detailed guide on using the package.
README.md
CausalMetaR
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 and subgroup treatment effects.
Installation
You can install the development version of CausalMetaR from GitHub with:
# install.packages("devtools")
devtools::install_github("ly129/CausalMetaR")