dat_multisource

<p>Simulated multi-source dataset consisting of 3 sources.</p>

datasets

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.

Sean McGrath

CausalMetaR

Causally Interpretable Meta-Analysis

Yi Lian

Guanbo Wang

Issa Dahabreh

dat_multisource function

<p>A data frame with 3,917 rows and 13 columns. The columns are:</p><table class="table">
<tr><td><code>EM</code></td><td>Effect modifier.</td></tr>
<tr><td><code>X2</code>, ..., <code>X10</code></td><td>Covariates.</td></tr>
<tr><td><code>S</code></td><td>Source indicator.</td></tr>
<tr><td><code>A</code></td><td>Treatment (1 for treated and 0 for untreated).</td></tr>
<tr><td><code>Y</code></td><td>Outcome.</td></tr>
</table><p></p><p></p>

Format

Multi-source dataset — dat_multisource

<p>A data frame with 3,917 rows and 13 columns. The columns are:</p><table class='table'>
<tr><td><code>EM</code></td><td>Effect modifier.</td></tr>
<tr><td><code>X2</code>, ..., <code>X10</code></td><td>Covariates.</td></tr>
<tr><td><code>S</code></td><td>Source indicator.</td></tr>
<tr><td><code>A</code></td><td>Treatment (1 for treated and 0 for untreated).</td></tr>
<tr><td><code>Y</code></td><td>Outcome.</td></tr>
</table>



dat_multisource: Multi-source dataset

Description

Usage

Arguments

Format

`EM`	Effect modifier.
`X2`, ..., `X10`	Covariates.
`S`	Source indicator.
`A`	Treatment (1 for treated and 0 for untreated).
`Y`	Outcome.