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https://github.com/insightsengineering/cardx
R Package to Supplement ARD Functions Found in {cards}
https://github.com/insightsengineering/cardx
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R Package to Supplement ARD Functions Found in {cards}
- Host: GitHub
- URL: https://github.com/insightsengineering/cardx
- Owner: insightsengineering
- License: other
- Created: 2024-01-15T12:27:13.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-11-01T22:05:56.000Z (7 days ago)
- Last Synced: 2024-11-01T22:38:27.539Z (7 days ago)
- Topics: r
- Language: R
- Homepage: https://insightsengineering.github.io/cardx/
- Size: 20.5 MB
- Stars: 14
- Watchers: 6
- Forks: 2
- Open Issues: 17
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
- Security: SECURITY.md
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README
---
output: github_document
editor_options:
markdown:
wrap: 72
---
# cardx 
[](https://github.com/insightsengineering/cardx/actions/workflows/R-CMD-check.yaml)
[](https://app.codecov.io/gh/insightsengineering/cardx?branch=main)
[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
The **{cardx}** package is an extension of the {cards} package, providing additional functions to create Analysis Results Data Objects (ARDs) using the **R** programming language.
The {cardx} package exports ARD functions that uses utility functions from {cards} and statistical functions from additional packages (such as, {stats}, {mmrm}, {emmeans}, {car}, {survey}, etc.) to construct summary objects.
Summary objects can be used to:
- **Generate Tables and visualizations for Regulatory Submission**
easily in **R**. Perfect for presenting descriptive statistics,
statistical analyses, regressions, etc. and more.
- **Conduct Quality Control checks on existing Tables** in R.
Storing both the results and test parameters supports the re-use and
verification of data analyses.
## Installation
Install cards from CRAN with:
```r
install.packages("cardx")
```
You can install the development version of cards from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("insightsengineering/cardx")
```
## Examples
### Example ARD Creation
Example t-test:
```{r}
library(cardx)
cards::ADSL |>
# keep two treatment arms for the t-test calculation
dplyr::filter(ARM %in% c("Placebo", "Xanomeline High Dose")) |>
cardx::ard_stats_t_test(by = ARM, variable = AGE)
```
Note that the returned ARD contains the analysis results in addition to
the function parameters used to calculate the results allowing for
reproducible future analyses and further customization.
### Model Input
Some {cardx} functions accept regression model objects as input:
```{r, eval=FALSE}
lm(AGE ~ ARM, data = cards::ADSL) |>
ard_aod_wald_test()
```
Note that the [Analysis Results Standard](https://www.cdisc.org/standards/foundational/analysis-results-standard) should begin with a data set rather than a model object.
To accomplish this we include model construction helpers.
```{r}
construct_model(
data = cards::ADSL,
formula = reformulate2("ARM", response = "AGE"),
method = "lm"
) |>
ard_aod_wald_test()
```
## Additional Resources
- The best resources are the help documents accompanying each {cardx} function.
- Supporting documentation for both companion packages [{cards}](https://insightsengineering.github.io/cards/) and {[gtsummary](https://www.danieldsjoberg.com/gtsummary/index.html)} will be useful for understanding the ARD workflow and capabilities.