The CDISC Analysis Results Standard aims to facilitate automation, reproducibility, reusability, and traceability of analysis results data (ARD). The {cards} package creates these CDISC Analysis Result Data Sets.
Use cases:
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Quality Control (QC) of existing tables and figures.
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Pre-calculate statistics to be summarized in tables and figures.
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Medical writers may easily access statistics and place in reports without copying and pasting from reports.
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Provides a consistent format for results and lends results to be combined across studies for re-use and re-analysis.
Install cards from CRAN with:
install.packages("cards")You can install the development version of cards from GitHub with:
# install.packages("devtools")
devtools::install_github("insightsengineering/cards")
The {cards} package exports three types of functions:
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Functions to create basic ARD objects.
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Utilities to create new ARD objects.
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Functions to work with existing ARD objects.
The {cardx} R package is an extension to {cards} that uses the utilities from {cards} and exports functions for creating additional ARD objects––including functions to summarize t-tests, Wilcoxon Rank-Sum tests, regression models, and more.
Review the Getting Started page for examples using ARDs to calculate statistics to later include in tables.
library(cards)
ard_continuous(ADSL, by = "ARM", variables = "AGE")
#> {cards} data frame: 24 x 10
#> group1 group1_level variable stat_name stat_label stat
#> 1 ARM Placebo AGE N N 86
#> 2 ARM Placebo AGE mean Mean 75.209
#> 3 ARM Placebo AGE sd SD 8.59
#> 4 ARM Placebo AGE median Median 76
#> 5 ARM Placebo AGE p25 Q1 69
#> 6 ARM Placebo AGE p75 Q3 82
#> 7 ARM Placebo AGE min Min 52
#> 8 ARM Placebo AGE max Max 89
#> 9 ARM Xanomeli… AGE N N 84
#> 10 ARM Xanomeli… AGE mean Mean 74.381
#> ℹ 14 more rows
#> ℹ Use `print(n = ...)` to see more rows
#> ℹ 4 more variables: context, fmt_fn, warning, error-
Posit 2024 pharmaverse Workshop: Follow the link for “Analysis Results Datasets” in the schedule.