https://github.com/merck/metalite.sl

An R package designed for the analysis & reporting of subject baseline characteristics in clinical trials.
https://github.com/merck/metalite.sl

Last synced: 9 months ago
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An R package designed for the analysis & reporting of subject baseline characteristics in clinical trials.

• Host: GitHub
• URL: https://github.com/merck/metalite.sl
• Owner: Merck
• License: gpl-3.0
• Created: 2024-03-29T21:03:50.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-05-22T14:12:39.000Z (almost 2 years ago)
• Last Synced: 2024-05-22T14:30:01.164Z (almost 2 years ago)
• Language: R
• Homepage: https://merck.github.io/metalite.sl/
• Size: 1.47 MB
• Stars: 0
• Watchers: 5
• Forks: 0
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

          
# metalite.sl 

[![Codecov test coverage](https://codecov.io/gh/Merck/metalite.sl/branch/main/graph/badge.svg)](https://app.codecov.io/gh/Merck/metalite.sl?branch=main)

[![R-CMD-check](https://github.com/Merck/metalite.sl/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/Merck/metalite.sl/actions/workflows/R-CMD-check.yaml)

## Overview

R package designed for the analysis & reporting of subject baseline characteristics in clinical trials.

We assume ADaM datasets are ready for analysis and

leverage [metalite](https://merck.github.io/metalite/) data structure to define

inputs and outputs.

## Workflow

The general workflow is:

1. Define metadata information using metalite.

2. `prepare_sl_summary()` prepares datasets for summary of baseline characteristics.

3. `format_base_char()` formats output layout.

4. `rtf_base_char()` creates TLFs.

Here is a quick example

```r

library("metalite.sl")

meta_sl_example() |>

  prepare_sl_summary(

    population = "apat",

    observation = "apat",

    parameter = "age;gender"

  ) |>

  format_base_char() |>

  rtf_base_char(

    source = "Source: [CDISCpilot: adam-adsl]",

    path_outdata = tempfile(fileext = ".Rdata"),

    path_outtable = tempfile(fileext = ".rtf")

  )

```

## Highlighted features

- Avoid duplicated input by using metadata structure.

  - For example, define analysis population once to use in all adverse events analysis.

- Consistent input and output in standard functions.

- Streamlines mock table generation.