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Exploring the Top 5 pharmaverse Packages

Gift Kenneth
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The pharmaverse is a collaborative project where leading pharmaceutical companies and passionate individuals come together to create helpful tools for clinical reporting. By using R programming and the open-source community, the pharmaverse makes it easier to gain insights and increase transparency in the pharma industry.

In this post, let’s explore the top 5 pharmaverse packages (by GitHub Stars ), their interesting features, and how they can be used in clinical data analysis and reporting.

< section id="rtables-crafting-regulatory-ready-tables" class="level2">

{rtables}: Crafting Regulatory-Ready Tables

Let’s start our journey with {rtables}, a package contributed by Roche, which enables the creation of tables for reporting clinical trials. It offers a flexible and efficient way to generate publication-quality tables, simplifying the reporting process and ensuring consistency across different trials.

{rtables} was primarily designed to address the needs of the pharmaceutical industry for creating regulatory-ready tables for clinical trial reporting. You can use it to generate tables to summarize patient demographics, adverse events, efficacy endpoints, and other clinical trial data.

< section id="features-of-rtables" class="level3">

Features of {rtables}

The {rtables} package offers several features that make it a useful tool for creating complex tables:

< section id="example-code" class="level3">

Example Code

The following example demonstrates how to create a basic table using the {rtables} package. We start by installing and loading the required libraries.

Next, we create a simulated dataset representing clinical trial data. We then define a function to calculate the mean of a biomarker and use {rtables} functions to build and print the table.

# Install rtables if it's not already installed
if (!requireNamespace("rtables", quietly = TRUE)) {
  install.packages("rtables")
}

# Load required library
library(rtables)

# Create a simulated dataset
set.seed(123)
ex_adsl <- data.frame(
  ARM = sample(c("Placebo", "Drug X", "Combination"), 100, replace = TRUE),
  BMRKR2 = sample(c("Low", "Medium", "High"), 100, replace = TRUE),
  RACE = sample(c("Asian", "Black", "White"), 100, replace = TRUE),
  SEX = sample(c("Male", "Female"), 100, replace = TRUE),
  BMRKR1 = rnorm(100, mean = 5, sd = 2)
)

# Define a function to calculate the mean of a biomarker
biomarker_ave <- function(x, ...) {
  val <- if (length(x) > 0) round(mean(x), 2) else "no data"
  in_rows("Biomarker 1 (mean)" = rcell(val))
}

# Create the table
table <- basic_table() |>
  split_cols_by("ARM") |>
  split_cols_by("BMRKR2") |>
  add_colcounts() |>
  split_rows_by("RACE", split_fun = trim_levels_in_group("SEX")) |>
  split_rows_by("SEX") |>
  summarize_row_groups() |>
  analyze("BMRKR1", biomarker_ave) |>
  build_table(ex_adsl)

# Print the table
print(table)

Results

You can learn more about rtables:

< section id="admiral-simplifying-adam-dataset-creation" class="level2">

{admiral}: Simplifying ADaM Dataset Creation

Next let’s take a look at the {admiral} package, initially developed through a collaboration between Roche and GSK. It provides a toolbox of reusable functions and utilities with dplyr-like syntax to prepare CDISC ADaM (Analysis Data Model) datasets. It serves as a valuable resource for statistical programmers looking to build ADaMs according to varying analysis needs while ensuring traceability and compliance with FDA standards. Many more pharma companies and CROs have contributed to the package since its inception in 2021.

< section id="features-of-admiral" class="level3">

Features of {admiral}

Let’s take a look at some features of the package:

< section id="example-code-1" class="level3">

Example Code

Let’s see how to use the {admiral} package to prepare an ADaM dataset.

We start by loading the necessary libraries and creating a sample study dataset. We then derive the ADSL (Subject-Level Analysis Dataset) and add additional variables for further analysis.

library(admiral)
library(dplyr)
library(lubridate)

# Sample study data
study_data <- tibble::tribble(
  ~USUBJID, ~AGE, ~SEX, ~ARM, ~RANDDTC, ~RAND2DTC, ~VISIT, ~VISITDY, ~VSDTC, ~VSTPT, ~VSORRESU, ~VSORRES, ~PARAMCD,
  "01", 34, "M", "Placebo", "2022-12-10", "2023-01-15", "Screening", -7, "2023-01-08", "Pre-dose", "kg", 80, "WEIGHT",
  "02", 45, "F", "Treatment", "2023-01-02", "2023-01-17", "Baseline", 1, "2023-01-17", "Pre-dose", "kg", 65, "WEIGHT",
  "03", 54, "F", "", "2022-10-16", "2023-01-09", "Screening", -7, "2023-01-09", "Pre-dose", "kg", 78, "WEIGHT"
)

# Function to aggregate by age group
format_agegr1 <- function(var_input) {
  case_when(
    var_input < 35 ~ "<35",
    between(var_input, 34, 50) ~ "35-49",
    var_input > 50 ~ ">64",
    TRUE ~ "Missing"
  )
}

# Derive ADSL (Subject-Level Analysis Dataset)
adsl <- study_data |>
  select(USUBJID, AGE, SEX, ARM, RANDDTC, RAND2DTC) |>
  distinct() |>
  # Convert blanks strings to NA
  convert_blanks_to_na() |>
  # admiral does not yet support aggregation function, but dplyr can be used
  mutate(
    AGEGR1 = format_agegr1(AGE)
  ) |>
  # Convert from character to DATE
  derive_vars_dt(
    dtc = RANDDTC,
    new_vars_prefix = "TRTS"
  ) |>
  derive_vars_dt(
    dtc = RAND2DTC,
    new_vars_prefix = "TRTE"
  ) |>
  derive_vars_duration(
    new_var = TRTD,
    start_date = TRTSDT,
    end_date = TRTEDT,
    add_one = FALSE
  )

# Display the ADSL dataset
print(adsl)

Results

More on {admiral}:

< section id="teal-interactive-data-exploration" class="level2">

{teal}: Interactive Data Exploration

{teal} is an open-source R Shiny framework developed by Roche that enables the creation of interactive data exploration applications for the pharmaceutical industry. {teal} is particularly well-suited for exploring and analyzing data from clinical trials, enabling researchers and clinicians to quickly identify trends, patterns, and insights.

{teal}’s reporting functionality can be used to generate regulatory-ready tables, figures, and listings. Study teams currently use it to explore data interactively and get the code to reproduce those TLGs. In the future, we hope to use it for submission to governing bodies. It can also be used to build interactive dashboards for monitoring and analyzing adverse events in clinical trials, supporting pharmacovigilance efforts. Its modular design allows for the integration of specialized modules for the analysis and visualization of high-dimensional biomarker data.

< section id="features-of-teal" class="level3">

Features of {teal}

< section id="example" class="level3">

Example

Here’s the Patient Profile {teal} application for patient-level analysis of clinical trial data from the teal.gallery.

Here’s how you can run the app yourself:

source("https://raw.github.com/insightsengineering/teal.gallery/main/_internal/utils/sourceme.R")

# Run the app
restore_and_run("patient-profile", package_repo = "https://insightsengineering.r-universe.dev")

You can also find the deployed version of the application.

Some Resources on {teal}

< section id="riskmetric-assessing-package-quality-and-risk" class="level2">

{riskmetric}: Assessing Package Quality and Risk

The {riskmetric} package provides a framework to quantify the “risk” of R packages by assessing various metrics. Developed by the R Validation Hub, it helps organizations evaluate the quality and suitability of R packages for validated environments. The resulting risk is parameterized by the organization, which makes the decision on how to weigh the risk from the different metrics.

< section id="features-of-riskmetric" class="level3">

Features of {riskmetric}

< section id="example-code-2" class="level3">

Example Code

The following example demonstrates how to use the {riskmetric} package to evaluate the risk of selected R packages. We start by loading the necessary libraries and using {riskmetric} functions to assess and score the packages.

# Load necessary libraries
library(dplyr)
library(riskmetric)

# Assess and score R packages
pkg_ref(c("riskmetric", "utils", "tools")) %>%
  pkg_assess() %>%
  pkg_score()

Results

It is closely related to {riskassessment} (the app’s main goal is to help those making “package inclusion” requests for validated GxP environments) and {riskscore} (data package for cataloging riskmetric results across public repositories).

More on Riskmetric

< section id="tidycdisc-tidying-cdisc-data" class="level2">

TidyCDISC: Tidying CDISC Data

{tidyCDISC} is an open-source R package developed by Biogen that provides a set of functions for tidying and manipulating CDISC (Clinical Data Interchange Standards Consortium) datasets. It aims to simplify the process of working with CDISC data by providing an intuitive interface for data transformation tasks and ensuring consistency with the principles of tidy data.

< section id="features-of-tidycdisc" class="level3">

Features of TidyCDISC

  1. {tidyCDISC} adheres to the principles of tidy data, ensuring that CDISC datasets are structured in a consistent and easy-to-work-with format.
  2. The package supports various CDISC datasets, including SDTM (Study Data Tabulation Model), ADaM (Analysis Data Model), and SEND (Standard for Exchange of Nonclinical Data).
  3. It provides a set of functions for common data transformation tasks, such as converting between CDISC datasets, handling missing values, and applying CDISC-specific terminology.
  4. The package includes functions for validating CDISC datasets and performing consistency checks across related datasets.
  5. {tidyCDISC} is designed to work seamlessly with other pharmaverse packages.
< section id="example-1" class="level3">

Example

Here’s a demo version of {tidyCDISC} you can try.

In the documentation, you can find more examples of applications and how to use {tidyCDISC}.

More on tidyCDISC

< section id="honorable-mention" class="level2">

Honorable Mention

< section id="xportr-creating-cdisc-compliant-xpt-files-in-r" class="level3">

{xportr}: Creating CDISC-Compliant XPT Files in R

Finally, let’s look at {xportr}, an open-source R package developed by GSK, Atorus, and Appsilon that simplifies the process of creating CDISC-compliant XPT files directly from R. It serves as a valuable tool for clinical programmers working with ADaM or SDTM datasets.

This package ensures compatibility with regulatory submission requirements, providing a seamless bridge between R and traditional SAS-based workflows.

{xportr} is designed to handle the intricacies of the XPORT format, making it easier to share data across different platforms and with regulatory authorities. This capability is crucial for teams working in environments where both R and SAS are used, facilitating smooth and compliant data exchanges.

Features of {xportr}

In summary, {xportr} is a valuable tool for clinical programmers working with CDISC data, as it helps ensure regulatory compliance, data quality, and workflow efficiency when creating XPT files for clinical trials and submissions.

More on xportr

< section id="conclusion" class="level2">

Conclusion

The pharmaverse offers a rich ecosystem of tools designed to streamline clinical research workflows, ensuring high-quality data management and reporting. By leveraging packages like {rtables}, {admiral}, {teal}, {riskmetric}, {tidyCDISC}, and {xportr}, pharmaceutical companies can enhance their data analysis capabilities, ensure regulatory compliance, and drive innovation in clinical research. Remember to give the packages that you use and value a star on GitHub.

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