In this workshop, we have three parts:

• Delivering TLFs in CSR (Yilong Zhang)
  • Session 1 (45 min)
  • Break and/or Exercise (20 min)
  • Session 2 (45 min)
  • Break (10 min)
• Clinical trial project (Nan Xiao)
  • Session 3 (30 min)
  • Break (10 min)
• eCTD submission package (Nan Xiao)
  • Session 4 (40 min)
  • Q&A (10 min)

• All opinions expressed are those of the presenter and not Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA.

• Some slides need to be scrolled down to see the full content.

• Learning how to create tables for clinical study reports.
• Learning how to organize a clinical development project.
• Learning how to prepare eCTD submission package to FDA.

The toolchain, process, and formats may be different in different organizations. We only provide a recommended way to address them.

• Team members from Merck & Co.
  • We are hiring!
  • Contact us if interested.
• Contributors of the training materials.
  • Please consider submitting issues or PR in the repo.
• R Consortium
  • R validation hub
  • Submission working group
  • R Tables for Regulatory Submission (RTRS) working group
  • Please consider to join and contribute to a working group.
• ASA Princeton-Trenton Chapter
  • Please consider to be a member of our chapter.

In this workshop, we assume you have some R programming experience and clinical development knowledge.

• Data manipulation: tidyverse, dplyr, tidyr, ggplot2 etc.
• ADaM data: adsl, adae etc.

install.packages(c(
  "tidyverse", # Data manipulation
  "r2rtf",     # TLF generation
  "pkglite"    # eCTD submission
))

• Training Material: https://r4csr.org/
• Demo project:
  • Analysis project: https://github.com/elong0527/esubdemo
  • eCTD submission package: https://github.com/elong0527/ectddemo
• R Consortium FDA submission pilot:
  • https://github.com/RConsortium/submissions-pilot1
• During the workshop, we will use esubdemo project.
  • esubdemo is shared in Posit Cloud (https://posit.cloud/)
  • Project link will be shared in chat
  • Post questions in group chat.

We share the same philosophy described in Section 1.1 of the R Packages book and quote here.

• “Anything that can be automated, should be automated.”
• “Do as little as possible by hand. Do as much as possible with functions.”
• “The goal is to spend your time thinking about what you want to do rather than thinking about the minutiae of package structure.”

The ICH E3: structure and content of clinical study reports provide guidance to assist sponsors in the development of a CSR.

In a CSR, most of TLFs are located in

• Section 10: Study patients
• Section 11: Efficacy evaluation
• Section 12: Safety evaluation
• Section 14: Tables, Figures and Graphs referred to but not included in the text
• Section 16: Appendices

• Public available CDISC pilot study data located at CDISC GitHub repository.

• The dataset structure follows the CDISC Analysis Data Model (ADaM).

• Source data: https://github.com/elong0527/r4csr/tree/master/data-adam

• tidyverse: a collection of R packages to simplify the workflow to manipulate, visualize and analyze data in R.

• r2rtf: an R package to create production-ready tables and figures in RTF format.

In the pharmaceutical industry, RTF/Microsoft Word play a central role in preparing clinical study reports

• ICH E3 Structure and Content of Clinical Study Reports

Different organizations can have different table standards

• E.g., Table layout, Font size, Border type, Footnote, Data source

• r2rtf is an R package to create production-ready tables and figures in RTF format.

r2rtf is designed to:

• provide simple “verb” functions that correspond to each component of a table, to help you translate a data frame to a table in RTF format;
• enable pipes (%>%);
• only focus on the table format.
  • Data manipulation and analysis should be handled by other R packages. (e.g., tidyverse)

Before creating an RTF table, we need to:

• Figure out table layout.

• Split the layout into small tasks in the form of a computer program.

• Execute the program.

r2rtf is designed to enable pipes (%>%).

head(iris) %>%
  rtf_body() %>%           # Step 1 Add table attributes
  rtf_encode() %>%         # Step 2 Convert attributes to RTF encode
  write_rtf("minimal.rtf") # Step 3 Write to a .rtf file

r2rtf package provides the flexibility to customize table appearance for

• Table component: title, column header, footnote, etc.
• Table cell style: size, border type, color, font size, text color, alignment, etc.
• Flexible control: the specification of the cell style can be row or column vectorized.
• Complicated format: pagination, section grouping, multiple table concatenations, etc.

r2rtf package also provides the flexibility to convert figures in RTF format.

r2rtf only focus on table format. Data manipulation and analysis should be handled by other R packages. (e.g., tidyverse)

• Let’s refer to examples in the Section 1.4.2

r2rtf provides simple “verb” functions that correspond to each component of a table, to help you translate data frame to tables in RTF file.

https://r4csr.org/tlf-disposition.html

https://r4csr.org/tlf-population.html

https://r4csr.org/tlf-baseline.html

https://r4csr.org/tlf-efficacy-ancova.html

https://r4csr.org/tlf-ae-summary.html

https://r4csr.org/tlf-ae-specific.html

In a late-stage clinical trial, the number of A&R deliverables can easily be in the hundreds.

For an organization, it is also common to have multiple ongoing clinical trials in a clinical program.

In this part, let’s consider how to organize a clinical trial project as an A&R lead.

Our primary focus is creating a standard R package structure to organize the project, with 4 goals in mind:

• Consistency
• Reproducibility
• Automation
• Compliance

https://r4csr.org/project-folder.html

• Setting up for success
  • Teamwork
  • Architecture
  • Boundaries
  • Community
• The SDLC
  • Planning
  • Development
  • Validation
  • Operation

https://r4csr.org/project-management.html

https://r4csr.org/submission-overview.html

We will discuss strategies to prepare proprietary R packages and analysis code into proper formats for submission:

• The whole game
• Practical considerations
• Prepare R packages using pkglite
• Prepare analysis programs
• Update ADRG and ARM

https://r4csr.org/submission-package.html

We will also give recommendations to make the R code running environment reproducible for dry run tests and reviews using cleanslate.

https://r4csr.org/submission-environment.html

• To provide a tool for packing and restoring R packages as plaintext assets that are easy to store, transfer, and review.
• To provide a grammar for specifying the file packing scope that is functional, precise, and extendable.
• To provide a standard for exchanging the packed asset that is unambiguous, human-friendly, and machine-readable.

library("pkglite")

"/path/to/pkg/" %>%
  collate(file_ectd()) %>%
  pack()

pack(
  "/path/to/pkg1/" %>% collate(file_ectd()),
  "/path/to/pkg2/" %>% collate(file_ectd()),
  output = "/path/to/pkglite.txt"
)

"/path/to/pkglite.txt" %>%
  unpack(output = "/path/to/output/")

File specifications offer flexibility and brevity in specifying the files to include.

File collections contain the evaluation results of file specifications for packing.

library("pkglite")

"/path/to/pkg" %>%
  collate(file_root_core(), file_r(), file_auto("inst/"))

pkglite.txt follows the standard Debian Control File (DCF) format used by Debian, R, and RStudio IDE, to be both machine-readable and human-readable.

# Generated by pkglite: do not edit by hand
# Use pkglite::unpack() to restore the packages

Package: pkg1
File: DESCRIPTION
Format: text
Content:
  Package: pkg1
  Type: Package
  Title: Example Package One
  Version: 0.1.0