Overview

The aim of this course is to introduce learners to open-source R packages that can be used to perform clinical data reporting tasks. The main emphasis of the course will be the clinical data flow from raw data (both CRF and non-CRF) to SDTM to ADaM to final outputs. While several open-source tools to complete these tasks will be introduced, the objective of this course is not to become an expert in any of these tools but rather to introduce participants to the broader concepts behind these tasks. That way the tools simply serve as an example of how the underlying concepts could be put into action in code.

Syllabus

Hands On Clinical Reporting Using R

In this module, we will introduce this course and provide a brief outline of what you will be learning. We will provide context on clinical reporting in R and the motivation for the recent shift in industry trends for the support of open-source tools. We will describe the challenges in current statistical programming practices and the benefits of applying open-source tools, as well as provide additional resources to learn more.

Fundamentals

In this module, we will cover several important topics related to Phase 3 clinical trials and clinical data. We will start with a brief introduction to Phase 3 trials and discuss the type of data that is collected during these trials. Following that, we will provide an overview of two data models that are commonly used to handle clinical trial data, namely SDTM and ADaM. Next, we will delve into the process of preparing a data submission package for health authorities, with a specific focus on the Food and Drug Administration (FDA) in the United States. We will explore the requirements and guidelines for submitting clinical trial data to the FDA. Lastly, we will wrap up this module by summarizing our understanding of the clinical data flow, highlighting the key points we have covered throughout the course.

SDTM : Study Data Tabulation Model

In this module, we will provide an introduction to Study Data Tabulation Model (or SDTM) by giving context and highlighting the importance of such data models on clinical trials. We will explore different SDTM data mappings for CRF and non-CRF data. Finally, we will provide an outlook on the programming of SDTMs on R.

ADaM Transformations (Introductory)

In this module, we explore what are analysis data model (ADaM) datasets, the 3 structures of ADaM, and how to create ADaM in R using Pharmaverse packages.

ADaM Transformations (Advanced)

In this module, we explore ADaM and R using Pharmaverse packages, one step further. We will focus on the ADaM Occurence Data Structure known as OCCDS using the example Analysis Dataset Adverse Events (ADAE). We'll go over what an OCCDS is, Adverse Events, and how to create ADAE using {admiral} and other R Pharmaverse packages.

Static TLGs (NEST)

In this module, we introduce the concepts of generating outputs used for regulatory purposes, and the NEST packages in particular. We show how you can use NEST effectively to create and customize your tables, listings, and graphs (TLGs) during clinical reporting and introduce the TLG-Catalog to aid output generation using our packages. We will explain the benefits of open-source and the industry collaboration efforts on clinical reporting.

Interactive Data Displays

In this module we will discuss benefits of of using interactive data displays for clinical reporting. We will introduce the teal family of R packages and become familiar with the key features this framework offers. Finally, we will learn how to develop a production level interactive application using teal modules for data review, safety and efficacy analyses.