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.

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.

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.

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. As you may be going through this training with a hands-on approach, when working in R, please first follow the installation instructions here to ensure you are using the same R version and R packages needed for both the Training and the Quiz at the end. You may do steps 1-6 now : https://hua.dididi.sbs/learn/hands-on-clinical-reporting-using-r/supplement/enxGp/adae-quiz-resources (copy and paste if you need to), then proceed with the training. Once you get to the quiz, then you may start from step 7 on.

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.

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.

In this final module we will briefly review the course and suggest next steps in your learning journey.