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Hands On Clinical Reporting Using R

Jana Stoilova, Vincent Shen, Tatiana Alonso Amor, Leena Khatri, Joel Laxamana, Adrian Chan, and Stefan Pascal Thoma

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.

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What's inside

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.
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Examines the data flow in clinical trials from CRF and non-CRF to SDTM, ADaM and final reports using R packages
Taught by instructors who are researchers and experts in clinical trial data reporting
Provides clear examples to illustrate key clinical data reporting concepts
Hands-on labs give learners practical experience in clinical data reporting using R
Course materials are well-organized and easy to follow

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Reviews summary

Practical clinical reporting with r

According to learners, "Hands On Clinical Reporting Using R" is an excellent and comprehensive introduction for clinical professionals seeking to leverage open-source R packages. Students laud its focus on the clinical data flow from raw data through to regulatory submissions, describing it as highly practical and industry-relevant. The course's strength lies in its effective use of the Pharmaverse ecosystem (e.g., admiral, NEST, teal) for generating SDTM, ADaM, and TLGs. While generally perceived as a strong foundational course, some reviewers noted that the hands-on activities and advanced modules, particularly around complex coding scenarios, could benefit from more extensive depth or detailed examples, suggesting it's an ideal starting point rather than an exhaustive deep dive.
The course is well-structured and explains complex concepts clearly.
"The modules are well-structured, and the content is highly relevant to industry standards."
"The course structure is logical, building from fundamentals to more advanced topics."
"The explanations of SDTM and ADaM were clear."
Covers the entire clinical data pipeline from raw data to final outputs.
"I found this course highly informative for understanding SDTM, ADaM, and the general clinical reporting pipeline in R."
"Covers all the essential models (SDTM, ADaM) and introduces powerful open-source packages."
"It really helped me understand the clinical data flow from raw to submission-ready outputs."
Strong emphasis on modern open-source R tools and Pharmaverse packages.
"The modules on SDTM and ADaM transformations with Pharmaverse packages were incredibly practical and well-explained."
"The focus on Pharmaverse packages like admiral and NEST is spot on. It really helped me understand the clinical data flow..."
"As a biostatistician, this course is exactly what I needed to update my skills with R and open-source tools."
Course content is highly practical and aligned with industry needs.
"This course was absolutely excellent! ... incredibly practical and well-explained. I especially appreciated the real-world examples and how it ties into regulatory submissions."
"Fantastic course for anyone looking to bridge the gap between theoretical knowledge and practical application in clinical R."
"This course is a game-changer for clinical programmers. It demystifies the entire clinical data flow and shows how R and open-source tools can be leveraged effectively."
A strong starting point but requires further self-study for true expertise.
"It's a great starting point, but not a standalone solution for immediate expert application."
"While it gives a good overview, one would need significant additional study to become proficient."
"Useful for getting familiar with the landscape, though."
Some modules could benefit from more in-depth coding and case studies.
"I wished for slightly more depth in the advanced ADaM section and perhaps more diverse examples for interactive displays."
"It felt more like an introduction than a 'hands-on' deep dive. While it gives a good overview, one would need significant additional study."
"My only critique would be that some sections, especially on TLGs, could benefit from more detailed coding examples or troubleshooting tips."
"I signed up hoping for a deeper dive into actual coding and problem-solving, but it felt more like an advanced conceptual overview."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Hands On Clinical Reporting Using R with these activities:
Review basic R programming concepts
Reviewing basic R programming concepts will strengthen your foundation and make it easier to follow the course material.
Browse courses on R Programming
Show steps
  • Review online tutorials or documentation on R programming basics.
  • Practice writing simple R scripts to perform basic data manipulation and visualization tasks.
Compile a glossary of clinical reporting terms
Compiling a glossary of clinical reporting terms will help you familiarize yourself with the key terminology used in the field and improve your understanding of the course material.
Show steps
  • Identify and collect relevant clinical reporting terms from the course materials and other sources.
  • Define each term clearly and concisely.
  • Organize the terms into a glossary for easy reference.
Participate in online discussion forums
Participating in online discussion forums allows you to engage with peers, ask questions, and share knowledge, which can enhance your understanding of the course material.
Show steps
  • Join online discussion forums related to the course topics.
  • Post questions, respond to others' posts, and engage in discussions.
  • Use the forums to seek clarification, share resources, and learn from others' perspectives.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice data cleaning and manipulation exercises
Practice data cleaning and manipulation exercises to solidifies the understanding of SDTM and ADaM structures.
Browse courses on Data Cleaning
Show steps
  • Identify and correct common data errors, such as missing values, outliers, and inconsistencies.
  • Transform data into different formats, such as from wide to long or vice versa.
  • Merge and join datasets from different sources.
Follow tutorials on statistical analysis methods
Following tutorials on statistical analysis methods will help you apply statistical concepts to real-world data and improve your R programming skills.
Browse courses on Statistical Analysis
Show steps
  • Find tutorials that cover topics relevant to the course, such as descriptive statistics, hypothesis testing, and regression analysis.
  • Follow the tutorials step-by-step, working through the examples.
  • Apply the techniques learned in the tutorials to your own data analysis projects.
Attend workshops on clinical data analysis and reporting
Attending workshops on clinical data analysis and reporting will provide you with the opportunity to learn from experts, ask questions, and network with other professionals in the field.
Show steps
  • Identify and register for workshops that cover topics relevant to the course.
  • Attend the workshops and actively participate in the discussions.
  • Network with other attendees and speakers to learn about different perspectives and resources.
Create a clinical reporting dashboard
Creating a clinical reporting dashboard will provide hands-on experience in using R to generate interactive visualizations and reports for clinical data.
Show steps
  • Design the dashboard, including the layout, widgets, and visualizations.
  • Develop the dashboard using R packages such as shiny, plotly, and ggplot2.
  • Test and refine the dashboard to ensure it meets the requirements.
Participate in online data analysis or clinical reporting competitions
Participating in online data analysis or clinical reporting competitions will challenge you to apply your skills and knowledge in a competitive environment, which can motivate you to learn and improve.
Browse courses on Data Analysis
Show steps
  • Identify and register for competitions that are relevant to the course.
  • Work on the competition tasks, either individually or as part of a team.
  • Submit your solutions and receive feedback on your performance.

Career center

Learners who complete Hands On Clinical Reporting Using R will develop knowledge and skills that may be useful to these careers:

Reading list

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Course image
Effort
5 weeks of study, 3-6 hours/week
Level
Intermediate
Via
Coursera
Institution
Genentech
Instructors
Jana Stoilova
Vincent Shen
Tatiana Alonso Amor
Leena Khatri
Joel Laxamana
Adrian Chan
Stefan Pascal Thoma
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Examines the data flow in clinical trials from CRF and non-CRF to SDTM, ADaM and final reports using R packages
Taught by instructors who are researchers and experts in clinical trial data reporting
Provides clear examples to illustrate key clinical data reporting concepts
Hands-on labs give learners practical experience in clinical data reporting using R
Course materials are well-organized and easy to follow
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