New
SM
Clara Fargo
EBMT Clinical Research Quality Assurance Coordinator
I have finalised the demo for the ICH-GCP E6 R3 refresher course. Overall, I liked the content and the interface. I also want to thank Whitehall Train...
R Programming for Clinical Trials Course & Certification
Intermediate
Watch Preview
4.7
★★★★★
97% Recommended
3 hours
193 active users
3 points
Watch Preview
Your Study Basket
R Programming for Clinical Trials
| Total | |
🎁 FREE Bonus Included
eBookWorth £29— Yours FREE when you purchase today.
VAT is levied as per your country’s applicable rules and rates at checkout
Save more with a monthly or annual plan that includes this course
Secure Payment Service
Rest assured we use secure processes to protect your payment.
Secured by:
About
R Programming for Clinical Trials training provides a practical introduction to using R for clinical research and statistical programming. It helps learners understand how R can be applied throughout the clinical trial data analysis lifecycle, from importing and cleaning clinical datasets to performing statistical analyses and creating presentation-ready outputs.
This R Programming for Clinical Trials Course & Certification provides essential knowledge of R programming fundamentals, data manipulation, clinical data structures, descriptive statistics, statistical testing, data visualization, reporting, and reproducible analysis practices. Learners gain practical exposure to using R for clinical trial data preparation, analysis, and reporting while developing an understanding of programming standards and good practices relevant to clinical research. Upon successful completion, learners receive a certification demonstrating their understanding of R programming concepts and their application in clinical trial analysis.
Who Should Enrol?
- Clinical Data Managers and Clinical Data Specialists
- Clinical SAS and R Programmers
- Biostatisticians and Statistical Programmers
- Clinical Research Associates and Clinical Research Professionals
- Data Analysts and Healthcare Data Scientists
- Pharmacovigilance and Drug Safety Professionals
- Biopharmaceutical and Clinical Research Teams
- Anyone involved in clinical trial data analysis, reporting, or statistical programming
What you will learn
Understand the fundamentals of R programming and how R is used in clinical research, including data structures, functions, packages, data import, and basic programming concepts.
Learn how to manage, clean, transform, and validate clinical trial datasets using R, including data manipulation, missing data handling, descriptive statistics, and data quality checks.
Develop practical knowledge of statistical analysis and visualization in R for clinical trials, including summary statistics, hypothesis testing, graphical analysis, and commonly used clinical research methods.
Gain an understanding of clinical trial reporting using R, including reproducible analysis, tables and figures, programming documentation, analysis workflows, and best practices for generating reliable clinical study outputs.
Course Syllabus
- Installing R (CRAN) then RStudio (Posit)
- The RStudio panes and running code
- Projects and the working directory
- Assignment, operators and calling functions
- Packages, CRAN and install.packages vs library
- Reading console messages and getting help
- Hands-on - read ORION-3 DM with read.csv, head/str/summary
- Vectors and the atomic types
- Coercion hierarchy and type checks
- Factors for treatment arms and age groups
- Indexing: [ ], [[ ]] and $
- Lists and data.frames
- Missing values: NA, is.na and na.rm
- Hands-on - build, subset and clean a DM data.frame
- The base pipe |> and reading a pipeline
- filter, select, arrange, mutate, rename
- group_by + summarise, count and distinct
- if_else and case_when derivations
- Joins: left_join, inner_join, anti_join
- SAS equivalents (WHERE, MERGE, PROC MEANS/FREQ)
- Hands-on - derive AGEGR1, count AEs, join demographics
- Tidy data; long vs wide layouts
- pivot_longer and pivot_wider
- separate and unite columns
- The grammar of graphics: data, aes, geoms
- Scales, facets and themes
- Kaplan-Meier plots with ggsurvfit / survminer
- Hands-on - mean change-from-baseline figure by arm
- Reading .xpt and .sas7bdat with haven
- Variable labels, attributes and as_factor
- SDTM domains (DM, AE, LB) as tibbles
- ADaM: ADSL and BDS structures
- Traceability and the pharmaverse
- Intro to {admiral} for ADaM derivations
- Hands-on - read ORION-3 .xpt and derive ITTFL/AGEGR1
- Tables, Listings and Figures; shells and the SAP
- Summary tables with gt
- Clinical-grade tables with rtables and tern
- Demographics Table 1 with gtsummary
- Big-N, denominators and clinical rounding
- Listings and figures; exporting TLFs
- Hands-on - demographics table, AE listing and figure
- Literate programming: code and prose together
- Anatomy: YAML, chunks, knitr and inline code
- Chunk options and caching
- Parameters (params:) for a report per arm
- Output to HTML, PDF and Word
- Embedding gt/flextable tables and ggplot figures
- Hands-on - knit a parameterised demographics report
- Why reproducibility breaks; sessionInfo()
- renv: init, snapshot, restore, status and the lockfile
- Project libraries and committing the lockfile
- The R Consortium R Submissions Working Group and FDA pilots
- pharmaverse, {admiral} and {pkglite} for submission
- Validation and qualification expectations (feasibility, not mandate)
- Hands-on - renv::init() then renv::snapshot(); inspect the lockfile
- Synthesis and the SAS <-> R comparison recap
- Junior R-programmer competency checklist
- Where to keep practising (Posit Cloud, pharmaverse)
- 📘 Bonus: R Programming for Clinical Trials Training eBook (Free with purchase)
Course Benefits
Free eBook
Get our exclusive eBook with every purchase - a complete companion guide to the course, yours to keep forever
CPD Points
Gain Continuing Professional Development (CPD) Points, accredited by The Faculty of Pharmaceutical Medicine of the Royal College of Physicians of the United Kingdom. These can be used to count towards the distance learning element of any scheme that comes under the umbrella of The Academy of Medical Royal Colleges or any other scheme for which there is mutual recognition.
Certification
Receive a personal certificate to show your subject knowledge on course completion.
Affordable
You get excellent value through our cost-effective prices. We can also offer you group discounts on larger purchases.
Flexibility
The course saves you time through the convenience of online availability. This lets you complete the interactive course at your own comfort.
Keep Up to Date
You will stay up to date with CDISC standards (SDTMIG and ADaMIG), Controlled Terminology and ICH E9 conventions, with the tidyverse and pharmaverse/{admiral} ecosystem, and with the evolving regulatory position on R (R Consortium R Submissions Working Group / FDA pilots), as our training courses are constantly monitored, reviewed and updated.
Learn from Industry Experts
The course content has been developed by experienced clinical R and SAS programmers and biostatisticians to ensure that learners can write, debug and validate real clinical-trial R programs at the bench.
Our Certified Customers
Learner Rating & Reviews
4.7
★★★★★★
Average Rating
536 global ratings
★★★★★
87.0%
★★★★★
5.0%
★★★★★
3.0%
★★★★★
3.0%
★★★★★
2.0%
