School of Scientific Computing & Informatics · Health & lab informatics
Clinical Data Science: CDISC, R & Real-World Data
Clinical trials and real-world studies follow strict data standards before any result reaches a regulator. This course teaches those standards and how to produce analysis datasets and outputs with the open-source R ecosystem now used across the industry.
Course overview
Clinical Data Science covers the life of trial data: collection, CDISC SDTM tabulation, ADaM analysis datasets, and the tables, listings and figures (TLFs) of a clinical study report, produced with R packages from the pharmaverse.
You also analyse time-to-event outcomes, explore real-world data from registries and records, and learn the documentation, validation and traceability expected by sponsors and regulators.
Learning outcomes
By the end of the course you will be able to:
- Explain the clinical data lifecycle and CDISC standards
- Build SDTM domains and ADaM datasets from raw data
- Produce tables, listings and figures in R
- Analyse survival outcomes with Kaplan–Meier and Cox models
- Document and validate outputs for traceability
Skills you will practise
Eligibility and prerequisites
Who can apply
- Students and graduates in any science, engineering, pharmacy, medicine, agriculture, geography or computing programme (B.Sc., B.Tech., B.E., BCA, M.Sc., M.Tech., MCA, PhD)
- Researchers, faculty and working professionals in research, healthcare, biotech, pharma or IT who want to apply computing to science
- Final-year students may apply before their results are declared
Prerequisites
- R for Bioinformatics & Biostatistics or equivalent
- Basic understanding of clinical trials
Best suited to
- Life-science, pharmacy and statistics graduates aiming at clinical data roles
- Clinical research professionals moving from SAS to R
- Biostatisticians and programmers in CROs
Module-wise syllabus
6 modules. Each pairs instructor-led sessions with a hands-on lab on public data.
Topics
- Trial phases and protocols
- Case report forms and EDC
- CDISC overview
Topics
- Domains and variables
- Controlled terminology
- Mapping raw data
Topics
- ADSL and BDS structures
- Derivations and traceability
- admiral in R
Topics
- Demographic and safety tables
- Listings
- Figures and formatting
Topics
- Kaplan–Meier and Cox models
- Real-world data sources
- Bias in observational data
Topics
- Datasets
- Outputs
- Validation
Practical sessions and projects
Capstone project
A traceable mini study report
Take a synthetic trial from raw data to SDTM, ADaM and validated tables, listings and figures.
Assessed on the correctness of the analysis, the quality of the code and the reasoning in the write-up. A pass is required for the certificate.
- Clinical data lifecycle
Map a protocol’s endpoints to the data that must be collected.
- SDTM
Build demographics, adverse events and laboratory SDTM domains from raw data.
- ADaM
Derive ADSL and a laboratory analysis dataset with admiral.
- Tables, listings and figures
Produce a demographic table, an adverse-event summary and a figure for a study report.
- Efficacy and real-world data
Analyse a time-to-event endpoint and compare it with a real-world cohort.
- Project: a mini study report
Produce traceable SDTM, ADaM and TLF outputs for a synthetic study with a validation log.
Software, tools and datasets
Software and tools
All software is free and open source or free for academic use. Recommended computer: 8 GB RAM, 30 GB free disk, Linux, macOS or Windows with WSL2.
Datasets
- CDISC pilot study data: SDTM and ADaM practicals.PHUSE / CDISC public pilot (synthetic)
Core reading
- CDISC SDTM and ADaM Implementation Guides (cdisc.org).
- Bland JM, Altman DG (1998). Survival probabilities (the Kaplan–Meier method). BMJ 317:1572.
Duration, format and fees
- Next cohort
- Dates are sent with your admission offer
- Timetable
- Three evening or weekend sessions a week (IST), plus a lab clinic; intensive and weekend-only schedules on request
- Payment
- Secure online payment link with your admission offer (UPI, cards, net banking). Instalments on request for fees above ₹15,000.
- Refunds
- See the refund and cancellation policy.
Instructor
The instructor for each cohort is named in your admission offer and on this page when the cohort is announced. Vedicgen instructors are selected for hands-on research or industry experience in the subject they teach, and every course is reviewed by a second subject expert.
Questions about who teaches a cohort? [email protected]
Certificate
Learners who complete the course and pass its capstone receive a Vedicgen certificate of completion for Clinical Data Science: CDISC, R & Real-World Data, issued by Vivofold, stating the guided hours and skills assessed. Each certificate has a unique ID and QR code that anyone can check.
Apply for Clinical Data Science: CDISC, R & Real-World Data
Apply
Send the form below. It takes about five minutes.
Review
An adviser confirms your fit and format within two working days.
Offer
You receive dates, timetable and a secure payment link.
Onboard
Set-up guide, pre-reading and joining links.
Frequently asked questions
In the live cohort, modules are taught in instructor-led sessions with a weekly lab clinic where you work on real public data with help at hand; every session is recorded. The self-paced format uses the same lessons, labs and datasets with mentor review of your capstone. Both formats end with an assessed project reviewed for the code and the science.
A laptop with at least 8 GB of RAM and about 30 GB of free disk space, running Linux, macOS, or Windows with WSL2. Installation instructions are sent before the first session, and lab clinics help with any set-up problems.
Yes. Learners who complete the course and pass its assessment receive a Vedicgen certificate of completion, issued by Vivofold. Every certificate carries a unique ID and a QR code that anyone, including an employer or admissions office, can check on the certificate verification page.
Apply with the form on this page. Live and in-person cohorts run through the year; the dates, timetable and joining details of the next cohort are sent with your admission offer and listed on this page once announced. Self-paced enrolment opens as soon as your application is accepted.
Yes. No course runs longer than four weeks, and the length is customisable: choose the standard schedule, an intensive one- to two-week schedule, weekends only, or a custom timetable. Pick your preference on the application form; colleges and teams can book their own dates.
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