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Certificate Program

R Programming for Clinical Research

Program Objective

This certificate course is designed to equip learners with a practical understanding of R programming within the context of Clinical Data Sciences. Participants will develop proficiency in using R and RStudio to import, clean, analyze, and report clinical trial data. The course emphasizes hands-on learning through real-world clinical datasets and case studies aligned with industry standards like CDISC (SDTM/ADaM). By the end of the course, learners will be able to confidently use R for performing statistical analyses, generating tables and visualizations, and preparing regulatory-compliant reports used in clinical research, data management, and biostatistics.

Mode: Online (Instructor-led)
Duration: 36 Hours (6 Weeks, 6 hours per week)
Certificate: Issued by IDDCR Global Institute

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Designed For

  • Life Sciences, Pharmacy, Biotech, and Medical graduates

  • Clinical Research and Clinical Data Management professionals

  • Biostatisticians and Statistical Programmers (beginner to intermediate level)

  • Research Scholars or students aspiring to work in data-driven roles within healthcare or life sciences industries

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Pre-requisite

  • Basic understanding of clinical trials and clinical research terminology

  • Familiarity with clinical data formats (helpful but not mandatory)

  • Fundamental knowledge of statistics is recommended

  • No prior programming experience required, but basic computer skills are essential

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Learning Delivery

  • Live virtual classes OR self-paced pre-recorded modules

  • Weekly assignments and code challenges

  • Downloadable datasets, scripts, and lecture slides

  • Access to discussion forums and doubt-clearing sessions

  • Final project mentorship and feedback

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Tools & Technologies Used

  • R and RStudio (Open-source tools)

  • R Packages: tidyverse, ggplot2, dplyr, tidyr, lubridate, readxl, haven, knitr, rmarkdown

  • Sample CDISC-compliant clinical datasets (SDTM/ADaM)

  • LMS platform for hosting content and assignments

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Assessment & Certification

  • Module-wise quizzes and hands-on exercises

  • Capstone project evaluated by the course mentor

  • Minimum 70% required to pass

  • Certificate of Completion issued by IDDCR Global Institute

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Learning Outcome

  • Gain hands-on skills in using R and RStudio for clinical data analysis.

  • Learn to import, clean, and manage clinical trial data from various sources.

  • Perform basic statistical analysis and create visual reports using R.

  • Understand CDISC standards (SDTM/ADaM) and apply them to clinical datasets.

  • Create regulatory-ready reports (TLFs) and complete a real-world project.

Why Learn R for Clinical Trials?

Widely Used in Clinical Research & Biostatistics
R is a powerful, open-source language used globally by biostatisticians, data scientists, and clinical researchers to analyze and visualize clinical trial data.

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In-Demand Skill for Career Growth
Proficiency in R adds a competitive edge for roles in Clinical Data Science, Biostatistics, Clinical Programming, and Pharmaceutical Analytics, with rising demand in global CROs and healthcare analytics companies.

Course Contents

Module 1: Introduction to R Programming

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  • Basics of R and RStudio

  • Data types, vectors, lists, matrices, data frames

  • Importing/exporting data (.csv, Excel, SAS, SPSS, etc.)

  • Basic functions and loops

 

​Module 2 :  Data Manipulation with Clinical Trial Data

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  • Tidyverse: dplyr, tidyr

  • Filtering, selecting, grouping and summarizing

  • Handling missing data and outliers

  • Real-world datasets from CDM/EDC/clinical trials

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Module 3: Data Visualization and Reporting

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  • ggplot2: Bar charts, Histograms, Boxplots

  • Clinical visuals: AE summaries, subject disposition, etc.

  • Customizing themes and exporting graphs

  • Reporting using RMarkdown


Module 4: Clinical Trial Dataset Structures

 

  • CDISC overview: SDTM and ADaM standards

  • Working with Clinical Domain Datasets

  • Transforming data to SDTM/ADaM-like structures

  • Metadata and Define.XML prep basics.


Module 5: Statistical Analysis for Clinical Trials

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  • Descriptive statistics

  • T-tests, ANOVA, Chi-square

  • Survival analysis (Kaplan Meier, log-rank test)

  • Safety and efficacy endpoint analysis


Module 6: Projects & Automation

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  • Automating data cleaning and summaries

  • Real-world mini project (case study from clinical trial data)

  • Final project presentation

  • Resume review + mock technical interview (optional)

Fact

Open-Source and Industry-Standard

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R is free and open-source, yet used by leading pharmaceutical companies and CROs like Novartis, Roche, and Pfizer in their clinical pipelines.

Join Our R Programming Course

Learn how to code, analyze, and visualize clinical trial data

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