Technologies

Medical and biomedical data analysis in R

Comprehensive training in medical and biomedical data analysis using R. Participants will learn statistical methods used in clinical trials, bioinformatics packages (Bioconductor), survival analysis, genomic data processing, and medical image preprocessing while ensuring regulatory compliance (GCP, HIPAA).

Why choose this training?

The medical and pharmaceutical sector generates vast amounts of data — from clinical trial results, through genomic data, to electronic health records. The ability to analyze this data using R is becoming a critical competency for professionals working at the intersection of technology and health sciences. There is growing demand for analysts who can not only process biomedical data but do so in compliance with rigorous regulatory requirements.

During the training, participants will learn statistical methods used in clinical trials, bioinformatics packages (Bioconductor), survival analysis, and medical image preprocessing — all while ensuring compliance with GCP and HIPAA standards.

After completing the training, participants will be able to: perform statistical analysis of clinical trial data in R, apply Bioconductor packages for genomic data analysis, conduct survival analysis using Kaplan-Meier and Cox models, process and analyze data from Electronic Health Records. These competencies directly translate into higher efficiency in IT project execution.

This training is particularly valuable for: data analysts working in the medical and pharmaceutical sector, biostatisticians and bioinformaticians, clinical researchers and biomedical scientists.

What sets our approach apart?

At EITT, we believe the best learning happens through practice. During 3 days of intensive training, participants work on real-world examples and scenarios, ensuring not only theoretical understanding but above all the ability to apply it in practice.

With over 2,500 trainings in our portfolio and a 4.8/5 rating from participants, EITT is a trusted partner in competency development for organizations of all sizes. Our trainers are practitioners with years of experience who share current knowledge and proven solutions.

Looking for training tailored to your team’s needs? Contact us — we’ll prepare a program customized to your requirements.

Benefits

Perform statistical analysis of clinical trial data in R
Apply Bioconductor packages for genomic data analysis
Conduct survival analysis using Kaplan-Meier and Cox models
Process and analyze data from Electronic Health Records
Perform preprocessing of medical images in DICOM format
Create reproducible analytical reports compliant with regulatory requirements
Apply normalization and quality control methods for biomedical data
Ensure analysis compliance with GCP and HIPAA requirements

Who is this training for?

Data analysts working in the medical and pharmaceutical sector

Biostatisticians and bioinformaticians

Clinical researchers and biomedical scientists

Data specialists in pharmaceutical companies and CROs

R programmers interested in biomedical applications

Research project managers in the healthcare sector

Professionals responsible for medical data regulatory compliance

Prerequisites

Basic knowledge of R (variables, functions, data structures)
Understanding of descriptive and inferential statistics fundamentals
Basic knowledge of biology or medicine (recommended)
Own laptop with R and RStudio installed

Training program

Specifics of medical and biomedical data
Setting up the R environment for biomedical analysis
Overview of key packages: tidyverse, survival, Bioconductor
Medical data standards (HL7, FHIR, CDISC)
GCP and HIPAA regulatory requirements in the context of data analysis

Clinical trial design and randomization in R
Statistical tests for clinical data (t-test, ANOVA, non-parametric tests)
Survival analysis — Kaplan-Meier curves and Cox model
Meta-analysis and systematic reviews in R
Results reporting compliant with CONSORT guidelines

Installing and configuring Bioconductor packages
Gene expression analysis (microarray and RNA-seq)
Normalization and quality control of genomic data
Functional enrichment analysis (GO, KEGG)
Genomic data visualization (heatmaps, volcano plots)

Loading and preprocessing medical images in R
Fundamentals of DICOM and NIfTI image analysis
Feature extraction from medical images
Working with Electronic Health Records (EHR)
Integration of clinical data with imaging data

Statistical model validation in a medical context
Creating reproducible reports with R Markdown
Analysis documentation compliant with audit requirements
Anonymization and pseudonymization of patient data
Best practices for storing and sharing results

Delivery Methods

Online

Convenience of participating from anywhere
Interactive live sessions with trainer
Materials available for 30 days
No travel costs

On-site

Direct contact with trainer and group
Intensive hands-on workshops
Networking with other participants
Full focus on learning

Training Topics

Frequently asked questions

What prerequisites do I need to meet before the training?

Basic knowledge of R (variables, functions, data structures) and statistics fundamentals is required. Basic knowledge of biology or medicine is also recommended but not mandatory — key concepts will be explained during the training.

What format is the training delivered in and how long does it last?

The training lasts 3 days and is available in online (live) and on-site formats. The program includes lectures, hands-on exercises with real medical datasets, and Q&A sessions. Participants receive training materials and a completion certificate.

Who is this training designed for?

The training is designed for data analysts in the medical and pharmaceutical sector, biostatisticians, bioinformaticians, clinical researchers, and data specialists in pharmaceutical companies and CROs. It is also suitable for R programmers looking to develop competencies in biomedical applications.