Introduction

Survival analysis has become a cornerstone in data-driven decision-making across medical research, public health, clinical trials, and actuarial sciences. As real-world events often involve multiple causes of failure or event types, understanding competing risks is essential. Survival Analysis with Competing Risks Training Course is designed to empower professionals with the advanced statistical and computational tools necessary to handle time-to-event data with multiple risk factors. By integrating competing risks models, cumulative incidence functions, and cause-specific hazard modeling, learners will gain practical, research-backed techniques to interpret and visualize complex survival data effectively.

With a strong emphasis on data analysis using R and Python, this course covers theoretical foundations and hands-on case studies from oncology, epidemiology, insurance, and engineering. Participants will also explore risk stratification, proportional hazards modeling, and machine learning integration for survival data. Designed for data scientists, epidemiologists, and researchers, this training blends statistical theory with real-world application, enhancing both analytical skills and domain-specific knowledge.

Programme Curriculum

Survival Analysis with Competing Risks Training Course

Introduction

Course Objectives

By the end of this course, participants will be able to:

Define and differentiate standard survival analysis and competing risks models.
Apply Kaplan-Meier and cumulative incidence estimators using R/Python.
Interpret cause-specific hazard and subdistribution hazard models.
Analyze real-world datasets with multiple failure types.
Visualize time-to-event data and cumulative incidence functions.
Fit and validate Fine and Gray models for competing risks.
Evaluate model performance using concordance indices and calibration.
Integrate machine learning for advanced survival prediction.
Develop robust survival models for clinical and public health settings.
Apply survival analysis in actuarial science and reliability engineering.
Handle missing data and censoring in time-to-event datasets.
Build dashboards and reports for survival outcomes communication.
Interpret and present statistical outcomes to non-technical stakeholders.

Target Audience

Epidemiologists and public health analysts
Biostatisticians and clinical researchers
Data scientists in healthcare and pharma
Actuarial analysts and insurance professionals
Biomedical and reliability engineers
Graduate students in biostatistics or data science
Medical research fellows and clinicians
Government and NGO program evaluators

Course Duration: 5 days

Course Modules

Module 1: Foundations of Survival Analysis

Introduction to survival analysis and censoring
Kaplan-Meier estimator basics
Time-to-event data structure and formats
Right censoring vs. left truncation
Real-world medical dataset analysis
Case Study: Cancer patient survival in a clinical trial

Module 2: Understanding Competing Risks

Definition and concepts of competing risks
Cumulative incidence vs. Kaplan-Meier
Identifying cause-specific events
Limitations of standard methods
Coding competing risks in R
Case Study: Kidney transplant failure causes

Module 3: Cause-Specific Hazard Models

Cause-specific vs. subdistribution hazards
Time-dependent covariates inclusion
Assumption checking and validation
Hazard ratio interpretation
Model building in R using survival package
Case Study: Heart disease vs. cancer mortality

Module 4: Subdistribution Hazard Models (Fine & Gray)

Overview of Fine and Gray model
Comparison with cause-specific models
Model fitting using cmprsk and riskRegression
Advanced model diagnostics
Interpretation of covariate effects
Case Study: Competing causes of ICU discharge

Module 5: Visualization and Interpretation

Cumulative incidence function plots
Risk tables and forest plots
Interpretation for clinical decision-making
Graphs using ggsurvplot and ggplot2
Interactive dashboards with Shiny and Plotly
Case Study: Stroke vs. bleeding risk in patients

Module 6: Advanced Topics and Machine Learning

Random survival forests and DeepSurv
Comparison with classical models
Model tuning and evaluation metrics
Use of scikit-survival and lifelines in Python
Integration with real-time clinical decision systems
Case Study: Predicting readmission in COVID-19

Module 7: Real-World Applications

Health economics and policy planning
Actuarial science applications
Engineering and manufacturing reliability
Designing public health interventions
Translating findings to recommendations
Case Study: Insurance claim predictions by event type

Module 8: Ethical and Practical Considerations

Data privacy and informed consent
Responsible reporting of results
Bias and confounding in survival models
Communicating uncertainty
Policy implications and stakeholder engagement
Case Study: Ethics in oncology survival studies

Training Methodology

Instructor-led lectures and live demos
Interactive coding labs in R and Python
Real-world datasets for practice
Peer-to-peer discussion and Q&A forums
Downloadable templates, scripts, and resources
Access to recorded sessions and technical support

Bottom of Form

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

Survival analysis competing risks modeling event history

Programme Fee

Duration: 5 Days

Method: Online or Physical

Online Price: $1,000

Physical Price: $1,500

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 23 Oct 2026

💻 Online / Virtual session

Online $1,000