Introduction

Survival Analysis and Event History Modeling are essential statistical techniques for analyzing time-to-event data across disciplines such as epidemiology, public health, engineering, social sciences, and economics. Survival Analysis and Event History Modeling Training Course is designed to equip researchers, analysts, and professionals with practical skills in modeling event occurrence and timing using real-world datasets. With the growing emphasis on data-driven decision-making and predictive modeling, learning these advanced methods has become vital in today's data science and statistical analytics landscape.

The course covers key methodologies, including Kaplan-Meier estimation, Cox Proportional Hazards models, time-dependent covariates, competing risks, and recurrent events. Through interactive modules, practical case studies, and applied statistical tools such as R and Python, participants will master the techniques to interpret, visualize, and present survival data effectively. This course is optimized for individuals who want to stay ahead in health analytics, financial risk assessment, operational research, and social sciences.

Programme Curriculum

Survival Analysis and Event History Modeling Training Course

Introduction

Course Objectives

Understand the fundamental concepts of survival analysis.
Interpret survival and hazard functions effectively.
Apply Kaplan-Meier estimation for time-to-event data.
Fit and interpret Cox Proportional Hazards Models.
Use time-dependent covariates in model building.
Analyze competing risks and multistate models.
Understand recurrent event models and frailty models.
Handle censored and truncated data.
Visualize survival data using R and Python.
Conduct model diagnostics and assess fit.
Build predictive survival models using machine learning.
Apply models to real-world datasets from healthcare, economics, and social science.
Communicate survival analysis results to both technical and non-technical audiences.

Target Audiences

Biostatisticians and Epidemiologists
Data Scientists and Analysts
Public Health Researchers
Financial Risk Managers
Social Science Researchers
Actuarial Analysts
Clinical Trial Designers
PhD and Postgraduate Students in Quantitative Fields

Course Duration: 5 days

Course Modules

Module 1: Introduction to Survival Analysis

Defining survival and event history data
Types of censoring and truncation
Survival and hazard functions
Overview of use cases in research and industry
Tools: R and Python setup
Case Study: Patient survival times post-treatment

Module 2: Kaplan-Meier Estimation

Constructing Kaplan-Meier curves
Calculating survival probabilities
Log-rank test for group comparison
Stratification techniques
Plotting and interpreting survival curves in R
Case Study: Cancer survival analysis by gender

Module 3: Cox Proportional Hazards Model

Assumptions of the Cox model
Model building and interpretation
Time-varying covariates
Checking proportional hazards assumption
Implementation in R and Python
Case Study: Predicting employee retention time

Module 4: Time-Dependent Covariates

Incorporating time-varying predictors
Advanced survival modeling techniques
Lagged variables and interaction terms
Time-split data methods
Visualizing covariate effects
Case Study: Blood pressure changes over treatment time

Module 5: Competing Risks and Multistate Models

Introduction to competing risks framework
Cumulative incidence functions
Transition intensity in multistate models
Cause-specific vs subdistribution hazards
Applications in clinical trials
Case Study: ICU discharge vs mortality modeling

Module 6: Recurrent Event Models

Event recurrence and frailty modeling
Gap time vs total time models
Counting process notation
Joint frailty models for clustered data
Robust variance estimation
Case Study: Hospital readmissions after surgery

Module 7: Predictive Modeling & Machine Learning

Survival trees and random survival forests
Gradient boosting for censored data
Model evaluation: Brier score, C-index
Cross-validation for survival data
Feature importance analysis
Case Study: Predictive modeling of loan default time

Module 8: Communication, Interpretation, and Reporting

Data visualization and storytelling
Survival dashboards in R Shiny
Presenting findings to stakeholders
Reporting standards and best practices
Ethical considerations in survival modeling
Case Study: Communicating cancer survival trends to policy makers

Training Methodology

Instructor-led interactive sessions
Real-world dataset analysis using R/Python
Group exercises and discussion
Hands-on labs and assignments
Module-based case study presentations
Pre/post-course assessments to measure learning

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 event history statistical modeling

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