Data|Statistics|Research|Consultancy

Survival Analysis

Survival Analysis

The main idea of the course is to develop a critical approach to the analysis of survival data often encountered in health and actuarial sciences research

Course Introduction

Survival Analysis is a branch of statistics focused on analyzing the expected duration of time until an event of interest occurs. This course provides a comprehensive introduction to survival analysis methods commonly used in medical research, clinical trials, and epidemiological studies. Students will learn both theoretical foundations and practical applications using real-world data.

Course Content

Module 1: Introduction to Survival Analysis

  • Basic concepts and terminology
  • Censoring and truncation
  • Survival, hazard, and cumulative hazard functions
  • Applications in medical and health research
  • Types of survival studies

Module 2: Non-parametric Methods

  • Life tables
  • Kaplan-Meier estimator
  • Nelson-Aalen estimator
  • Log-rank test and other comparison tests
  • Graphical methods for survival data

Module 3: Semi-parametric Methods

  • Cox proportional hazards model
  • Model building and variable selection
  • Checking proportional hazards assumption
  • Time-dependent covariates
  • Stratified Cox models

Module 4: Parametric Models

  • Exponential, Weibull, and log-logistic models
  • Accelerated failure time models
  • Model selection and goodness-of-fit
  • Frailty models
  • Competing risks analysis

Module 5: Advanced Topics

  • Recurrent event analysis
  • Joint modeling of longitudinal and survival data
  • Bayesian approaches to survival analysis
  • Machine learning methods for survival prediction
  • Current research trends in survival analysis

Assignments

  • 1
    Analysis of a clinical trial dataset using Kaplan-Meier and log-rank tests
  • 2
    Building and interpreting Cox proportional hazards models
  • 3
    Comparison of parametric and semi-parametric approaches
  • 4
    Survival analysis project using real medical data
  • 5
    Critical review of survival analysis methods in published literature

Case Studies

Case Study 1

Cancer survival analysis with multiple treatment arms

Case Study 2

Time-to-event analysis in cardiovascular studies

Case Study 3

Recurrent hospitalization analysis in chronic disease patients

Case Study 4

Competing risks analysis in transplant studies

Case Study 5

Survival prediction models in critical care medicine

Datasets

Veterans' Administration Lung Cancer Trial

Classic dataset for survival analysis demonstrations

Stanford Heart Transplant Data

Survival times of heart transplant patients

SEER Cancer Statistics

Comprehensive cancer incidence and survival data

Framingham Heart Study

Longitudinal data for cardiovascular event analysis

Recommended Textbooks

Survival Analysis: Techniques for Censored and Truncated Data

by John P. Klein and Melvin L. Moeschberger

View Book Details

Applied Survival Analysis: Regression Modeling of Time-to-Event Data

by David W. Hosmer, Stanley Lemeshow, and Susanne May

View Book Details

Modelling Survival Data in Medical Research

by David Collett

View Book Details

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