2026
Survival Analysis
Name: Survival Analysis
Code: MAT13610M
6 ECTS
Duration: 15 weeks/156 hours
Scientific Area:
Mathematics
Teaching languages: Portuguese
Languages of tutoring support: Portuguese
Sustainable Development Goals
Learning Goals
Goals:
It is intended to provide students with fundamental concepts of survival analysis, allowing them to correctly apply and interpret parametric, nonparametric and semi-parametric models for survival data from several areas.
Skills:
To know how to use nonparametric methods correctly, not only in analyzing a single sample of survival data, but in comparing two or more groups.
To know how to implement, analyze and interpret a survival model.
To know how to identify and to apply the appropriate methods and models in each situation.
To know how to use the right statistical software correctly and to interpret the outputs in a critical way.
It is intended to provide students with fundamental concepts of survival analysis, allowing them to correctly apply and interpret parametric, nonparametric and semi-parametric models for survival data from several areas.
Skills:
To know how to use nonparametric methods correctly, not only in analyzing a single sample of survival data, but in comparing two or more groups.
To know how to implement, analyze and interpret a survival model.
To know how to identify and to apply the appropriate methods and models in each situation.
To know how to use the right statistical software correctly and to interpret the outputs in a critical way.
Contents
Module 1: Introduction to Survival Analysis
Basics concepts of survival analysis (survival, risk and cumulative risk functions, censured and truncated data).
Nonparametric Estimation (Kaplan-Meier and Nelson Aalen estimators, Confidence Intervals)
Survival curve comparison tests.
Cox proportional hazards model.
Parametric regression models.
Module 2: Advanced Survival Analysis
Extensions of the Cox Model (stratified and time-dependent covariates).
Aalen Additive Model.
Frailty models.
Multiple risk models.
Competitive risk models.
Basics concepts of survival analysis (survival, risk and cumulative risk functions, censured and truncated data).
Nonparametric Estimation (Kaplan-Meier and Nelson Aalen estimators, Confidence Intervals)
Survival curve comparison tests.
Cox proportional hazards model.
Parametric regression models.
Module 2: Advanced Survival Analysis
Extensions of the Cox Model (stratified and time-dependent covariates).
Aalen Additive Model.
Frailty models.
Multiple risk models.
Competitive risk models.
Teaching Methods
Theoretical-practical lessons lectured with a blackboard and with e-learning tools. Introduction to theoretical concepts appealing to examples in different areas (health, economy, management=. Emphasis in the resolution of real problems. To emphasize in modeling, critical analysis and interpretation of data on basis of software outputs.
Assessment
Continuous assessment with the completion of 2 individual or group assignments, with a weight of 50% each.
The final assessment regime consists of a written exam in the regular period and a written exam in the appeal period, with a weight of 75%, and 2 individual or group works, with a weight of 12.5% each.
The student is Approved in the case of obtaining a classification equal to or greater than 10 values.
The final assessment regime consists of a written exam in the regular period and a written exam in the appeal period, with a weight of 75%, and 2 individual or group works, with a weight of 12.5% each.
The student is Approved in the case of obtaining a classification equal to or greater than 10 values.
Teaching Staff (2025/2026 )
- Paulo de Jesus Infante dos Santos [responsible]
- Paulo de Jesus Infante dos Santos [responsible]
