Παρουσίαση/Προβολή

(MED1858) -  Ευάγγελος Κριτσωτάκης, Χρήστος Θωμαδάκης

Περιγραφή Μαθήματος

MED1858 Survival Data Analysis

Evangelos Kritsotakis, Christos Thomadakis

Module summary

This module covers essential topics for analysing time-to-event data or survival times. The emphasis is on practical aspects of applied data analysis in real-world datasets, without overlooking the underlying statistical theory. The module begins by illustrating the special features of survival data and introducing the basic mathematical functions central for describing and analysing survival times (the survival, hazard, and cumulative hazard functions), discussing their interrelationships and links to the cumulative distribution and probability density functions. A revision of the basic theory in Maximum Likelihood Estimation and the Delta method for variance estimation takes place early on to ensure the effective application of these methods in the context of survival analysis. We then explain methods for summarising survival data based on non-parametric techniques to estimate survival functions such as the Kaplan-Meier and the Nelson-Aalen estimators and consider the derivations of variances and confidence intervals. We proceed with methods for comparing survival time distributions between two or more groups and discuss the derivation of the log-rank test, its underlying assumptions, and its relation to other tests, such as the Wilcoxon test. The module then turns to regression models for survival outcomes, emphasising the formulation and application of the Cox proportional hazards model (estimation, testing, prediction and interpretation of model coefficients), and ways to assess model validity in practice. Some more general issues in regression modelling are discussed, including model fit, linearity assessment, flexible modelling, and variable selection. We then extend the Cox model to incorporate time-dependent explanatory variables. Alternative proportional-hazards models based on parametric distributions (exponential, Weibull) are also examined. Finally, we introduce methods for summarising times-to-event for different event causes (competing risks) and describe models for cause-specific survival data. Class materials include lecture presentations, readings (journal papers and book chapters), Lab exercises and individual assignments using the R software to implement survival analyses of real-world clinical and epidemiological studies. Stata code is also provided as an optional study.

 

Recommended textbook

• Collett: Modelling Survival Data in Medical Research (4th ed., 2023).

 

Other useful textbooks, available via Heal Link (access via university VPN):

• Kleinbaum (2012, 3rd ed). Survival Analysis: A self-learning text. HeaL-Link. Link 2.  Link 3.
• Klein & Moeschberger (2003, 2nd ed.). Survival Analysis: Techniques for censored and truncated data. Heal-link.
• Liu (2012). Survival analysis: Models and applications. Heal-Link.
• Moore (2016). Applied Survival Analysis Using R. Heal-Link.

 

Timetable 2024

Unit	Lectures (E. Kritsotakis)	R Labs  (C. Thomadakis)
1. Introduction & basic functions in Survival Analysis	02.10.2024, 4-6 pm ONL	-
2. Revision: Maximum Likelihood Estimation & Delta method	03.10.2024, 4-6 pm ONL	-
3. Non-parametric estimation of the survival function	14.10.2024, 4-6 pm	14.10.2024, 6-8 pm
4. Comparing survival functions	16.10.2024, 4-6 pm	16.10.2024, 6-8 pm
5. Introduction to Cox regression	18.10.2024, 4-6 pm	18.10.2024, 6-8 pm
6. More on Cox regression: Testing & predicting	23.10.2024, 4-6 pm ONL	23.10.2024, 6-8 pm ONL
7. Cox regression: Linearity, Model Fit & Model Selection	25.10.2024, 4-6 pm ONL	25.10.2024, 6-8 pm ONL
8. Assessing proportional hazards	29.10.2024, 4-6 pm	29.10.2024, 6-8 pm
9. Time dependent variables	31.10.2024, 4-6 pm	31.10.2024, 6-8 pm
10. Parametric Survival Analysis	01.11.2024, 4-6 pm	01.11.2024, 6-8 pm
11. Competing Risks	07.11.2024, 4-6 pm ONL	07.11.2024, 6-8 pm ONL

 

Assignments (individual submissions)	Release date	Deadline
Take-home assignment 1	23.10.2024	11.11.2024
Take-home assignment 2	18.11.2024	20.12.2024

 

Ημερομηνία δημιουργίας

Πέμπτη 7 Οκτωβρίου 2021