Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
3 credits
15.0 h
Q2
Teacher(s)
Legrand Catherine;
Language
English
Prerequisites
The content of the course LSTAT2220 Survival Data Analysis is a prerequisite for this course. The student should be familiar with the basis of analysis of survival data, including the definition, estimation and interpretation of
the survival function and of the (cumulative) hasard function, and of the most commonly used regression models (parametric proportional hasards models, semi-parametric Cox model, Accelerated Failure Time model, ') for independent
survival data
the survival function and of the (cumulative) hasard function, and of the most commonly used regression models (parametric proportional hasards models, semi-parametric Cox model, Accelerated Failure Time model, ') for independent
survival data
Main themes
Classical survival analyses techniques assume that (1) the observations are independent, (2) if followed long-enough all observations will eventually experience the event of interest, and (3) only one event is of particular
interest and no other event may prevent this event to occur. In this course, we will investigate other models which are applicable for correlated observations
(frailty models), models which allow to consider the case when a proportion of the population will never experience the event of interest (cure models), and models to be applied in the case of competing risks (competing risks models) or
of several events of interest (multi-state models)..
interest and no other event may prevent this event to occur. In this course, we will investigate other models which are applicable for correlated observations
(frailty models), models which allow to consider the case when a proportion of the population will never experience the event of interest (cure models), and models to be applied in the case of competing risks (competing risks models) or
of several events of interest (multi-state models)..
Aims
At the end of this learning unit, the student is able to : | |
| 1 |
The objectives of the course are to provide each year a comprehensive exposition of one or more specific topic(s) of special interest in the field of biostatistics. |
The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Content
The following models will be studied : frailty models, mixture cure models and promotion time models, competing risks models, competing risks models. Main estimation techniques (parametric and/or semi-parametric models) will be presented, as well as their implementation in standard statistical software (when available). Cases of applications of these models will be studied and
interpretation of these models will be discussed.
interpretation of these models will be discussed.
Bibliography
Articles mis à disposition via moodle.
Teaching materials
- matériel sur moodle
Faculty or entity
LSBA
