Logistic Regression and Survival Analysis

Code:

M6026

Acronym:

M6026

Keywords
Classification	Keyword
OFICIAL	Mathematics

Instance: 2017/2018 - 2S

Active?	Yes
Responsible unit:	Department of Mathematics
Course/CS Responsible:	Doctoral Program in Applied Mathematics

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
PDMATAPL	3	Official Study Plan	1	-	3	21	81

Teaching language

Suitable for English-speaking students

Objectives

At the end of the course students should be able to perform a statistical analysis of binary response data as well as for survival data. Such problems can be found in health sciences as well as in many other areas. Students will learn fundamental concepts of logistic regression and survival analysis as well as the statistical methods necessary to carry out statistical inference. Once acquired these knowledges, students should be able to identify and apply the appropriate methods in a given situation. Real databases and statistical software (R or Rstudio) will be used to illustrate the application of the studied methods.

Learning outcomes and competences

The student at the end of the course should be able to:

- Identify the application context of each of the studied techniques with real data.

- Select the appropriate statistical methods.

- Apply statistical methods to databases in medicine.

- Interpret the results provided by the various methods.

- Develop statistical modeling with censored and truncated data.

- Use statistical software for the analysis of survival data and logistic regression.

Working method

Presencial

Program

Logistic regression:

Definition of a generalized linear model. Definition of Odds Ratio. Simple logistic regression model for binary responses. Simple logistic regression model for qualitative responses with more than two levels. Model for continuous responses. Selection of variables and diagnostic methods. Applications to real data and use of statistical software. Use of Receiver Operating Characteristic (ROC) curves to evaluate the quality of the model.

Survival Analysis:

Basic concepts of survival analysis: Censorship and truncation. Concepts related to the life time: survival function, risk function. Study of some of the most used continuous distributions (exponential, Weibull, etc). The Kaplan-Meier estimator of the survival function; non-parametric tests for comparison of survival curves: the "log-rank" test. Study of the Cox regression model.

Mandatory literature

McCullagh P.; Generalized linear models. ISBN: 0-412-31760-5
Klein John P.; Survival analysis. ISBN: 978-1-4419-2985--3
Hosmer David W.; Applied survival analysis. ISBN: 9780471754992

Teaching methods and learning activities

Teaching methodologies combine theory practice classes with classes in computer lab.

Evaluation Type

Distributed evaluation without final exam

Assessment Components

designation	Weight (%)
Apresentação/discussão de um trabalho científico	30,00
Trabalho escrito	70,00
Total:	100,00

Calculation formula of final grade

Final Classification = 35*T1 + 35*T2 + 30*AO

T1- Written Work 1
T2- Written Work 2
AO- Oral Presentation of the written works