# Fiches de cours

## Biostatistics

#### Lecturer(s) :

Stensrud Mats Julius

English

#### Summary

This course covers statistical methods that are widely used in medicine and biology. A key topic is the analysis of longitudinal data: that is, methods to evaluate exposures, effects and outcomes that are functions of time. While motivated by real-life problems, some of the material will be abstract

#### Content

• Analysis of time-to-events (survival analysis / failure time analysis)
• Censoring
• Likelihood functions for censored data
• Martingales
• Identification of parameters with a clear interpretation
• Non-parametric and semi-parametric estimators
• Discrete vs continuous time
• Longitudinal data analysis
• Parametric regression models
• Semi-parametric models
• Interpretation and evaluation of statistical parameters
• Description, Prediction and Causal inference
• Biases
• Sensitivity analyses
• Research synthesis
• Transportability and generalizability (Meta analysis)
• Multiple testing
• Publication bias

#### Keywords

Biostatistics; statistical inference; survival analysis; longitudinal data; research synthesis

#### Learning Prerequisites

##### Required courses

The students are expected to have taken introductory courses in statistical theory, probability theory and regression modeling.

##### Important concepts to start the course

Likelihood theory, statistical testing. Experience with R is an advantage, but is not required.

#### Learning Outcomes

By the end of the course, the student must be able to:
• Identify statistical methods that are suitable for answering a given scientific problem.
• Justify why a statistical method is applied to given problem.
• Apply methods that have been taught in the course.
• Critique evaluate published studies and methodologies.

#### Transversal skills

• Communicate effectively with professionals from other disciplines.
• Access and evaluate appropriate sources of information.
• Demonstrate the capacity for critical thinking

#### Teaching methods

Classroom lectures, where I will use Beamer slides and the blackboard. Exercises and take-home projects that will require programing in R.

#### Assessment methods

Final written exam and a mini-project.

Dans le cas de l'art. 3 al. 5 du Règlement de section, l'enseignant décide de la forme de l'examen qu'il communique aux étudiants concernés.

#### Supervision

 Office hours No Assistants Yes Forum No

#### Resources

No

##### Bibliography

Teaching resources

• Aalen, O., Borgan, O. and Gjessing, H., 2008. Survival and event history analysis: a process point of view. Springer
• Andersen, P.K., Borgan, O., Gill, R.D. and Keiding, N., 2012. Statistical models based on counting processes. Springer

### Reference week

MoTuWeThFr
8-9
9-10
10-11
11-12
12-13
13-14
14-15
15-16
16-17
17-18
18-19
19-20
20-21
21-22
Under construction

Lecture
Exercise, TP
Project, other

### legend

• Autumn semester
• Winter sessions
• Spring semester
• Summer sessions
• Lecture in French
• Lecture in English
• Lecture in German