Biostatistics II [CK030]

Detailed information about this course:

Description

This course is offered in a hybrid setting, which means students can join the live lectures in class and online simultaneously.

This course presents statistical regressions models for the analysis of dichotomous, count, and time-to-event data. In the first part, the course builds upon the introductory presentation of logistic regression from the Biostatistics I course and shows some of its extensions, including the conditional logistic regression model. The course then introduces regression models for the analysis of count data. The last part focuses on the statistical analysis of time-to-event data, starting from simple statistical tests and followed by the presentation of accelerated failure time and Cox proportional hazards models. For each modeling framework, a detailed discussion is given on how to build the model to answer the scientific questions of interest, estimate the model’s parameters, assess its assumptions, and finally, interpret the results of the analysis.

The course will be explanatory rather than mathematically rigorous, emphasizing application such that participants will obtain a clear view of the different modeling approaches and how they should be used in practice. To this end, the course includes several computer sessions, during which participants will learn to work with the R statistical language and implement the methods discussed in the theory sessions.

For students in our master programmes, the core concepts presented in this course will be assessed in the core competences exam that bundles the fall semester courses. This is in addition to the assessment during the course in the form of assignment(s). The core competences exam is only mandatory for students starting their programme in August 2021 or later, while the assignments during the course are mandatory for all participating students.

Objectives

At the end of the course, participants should be able to:

• Appropriately analyze dichotomous, count and time-to-event data
• Assess the assumptions behind the chosen statistical analysis technique
• Correctly interpret the results of the analysis
• Implement all the above in the R statistical software

Participant profile

Clinical researchers, clinical epidemiologists, decision scientists, public health researchers, those in health technology assessment or value-based healthcare.

Assessment

Assignment(s)