Biostatistics II [CK030]
Monday-Friday, the course covers a fulltime workload
Prof. Dimitris Rizopoulos, PhD, Eleni Rosalina Andrinopoulou, PhD, Nicole Erler, PhD, Joost van Rosmalen, PhD
CK020 Biostatistics I
Detailed information about this course:
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.
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
Clinical researchers, clinical epidemiologists, decision scientists, public health researchers, those in health technology assessment or value-based healthcare.
Reduction on fees
PLEASE NOTE THIS DOES NOT APPLY TO THE RESEARCH MASTERS (120 EC points)
No fees are charged for participation of Erasmus MC PhD candidates
Please note that in case of cancellation or no show, the cancellation policy applies based on the full course fee.
50% reduction on our fees for participating institutes of NIHES
This 50% reduction is offered to participants formally appointed at the departments or sections participating in NIHES.
University Medical Center Rotterdam/Erasmus University Rotterdam:
- Department of Epidemiology
- Department of Public Health
- Department of Psychiatry, Section of Medical Psychology and Psychotherapy
- Department of Child and Adolescent Psychiatry/Psychology
- Department of Medical Informatics
- Department of General Practice
- Erasmus School of Health Policy & Management
- Generation R
50% reduction on our fees for NIHES affiliated partners
This 50% reduction is offered to participants formally appointed at the following NIHES affiliated partners:
University Medical Center Rotterdam/Erasmus University Rotterdam, departments of:
- General Paediatrics
- Plastic and Reconstructive Surgery
- Rehabilitation Medicine
UMIT, Department of Public Health, Health Services Research and Health Technology Assessment.
25% reduction on our fees for Erasmus MC employees
For participants formally appointed at all departments of Erasmus MC (except the departments mentioned above and Erasmus MC PhD candidates).
25% reduction for all (international) PhD students without formal appointment at Erasmus MC
Upon receipt of your application you will receive a request to upload proof of enrollment as a PhD student.