Modern Methods in Data Analysis
During this online course, you will learn to use statistical methods to study the association between (multiple) determinants and the occurrence of an outcome event. The course will begin with an introduction to likelihood theory, using simple examples and a minimum of mathematics. You will then move to learning about the most important regression models used in medical research. These include logistic regression, Poisson regression, analysis of `event history´ data, and the Cox proportional hazards regression model. In addition, you will become familiar with model validation and regression diagnostics, as well as with the basic principles of resampling methods and longitudinal data analysis. The course is aimed at professionals who are interested in to learn more about statistics for medical research. However, a medical education is not a requirement to succesfully participate in this course.
Modern Methods in Data Analysis is one of the online medical courses of the MSc Epidemiology Postgraduate Online; the online MSc program in Epidemiology offered by Utrecht University, University Medical Center Utrecht, MSc Epidemiology and Elevate Health.
By the end of the course, you should be able to:
- Explain the principles of the likelihood theory and maximum likelihood methods
- Explain the principles of the following statistical analysis techniques: Logistic regression analysis, Poisson regression analysis, Analysis of event history data, including the Cox proportional hazards regression model
- Explain model validation and regression diagnostics
- Describe the basic principles of longitudinal data analysis
- Apply the above-mentioned techniques using common statistical packages (e.g. SPSS or R)
- Name the situations in which these techniques can be applied and the conditions that should be met to obtain reliable results using these techniques
- Explain and interpret the results obtained with these techniques, and apply these results in practice (e.g. to answer a research question)
- Linear models
- Likelihood and logistic regression
- Poisson models and generalized linear models
- Survival analysis
- Resampling methods
- Longitudinal data analysis
The whole course will take place online, apart from the final exam. The following learning methods will be used:
- Web lectures
- Computer exercises
- Reading assignments
Sunday before start date - introduce yourself
Sunday – complete Learning Unit 1
Sunday – complete Learning Unit 2
Sunday – complete Learning Unit 3
Sunday – complete Learning Unit 4
Sunday – complete Learning Unit 5
Sunday – complete Learning Unit 6
Sunday – complete Learning Unit 7
Sunday – complete Learning Unit 8
Monday – Final Exam
This course includes an exam that primarily consists of essay questions, which is the only part of the course that is not online. The exam will take place on 29 May 2017. The exact time will be announced as soon as possible.
If you are able and willing to take the exam in Utrecht, the Netherlands, we are available to proctor the exam for you without any costs. If you have to take the exam from a different location, you need a proctor. This proctor may ask you to pay for their expenses. Please read more about proctoring on our specific webpage.
The exam is not compulsory. However, if you want to receive the Course Certificate and the credits, it is obligatory to take the exam. You are allowed to redo the exam once. The re-examination date is 10 July 2017.
To enroll in this course, you need:
- A BSc degree
- Access to the program R
- Sufficient understanding of statistics and data analysis. Elevate courses offering this knowledge include Introduction to Statistics and Classical Methods in Data Analysis
- Sufficient proficiency in English reading and writing
This course is offered through the MSc Epidemiology program developed by the UMC Utrecht and Utrecht University. The only part of the course that is not online is the examination. You do therefore need access to an internet connection in order to be able to follow lectures, complete assignments and communicate with fellow participants.