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At the end of this course, the student:
- will have developed advanced and computationally efficient R programming skills;
- is able to conduct and report on simulation studies, comparing the performance of statistical methods in specific settings;
- is able to implement and use methods for statistical inference such as the bootstrap and permutation test;
- will be familiar with the Metropolis-Hastings algorithm, as an example of a Markov Chain Monte Carlo method;
- is familiar with some widely used numerical methods;
- will be able to translate new statistical methods from the literature into a usable R program.;
- know the role of link functions and error distributions;
- be familiar with the most commonly used generalized linear models;
- know when to apply which model in practice;
- know the most commonly used methods for checking model appropriateness and model fit;
- be able to perform GLM analyses using the appropriate software (R and SPSS);
- be able to interpret the output and report the results of GLM analyses in terms of the context of the research question;
- understand the difference between fixed and random effects;
- know when to apply a mixed model in practice;
- know the most commonly used methods for checking model appropriateness and model fit;
- be able to perform mixed model analyses using statistical software (R, SPSS);
- be able to interpret the output of mixed model analyses in terms of the context of the research question(s);
- be able to report the results of mixed model analyses to non-statistical investigators;
- understand the differences and similarities among factor analysis, cluster analysis, SEM and MANOVA;
- know when to apply which multivariate method in practice;
- know the most commonly used methods for checking model appropriateness and model fit;
- be able to perform multivariate analyses using the appropriate software (R or SPSS);
- be able to interpret the output of multivariate analyses in terms of the context of the research question(s);
- be able to report the results of multivariate analyses to non-statistical investigators.
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Period (from – till): short courses during whole study year
Lecturer(s):
Staf Julius Centrum
Description
Each student is expected to attend so called ‘specialisations’ Each specialisation consists of obligatory and free to be chosen courses. These lecture days of choice should consice of (advanced) epidemiological or statistical courses. Listed below are the courses per specialisation.
| Medical Statistics (MS) – program director Dr. MJC Eijkemans |
| With the following obligatory courses |
| Generalized Linear Models (1.5EC) |
| Mixed Models (1.5EC) |
| Computational Statistics (1.5EC) |
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Add other courses * to reach the total amount of EC’s for the specialisation programme such as;
Inference and models (1.5EC)
Survival Analysis (1,5 EC) |
* These other courses do not need to be within the same specialisation, but they do need to:
- be epidemiological courses and/or statistical courses and/or;
- be for PhD students with a TSA, part of the obligatory programme specific courses at the PhD program the student is enrolled in.
Literature/study material used
Depending on chosen courses.
Mandatory for students in own Master’s programme:
N.A.
Optional for students in other GSLS Master’s programme:
No
Prerequisite knowledge:
Introduction to Epidemiology
Introduction to Statistics
Classical Methods in Data Analysis
Modern Methods in Data Analysis
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