Course catalogue doctoral education - HT19

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Title Fundamentals of statistical modeling
Course number 2959
Programme Epidemiologi
Language English
Credits 1.5
Date 2019-05-20 -- 2019-05-24
Responsible KI department Institutet för miljömedicin
Specific entry requirements Courses "Epidemiology I: Introduction to epidemiology", "Epidemiology II: Design of epidemiological studies", "Biostatistics I: Introduction for epidemiologists", "Biostatistics II: Logistic regression for epidemiologists" and "Biostatistics III: Survival analysis for epidemiologists"or corresponding courses.
Purpose of the course The purpose of this advanced course is to provide an introduction to the tools of statistical modeling.
Intended learning outcomes After successfully completing this course the students should be able to do the following independently of others:
- explain the concepts of marginal and conditional distributions,
- illustrate the relationship between cumulative distribution, probability mass/density, quantile, sparsity, cumulative hazard, and hazard functions,
- propose possible models for the above functions both marginally and conditionally on covariates,
- identify suitable models to answer scientific research questions and motivate the choice,
- estimate the parameters of the above functions, and
- use standard statistical software, evaluate the fit of the model, and critically interpret the results.
Contents of the course The students are introduced to a general framework for data analyses that hinges on creating statistical models. The course focuses on the intricacies and potentials of modeling in a number of examples and real-data applications. The range of the covered examples is broad, and some examples are worked out in greater details than others. The course will enable students to gain an advanced knowledge of (1) random variables, (2) joint and conditional probability distributions, (3) modeling tools, (4) interpretation of statistical models, (5) relations between known methods, (6) estimation tools, (7) computer programming. The students will improve the level of knowledge of the foundations for data analysis, statistical practice, and use of statistical software. They will also be prepared to pursue more advanced studies in statistics. The focus of the course is on analysis of real data and interpretation.
Teaching and learning activities The course activities are based on lectures and computer exercises, exercises not requiring statistical software, and literature review. We will provide laptop computers to all participants, but participants are welcome to bring their laptops if they prefer.
Compulsory elements The individual examination (summative assessment) is compulsory.
Examination Individual written examination based on practical application of the course content, where the student has to show that all the intended learning outcomes have been achieved. Students who do not pass the examination will be offered a second examination within two months from the end of the course.
Literature and other teaching material There is no required literature. Lecture notes will be provided at the start of the course.
Number of students 8 - 25
Selection of students Eligible doctoral students, with required prerequisite knowledge, prioritized according to 1) the relevance of the course syllabus for the applicant's doctoral project (according to written motivation), 2) date for registration as a doctoral student (priority given to earlier registration date). To be considered, submit a completed application form. Give all information requested, including a description of current research and motivation for attending, and an account of previous courses taken.
More information The individual examination will be performed as a take­home examination.
Additional course leader
Earlier evaluation of the course Evaluation report
Course responsible Matteo Bottai
Institutet för miljömedicin
08-524 870 24
matteo.bottai@ki.se
Contact person Johanna Bergman
Institutet för miljömedicin

johanna.bergman@ki.se

Nobels väg 13

17177
Stockholm