| Responsible Department | Department of Large Animal Sciences | ||||||||||||
| Course Dates | The course is taught as e-learning in weeks 43-50, 2012. During this period of time, the student must expect to participate 10-30 hours per week. | ||||||||||||
| Course Abstract | The aim of the course is that the participant can establish the association between hypotheses, data and suitable analyses - and carry out, present and interpret the results of such analyses on the participants own data in the form of an outline of a manuscript for an international journal. | ||||||||||||
| Course Registration | To sign up for the course, please send an e-mail to Søren S. Nielsen at ssn@life.ku.dk. Please also remember to add the course to your PhD plan. | ||||||||||||
| Deadline for Registration | 1 Sep 2012 | ||||||||||||
| Credits | 6 (ECTS) | ||||||||||||
| Level of Course | PhD course | ||||||||||||
| Organisation of Teaching | The course is taught during weeks 43-50, 2012 as an e-learning course. During this period, the participants must write a report with analyses of epidemiological data as a basis for oral examination. Own data can be used if approved by the course leader. | ||||||||||||
| Language of Instruction | English | ||||||||||||
| Course Content | |||||||||||||
| This is an advanced course in epidemiology focusing on statistical methods used in epidemiology to analyze continuous, dichotomous and count data. The course is not a statistics course. Rather, the aim of the course is to enable the participant to establish the association between hypotheses, data and suitable analyses - and to carry out, present and interpret the results of such analyses on the participants own data in the form of an outline of a manuscript for an international journal. The course focus on epidemiological analysis of continuous and dichotomous data. Descriptors are hypothesis testing, causality and bias, correlation and linear regression, analysis of variance, assumptions, Chi-square test, logistic regression and logistic analysis, multivariable logistic analysis, interaction, confounding and non-parametric analyses. | |||||||||||||
| Teaching and learning Methods | |||||||||||||
| The course is taught as an e-learning course, where the student should provide a project report. During the study period, the student is expected to continously: a) present project parts in e-learning environment, as the basis for discussions with peers, b) contribute to the discussion of other students' report-parts. The students will work in groups. Each participant can provide own data set to be used for the project report. If these data are not found suitable by the course leader for the project report, other students' data will be used, or the course will provide suitable data. The data must be available at the beginning of the first course week. The course will use R software for data analyses. The participants should have skills in Epidemiology and R equivalent to Veterinary epidemiology, part 1 (course no LPhD102). Notice that the extent of the course can exceed 165 hours if the data you bring to the course are complex, you have not prepared the data or your problem is insufficiently described prior to the course. | |||||||||||||
| Learning Outcome | |||||||||||||
| The aim of the course is that the participant can establish the association between hypotheses, data and suitable analyses - and carry out, present and interpret the results of such analyses on the participants own data in the form of an outline of a manuscript for an international journal. At the end of the course it is expected that the participant has the following qualifications: Knowledge: Identify an epidemiological problem to be investigated using relevant analytical methods. Specifically, the participant should be able to identify and address potential problems in the data, such as bias and/or confounding and unwanted clustering. Skills: Use relevant epidemiological and statistical methods for descriptive and analytical epidemiological studies. Competences: Collaborate scientifically with epidemiologists and statisticians and other relevant scientists. Be able to evaluate the validity and reliability of the epidemiological results in relation to generalising to other populations than just the study population. | |||||||||||||
| Course Literature | |||||||||||||
| Houe H, Ersbøll AK, Toft N: Introduction to Veterinary Epidemiology. Biofolia. 2004. Dohoo I, Martin W, Stryhn H: Veterinary Epidemiologic Research. 2nd ed. Ver Inc. 2009. Dalgaard P: Introductory Statistics with R. Springer. 2008. | |||||||||||||
| Course Coordinator | |||||||||||||
| Søren Saxmose Nielsen, ssn@life.ku.dk, Department of Large Animal Sciences/Populationsbiology, Phone: 353-33096 | |||||||||||||
| Other Lecturers | |||||||||||||
| Nils Toft | |||||||||||||
| Type of Evaluation | |||||||||||||
| Project report and presentation | |||||||||||||
| Work Load | |||||||||||||
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