Syllabus database for doctoral courses
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Syllabus database for doctoral courses
SYLLABI FOR DOCTORAL COURSES
| Swedish title | Biostatistik II: Logistisk regression för epidemiologer |
|---|---|
| English title | Biostatistics II: Logistic Regression for Epidemiologists |
| Course number | 3043 |
| Credits | 2.0 |
| Notes |
The course meets the requirements for a general science course. |
| Responsible KI department | Institutionen för medicinsk epidemiologi och biostatistik |
| Specific entry requirements | Knowledge in epidemiology and biostatistics equivalent to ""Epidemiology I: Introduction to epidemiology"" and ""Biostatistics I: Introduction for epidemiologists"" or corresponding courses |
| Grading | Passed /Not passed |
| Established by | The Committee for Doctoral Education |
| Established | 2019-09-10 |
| Purpose of the course | The aim is to introduce statistical methods for categorical outcome data. |
| Intended learning outcomes | After successfully completing this course you as a student are expected to be able to:
- estimate and explain the difference between absolute and relative effect measures, including but not limited to odds ratio, risk ratio, and risk difference, - perform tests for multiple category outcome data, - fit and interpret the results of the logistic regression model, - apply and interpret appropriate statistical models for studying effect modification and confounding. - critically evaluate the methodological aspects (design and analysis) of a scientific article reporting an epidemiological study. |
| Contents of the course | This course focuses on the application of methods for binary data and in particular logistic regression in the analysis of epidemiological studies. Topics covered include a brief introduction two-by-two tables and methods for estimating relative effect measures. Then moving on to univariable and multivariable models for binary outcomes to estimate relative and absolute effect measures, with the interpretation of parameters categorical predictors, flexible modeling of quantitative predictors, confounding and interaction, model fitting and model diagnostics. |
| Teaching and learning activities | Lectures, exercises focusing on analysis of real data using statistical softwares, exercises not requiring statistical software, group discussions, literature review. |
| Compulsory elements | |
| Examination | To pass the course, the student has to show that the intended learning outcomes have been achieved. The course grade is based on the individual written examination (summative assessment). Students who do not obtain a passing grade in the first examination will be offered a second examination within two months of the final day of the course. Students who do not obtain a passing grade at the first two examinations will be given top priority for admission the next time the course is offered. If the course is not offered during the following two academic terms, then a third examination will be scheduled within 12 months of the final day of the course. |
| Literature and other teaching material | Suggested reading:
Hosmer DW, Lemeshow S, and Sturdivant, RX. Applied Logistic Regression, 3rd Ed, A Wiley-Interscience Publication, John Wiley & Sons Inc., New York, NY, 2013. Jewell NP. Statistics for Epidemiology, Chapman & Hall, New York, 2003. |
Course responsible |
Rino Bellocco Institutionen för medicinsk epidemiologi och biostatistik 0707330255 Rino.Bellocco@ki.se . |
| Contact person |
Gunilla Nilsson Roos Institutionen för medicinsk epidemiologi och biostatistik 08-524 822 93 gunilla.nilsson.roos@ki.se |
