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Title Biostatistics II: Logistic Regression for Epidemiologists
Course number 5314
Programme Epidemiologi
Language English
Credits 1.5
Notes The course meets the requirements for a general science course.

Date 2021-09-20 -- 2021-09-24
Responsible KI department Institutionen för global folkhälsa
Specific entry requirements Knowledge in epidemiology and biostatistics equivalent to Epidemiology I: Introduction to epidemiology and Biostatistics I: Introduction for epidemiologists or corresponding courses
Purpose of the course The course introduces statistical methods for the analysis of categorical outcome data.
Intended learning outcomes After successfully completing this course you as a student are expected to be able to:
- choose the appropriate regression model for studying a specific research hypothesis using data collected from an epidemiological study, implement the model using standard statistical packages, assess the goodness of fit, and interpret the results,
- explain the concept of confounding in observational studies and use statistical models to to control/adjust for confounding,
- apply appropriate statistical models to study and interpret effect modification,
- carefully read an epidemiological paper to critically review the methodological aspects of the article, with emphasis on the study assumptions, design, analysis and interpretation

Intended learning outcomes are classified according to Bloom´s taxonomy: knowledge, comprehension, application,
analysis, synthesis, and evaluation (Bloom, 1956, extended by Anderson and Krathwohl, 2001).
Contents of the course The course focuses on the formulation and application of the logistic regression model in the analysis of epidemiological studies to estimate relative and absolute effect measures. Topics covered include a brief introduction to binary outcome data, measures of associations in two-by-two tables, univariable and multivariable models, interpretation of parameters for continuous and categorical predictors, flexible modeling of quantitative predictors, confounding and interaction, model fitting and a glance to model diagnostics.
Teaching and learning activities Lectures, computer based assignments with applications focusing on analysis of real data sets, using statistical packages such as Stata or R, hand based exercises, group discussions and literature review.
Compulsory elements The individual take-home written examination (summative assessment).
Examination The student has to show that the learning outcomes have been achieved to pass the exam. The course grade is based on the individual written examination (summative assessment). The focus of the examination will be on the understanding of the underlying principles of categorical data models and their application to analysis of epidemiological studies, and therefore less emphasis will be given to mathematical details. 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 priority for admission to the next course's offering. 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 No mandatory literature. Any of the below textbooks are recommended reading.

Nicholas P. Jewel: Statistics for Epidemiology. Chapman & Hall/CRC, 2004.
Hosmer DW, Lemeshow S, and Sturdivant, RX. Applied Logistic Regression, 3rd Ed, A Wiley-Interscience Publication, John Wiley & Sons Inc., New York, NY, 2013.
Vittinghoff, E., Glidden, D.V., Shiboski, S.C., McCulloch, C.E. Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models (Statistics for Biology and Health) 3rd Ed. Springer-Verlag, New York, NY, 2017.
Number of students 8 - 25
Selection of students Eligible doctoral students are 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. Give all information requested, including a short description of current research training and motivation for attending, as well as an account of previous courses taken. Prior knowledge in any software, e.g. Stata, R or SAS is strongly recommended.
More information
Additional course leader
Latest course evaluation Course evaluation report
Course responsible Nicola Orsini
Institutionen för global folkhälsa

Nicola.Orsini@ki.se
Contact person Anastasia Urban
Institutionen för global folkhälsa
0852483350
anastasia.urban@ki.se