Course catalogue doctoral education  HT21

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Title  Biostatistics I: Introduction for Epidemiologists 

Course number  3154 
Programme  Epidemiology 
Language  English 
Credits  3.0 
Notes 
The course meets the requirements for a general science course. 
Date  20211004  20211027 
Responsible KI department  Department of Medical Epidemiology and Biostatistics 
Specific entry requirements  
Purpose of the course  The aim is to introduce classical statistical concepts and methods with emphasis on methods used in epidemiology and public health. 
Intended learning outcomes  After successfully completing this course students should be able to:
define the concept of probability, laws of probability, and make simple probability calculations. (S2) suggest a statistical distribution to describe a naturally occurring phenomonen and evaluate the appropriateness of the distribution given real data. (S3) present appropriate descriptive statistics for an epidemiological study. (S2) explain the difference between hypothesis testing and interval estimation and the relation between pvalues and confidence intervals. (S3) suggest an appropriate statistical test for a comparison of two groups, perform the hypothesis test using standard statistical software, and interpret the results. (S3) estimate and interpret three alternative measures of association between binary exposures and binary outcomes and discuss the relative merits of each measure for a given research question. (S3) explain the concept of confounding in epidemiological studies and demonstrate how to control/adjust for confounding using stratified analysis. (S2) explain the basis of the linear regression model, fit a linear regression model using standard statistical software, assess the fit of the model, and interpret the results. (S2) Learning outcomes are classified according to Bigg's structure of the observed learning outcome (SOLO) taxonomy: (S1) unistructural, (S2) multistructural, (S3) relational, and (S4) extended abstract. 
Contents of the course  The course introduces classical statistical concepts and methods with emphasis on methods used in epidemiology and public health. Topics covered include: the importance of statistical thinking; types of data (nominal, binary, discrete and continuous variables); data summary measures; contingency tables; graphical representations; notions of probability; probability models (distributions); principles of statistical inference; parameter estimation (mean, proportion (prevalence), incidence and ratios); concepts of confidence intervals and hypothesis tests; and a general introduction to correlation and linear regression models. 
Teaching and learning activities  Lectures, exercises focusing on analysis of real data using the statistical software R, exercises not requiring statistical software, different group and individual assignments, literature review. 
Compulsory elements  The individual written examination (summative assessment) is compulsory. 
Examination  To pass the course, the student has to show that the learning outcomes have been achieved. Assessments methods used are group assignments (formative assessments) along with an individual examination (summative assessment). Students who fail will be offered a reexamination within two months of the final day of the course. Students who fail the reexam 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 another reexamination will be scheduled within 12 months of the final day of the course. 
Literature and other teaching material  Recommended texts:
 P. Dalgaard. Introductory Statistics with R. 2nd ed. Springer 2008.  Rosner B, Fundamentals of Biostatistics, 8th ed. 2016.  Pagano M and Gauvreau, Principles of Biostatistics 2nd ed. 2018.  Fisher and van Belle, Biostatistics: A Methodology for the Health Sciences 2nd ed 2004.  Moore DS and McCabe GP: Introduction to the Practice of Statistics, 9th ed. 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  The course is extended over time in order to promote reflection and reinforce learning. Course dates are October 4, 6, 8, 11 and 13 (week 1) and October 18, 20, 22, 25 and 27 (week 2). During the computer labs, the statistical software R will be used. 
Additional course leader  
Latest course evaluation  Course evaluation report 
Course responsible 
Erin Gabriel Department of Medical Epidemiology and Biostatistics erin.gabriel@ki.se 
Contact person 
Gunilla Nilsson Roos Institutionen för medicinsk epidemiologi och biostatistik 08524 822 93 gunilla.nilsson.roos@ki.se 