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Basic Statistics for Health Researchers (Danish course) - SAS
Provider: Faculty of Health and Medical Sciences

Activity no.: 3304-21-00-09 
Enrollment deadline: 06/08/2021
Date and time06.09.2021, at: 10:00 - 17.11.2021, at: 18:00
Regular seats15
Course fee6,480.00 kr.
LecturersSusanne Rosthøj
ECTS credits9.00
Contact personSusanne Kragskov Laupstad    E-mail address: skl@sund.ku.dk
Enrolment Handling/Course OrganiserPhD administration     E-mail address: fak-phdkursus@sund.ku.dk

Aim and content
This is a generic course. This means that the course is reserved for PhD students at the Graduate School of Health and Medical Sciences at UCPH. Anyone can apply for the course, but if you are not a PhD student at the Graduate School, you will be placed on the waiting list until enrollment deadline. After the enrolment deadline, available seats will be allocated to the waiting list.

The course is free of charge for PhD students at Danish universities (except Copenhagen Business School), and for PhD students at graduate schools in the other Nordic countries. All other participants must pay the course fee.

Learning objectives
A student who has met the objectives of the course will be able to:

After finishing the course, the participants will

1.have a general feeling for the ideas in a statistical model and the type of conclusions that can be drawn from the subsequent statistical analysis

2.be able to understand and interpret the results of basic statistical procedures (t-test, associations in 2x2 tables, linear regression, multiple linear regression, logistic regression, survival analysis)

3.know the assumptions involved in the basic statistical procedures, and why these are not all equally important, depending on the aim of the analysis

4.be able to carry out these basic statistical procedures using one of the mainstream statistical software packages

5.have required tools for detecting gross misfit of the model and make remedies such as transformation of outcome and/or covariates

6.have a thorough understanding of the concepts of confounding and interaction, preferably in the context of their own work

7.know about estimation of association parameters, statistical significance and power, so that they can write the statistical methods and results sections for their own research reports (limited to basic statistical procedures)

8.know when to seek expert help.

Topics covered: Basic statistical concepts (probability, distribution, estimation, test of significance). Analysis of quantitative measurements (group comparisons, regression and the general linear model). Sample size determination. Categorical data (association in two-way tables, logistic regression analysis). Survival analysis (Kaplan-Meier, Cox regression). Correlated measurements, longitudinal data.

Statistical software - SAS:
This course uses SAS for the statistical analyses. Are you sure that you have chosen the right software and the right class? See this link for more information: http://staff.pubhealth.ku.dk/~sr/BasicStatistics/diverse/valg_af_software.html
For the exercise classes, the participants must bring their own laptops with SAS installed. As a Ph.D.-student, you can download SAS from the Self Service pages on KU-net, free of charge. RegionH also provides a license for SAS. Other users can use the web based version of SAS, SAS Studio. Before course start you are expected to become familiar with SAS. Find out how to enroll in our online introduction to SAS hosted on Absalon (KU’s Learning Management System) here:https://publicifsv.sund.ku.dk/~sr/BasicStatistics/misc/indskrivning.html. In the introduction we guide you through how to install/get access to SAS, how to load data, data manipulation and simple calculations and plots. Estimated number of hours to complete the introduction: 8 +/- 5 hours. You can start working with the introduction now if you have limited time up to the course start.

Ph.D.-students. In case of vacant seats, also other health researchers. Max. 15 participants.

Relevance to graduate programmes
The course is relevant to PhD students from the following graduate programmes at the Graduate School of Health and Medical Sciences, UCPH:

All graduate programmes


10 ? 3 hr forum lectures and 10 ? 3 hr class room exercises. The lectures will be recorded and made available online and physical attendance at the lectures is therefore optional. However, the quality of the recordings may vary and we therefore recommend physical attendance. Physical and active attendance in at least 8 out of the 10 class room exercises is required to pass the course. Some class room exercises do not require preparation, some will require that you have had a look at the exercises in advance (in groups, with assistance from a teacher) and each group will have to present a part of the exercise. Mandatory take home exercise. Please note that the course has approximately 74 hours of extra preparation:
-Preparation before course start, online introduction to software: 20 hours
-Mandatory home assignment: 30 hours
-Extra videos: 15 hours
-Extra preparation for class exercises: 9 hours

At least two weeks before course start you will receive an email on how to prepare for the first session. Please check your spam filter if you do not receive an email.

Course director
Associate professor Susanne Rosthøj

Associate professors Lene Theil Skovgaard, Susanne Rosthøj plus external teachers.


Course 9: 6, 8, 13, 15, 20, 22, 27, 29 September, 4, 6, 11, 13, 25, 27 October, 1, 3, 8, 10, 15, 17 November 2021 (Mondays 10-13, Wednesdays 15-18)

Course location

Please register before 6 August 2021.

Seats to PhD students from other Danish universities will be allocated on a first-come, first-served basis and according to the applicable rules. Applications from other participants will be considered after the last day of enrollment.

Note: All applicants are asked to submit invoice details in case of no-show, late cancellation or obligation to pay the course fee (typically non-PhD students). If you are a PhD student, your participation in the course must be in agreement with your principal supervisor.

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