Graduate School of health and Medical Sciences

Basic Statistics for health researcher (English course)
Provider: Faculty of Health and Medical Sciences

Activity no.: 3305-23-00-00	There are no available seats
	Enrollment deadline: 17/03/2023

Date and time	17.04.2023, at: 08:00 - 24.05.2023, at: 15:00
Regular seats	30
Course fee	7,200.00 kr.
Lecturers	Paul Blanche
ECTS credits	8.00

Contact person	Susanne Kragskov Laupstad E-mail address: skl@sund.ku.dk

Enrolment Handling/Course Organiser	PhD administration SUND E-mail address: 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 NorDoc member universities. All other participants must pay the course fee Course title Basic statistics for health researchers (English course). Learning objectives After finishing the course, the participants will • 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. • be able to understand and interpret the results of basic statistical procedures (t-test, Wilcoxon rank test, associations in 2x2 tables, linear regression, multiple linear regression, logistic regression, survival analysis). • know the assumptions involved in the basic statistical procedures, and why these are not all equally important, depending on the aim of the analysis. • be able to carry out these basic statistical procedures using one of the mainstream statistical software packages. • have learned about graphical tools for assessing the fit of the statistical model and basic recipes to make remedies such as transformation of outcome and/or covariates. • have a thorough understanding of the concepts of confounding and interaction, preferably in the context of their own work. • 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). • know when to seek expert help. The participants are invited to bring their own data to the exercise sessions, since in quiet moments there will be a limited access to discuss these in the light of the topics covered in the course. Content Topics covered: Descriptive statistics: mean, standard deviation, quantiles, percentages. Basic concepts of statistical inference: parameter estimate, confidence interval, p-value, significance level, power, multiple testing. Analysis of quantitative measurements: group comparisons, regression and the general linear model. Categorical data: association in two-way tables, logistic regression analysis. Survival analysis: Kaplan-Meier, Cox regression. Sample size determination, regression to the mean, confounding and interaction, association versus causation. Statistical software The focus of this course is not on how to use statistical software. But statistical software is needed for all data analyses and examples that illustrate the statistical methods. The free statistical software R will be used throughout the course. It is expected that the students learn the syntax and semantics of R before and during the course by themselves. Note that this will often mean a lot of extra hours for preparation and self-training in addition to the actual teaching hours. All students are expected to start working with R syntax and semantics several weeks before the course starts. A minimum level corresponding to that obtained after completed our online introduction to R at http://r.sund.ku.dk/ is considered as a prerequisite. In this introduction, we guide you through how to install R, how to load data, data manipulation and simple calculations and plots. Estimated number of hours to complete the introduction: 10-15 hours depending on your R- and technical skills. You can start working with the introduction now if you have limited time up to the course start. The participants are expected to use their own laptops during the course, to have installed all relevant software and to have downloaded all data for use during the course. Participants Ph.D. students and visiting researchers. Max. 30 participants. Relevance to graduate programs The course is relevant to PhD students from the following graduate programs at the Graduate School of Health and Medical Sciences, UCPH: All graduate programmes Language English Form Forum lectures and computer exercises. Most course days require preparation (usually 1-2 hours). A homework assignment is handed out after lecture 5. Participants work with their own data or data related to their own research provided by their PhD supervisor. The homework assignment is turned in after lecture 9. This highly ambitious course gives many ECTS points, if - you attend 80% of all teaching units (we count the signatures) - you present the results of your homework on the last course day. Please note that the course has approximately 25 hours of extra preparation: • Preparation before course start, online introduction to software: 10 hours • Mandatory home assignment: 10 hours • Reading and watching videos to complement the lectures: 5 hours Course director Associate professor Paul Blanche. Teachers Associate professor Paul Blanche and others, all affiliated to the Section of Biostatistics. Dates 17, 19, 24, 26 April, 3, 8, 10, 15, 17, 24 May 2023, all days 8-15. Course location CSS Registration Please register before 17 March 2023. 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 enrolment. 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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