Graduate School of health and Medical Sciences

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

Activity no.: 3305-25-00-00	There are 2 available seats
	Enrollment deadline: 17/01/2025

Date and time	24.02.2025, at: 08:00 - 02.04.2025, at: 15:00
Regular seats	30
Course fee	10,200.00 kr.
Lecturers	Paul Blanche
ECTS credits	7.50

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

Enrolment Handling/Course Organiser	PhD administration 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 faculties. All other participants must pay the course fee Course title Basic statistics for health researchers (English course) Learning objectives This course will teach you how to use statistics in a research context by giving you a thorough presentation of basic statistical concepts and models illustrated with case studies from health science. A student who has met the objectives of the course will be able to: • Interpret basic statistical information from research papers: descriptive statistics, sample size calculations, estimates of effect or association, confidence intervals, and p-values. • Understand the basic statistical analyses most commonly used in health science: two-sample and paired t-test, linear regression, correlation, analysis of variance (ANOVA), analysis of covariance (ANCOVA), linear models, risk difference, relative risk, odds ratio, chi-square test, logistic regression, survival analysis, hazard ratio and linear mixed models. • Carry out the most commonly used basic statistical analyses using the R statistical software, interpret the results, and present them in appropriate tables and figures. • Recognize the limitations and potential misinterpretations of statistical analyses related to e.g. model violations, confounding, missing data, regression to the mean, lack of power, and multiple testing. • Follow advanced statistics courses from the Ph.D. school at the faculty of health science. • Take advice from or collaborate with a statistician, e.g. in the advisory service at the Section of Biostatistics. Content Basic statistical concepts (data types, distributions, estimation, confidence intervals). Significance tests (power and sample size calculation, adjustments for multiple testing). Planning and interpretation (exploratory vs confirmatory analyses, randomized vs observational studies, confounding, effect modification, estimation vs prediction, association vs causation). Analysis of quantitative outcomes (t-tests, ANOVA, linear regression, correlation, ANCOVA, multiple linear regression). Analysis of binary and categorical outcomes (association in two-way tables, logistic regression). Introduction to survival analysis (Kaplan-Meier curves, log-rank test, Cox regression). Introduction to analysis of repeated measurements and clustered data (linear mixed models, simplification). Prerequisites: • ESSENTIAL: A minimum level of familiarity with basic R corresponding to that obtained after completing the course “Introduction to R for basic statistics” (taking place one week before this course). This might be obtained via the online introduction at https://biostat.ku.dk/r/ , although it is often best achieved by attending the in-person course. The estimated number of hours to complete the online introduction is 10 to 15 hours, depending on your R- and technical skills. • RECOMMENDED: Familiarity with the most basic statistical concepts e.g., from completing a statistics course during previous education and from reading research papers. 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. 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. Participants work with their own data or data related to their own research provided by their PhD supervisor. The homework assignment is turned in during the second last week of the course. Before the deadline you can attend one day (non mandatory), where you can work on the assignment and receive feedback from the teachers. 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. Course director Associate professor Paul Blanche, Section of Biostatistics Teachers Associate professor Paul Blanche, assistant professor Alessandra Meddis, and others, all affiliated to the Section of Biostatistics. Dates 24, 26 February, 3, 5, 10, 12, 17, 19, 24, 26 March, 2 April 2025, all days 8-15. The 26th of March is optional. Course location CSS Registration Please register before 17 January 2025. 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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