Graduate School of health and Medical Sciences

Statistics for experimental medical researchers (Online)
Provider: Faculty of Health and Medical Sciences

Activity no.: 3325-21-00-00
	Enrollment deadline: 06/04/2021

Date and time	04.05.2021, at: 08:00 - 08.06.2021, at: 15:00
Regular seats	40
Course fee	3,240.00 kr.
Lecturers	Julie Forman
ECTS credits	4.00

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 graduate schools in the other Nordic countries. All other participants must pay the course fee. Aim This six-day intensive course aims at Ph.D. students in biomedical research who work in a laboratory or similar setting, performing experiments on e.g. cells, tissues, mice, or human volunteers. When participating in this course, you will get a working knowledge of statistical concepts, methods of analysis, and adequate ways of presenting statistical results, as well as hands on experience in analysing experimental data with R statistical software. We will also explain some of the most common errors biomedical researchers make in their statistical analyses. In summary, we aim at teaching you high-quality statistics suitable for research publications. Learning objectives A student who has met the objectives of the course will be able to: 1. Have a qualified discussion with a statistical consultant, e.g. on how to plan the analyses for a research project or how to answer the concerns raised by a reviewer. 2. Interpret basic statistical information from research papers, e.g. descriptive statistics, effect estimates, confidence intervals and p-values. 3. Apply the most frequently used statistical analyses to real life experimental data using the statistical software R (see contents section for the specific analyses taught in this course). 4. Present statistical results in suitable figures, tables, and words. 5. Critically assess the validity of the most frequently used statistical analyses by being aware of their modelling assumptions and limitations. Content Day 1: Statistics in the research process. Planning, conducting, and reporting statistical analyses. Data types. Numerical and graphical descriptive statistics. The normal distribution. What is statistical uncertainty? Statistics and reproducibility: Making conclusions based on confidence intervals. Day 2: Comparing two groups or conditions: The two-sample t-test and the paired t-test. Understanding statistical testing. What is evidence? Making conclusions based on p-values. Statistical power. Adjustment for multiple testing. P-hacking and the reproducibility crisis. Day 3: Comparing two frequencies: Risk difference, risk ratio, and odds ratios. The chi-square test and Fisher’s exact test. Day 4: Assessing the association between two quantities: Linear regression and correlation. Day 5: Comparing more than two groups/conditions: One- and two-way analysis of variance (anova). Assessing effect modification (interaction). Day 6: Brief introduction to linear mixed models for repeated measurements and clustered data. Statistical software We will be working with the open source statistical software R using the interface R Studio. To participate in the course you must bring your own laptop with R and R Studio installed. Prerequisites Familiarity with R programming is necessary for taking part in the exercise classes and for completing the homework problems. If you are not familiar with R programming, we recommend that you complete the free access e-learning course at http://r.sund.ku.dk/ before starting on this course. Considering statistical theory, almost all of the students who participate in this course have completed a statistics course during previous education. For repetition and for emphasizing research applications, we start from scratch. However, you should be warned that we cover many topics in few days and at high pace. Participants Ph.D.-students. In case of vacant seats also other medical researchers. Max. 40 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 Language English Form Forum lectures and computer exercises 3 hours in the morning 3 hours in the afternoon on course days 1-2 and 5-6, 3 hours in the morning on course days 3-4. Compulsory homework assignments following course days 1-5 (we will go through the solution in plenary on the next course day). Note: In case the covid19 restrictions are still in power when the course commence, lectures and/or exercise classes may be converted to online or hyflex teaching. Course director Associate professor Julie Forman, Section of Biostatistics Teachers Associate professors Julie Forman and others. Dates 4, 11, 18, 25 May, 1, 8 June 2021, all days 8-15 Course location Online Registration Please register before 6 April 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 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.

Click the search button to search Courses.

See which courses you can attend and when

Jan	Feb	Mar	Apr
May	Jun	Jul	Aug
Sep	Oct	Nov	Dec

New courses
Courses are published regularly. High demand courses are announced in spring and autumn.

Learn which courses are announced on fixed dates