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Design and Analysis of Biomedical Studies

Vak
2017-2018

Period

Monday February 5th – Friday March 2nd 2018.

Description

This four-week course is about research methods for biomedical (pre-clinical and clinical) studies. The course aims at providing basic knowledge of frequently used designs and methods for biomedical studies. It is important that the student works with these designs and methods, and is able to discuss strengths and weaknesses. Three themes will be central in this course:

Design Choices between different study designs in animals or in humans, such as case-control, follow-up, randomized controlled trials will be discussed, as well as aspects specific to each of the study designs, for instance choice of controls, cross-over or parallel, randomisation, blinding.

Analysis Statistical methods to analyse data from these studies will be presented. The methods discussed include univariate and multivariate linear regression, logistic regression, survival analysis (Kaplan Meier survival curves and Cox regression), and linear mixed models.

Interpretation and validation Interpretation of the results involves the interpretation of relative risks, odds ratios and hazard ratios in univariate and multivariate analyses. Validation of the methods involves model checks, the reliability of measurements (inter- and intraobserver variation, kappa), the evaluation of diagnostic systems (ROC curves), and recognition of the principles of bias and confounding.
Other aspects discussed are literature search, database use and ethical aspects.

Course objectives

The student:

  • Explains methods of study design used in a clinical or population setting, and knows how these studies are performed

  • Discusses ethical and legal aspects of clinical studies with human subjects

  • Shows the ability to perform a comprehensive literature search for the design of a biomedical study by following reproducible methodology

  • Shows the ability to perform database searches for literature (PubMed and Web of Science) and biological items and concepts (such as OMIM, LOVD, Anni) and account for the method of literature searching by following reproducible methodology

  • Translates a clinical problem into a research question
    Design a biomedical study in an efficient way

  • Chooses, applies and interprets statistical analyses

  • Shows the ability to perform a comprehensive literature search for the design of a biomedical study by following reproducible methodology

  • Recognizes and discussed the possibility of bias and confounding in a study

  • Evaluates the reliability and reproducibility of measurement systems

  • Discusses ethical and legal aspects of clinical studies with human subjects

  • Designs a biomedical study in an efficient way

  • Discusses ethical and legal aspects of clinical studies with human subjects

  • Shows the ability to perform a comprehensive literature search for the design of a biomedical study by following reproducible methodology

  • Translates a clinical problem into a research question

  • Recognizes and discuss the possibility of bias and confounding in a study

Mode of instruction

This course consists of a number of lectures, 5 working groups, 9 self-study assignments, and 7 computer practicals. Depending on the exercise, the students will work together with one student, or in small groups.

Assessment method

Students will be evaluated by preparing a protocol and presentation for an animal experiment (pass), a theme assignment of information literacy (10% of final grade), a written report on a clinical trial (pass), a written report of each of the computer practicals (pass), and an examination (tentamen, 90% of final grade).

The nature of the examination is a combination of multiple choice and “open questions”. The final grade will be published in uSis as soon as all assignments have been turned in. There is a re-examination during spring.

Exam dates

  • Exam: Friday March 2nd 2018 from 13.00-16.00h

  • Re-exam: Monday May 7th 2018 from 13.00-16.00h

Reading list

  • A. Petrie, C. Sabin. Medical Statistics at a Glance.