Graduate School of health and Medical Sciences

Quantitative Bias Analysis for Epidemiologic Research
Provider: Faculty of Health and Medical Sciences

Activity no.: 3649-25-00-00	There are 18 available seats
	Enrollment deadline: 01/04/2025

Date and time	26.08.2025, at: 09:00 - 28.08.2025, at: 16:00
Regular seats	20
Course fee	3,720.00 kr.
Lecturers	Naja Hulvej Rod
ECTS credits	1.70

Contact person	Kathe Jensen E-mail address: kje@sund.ku.dk

Enrolment Handling/Course Organiser	PhD administration E-mail address: phdkursus@sund.ku.dk

Aim and content This course is free of charge for PhD students at Danish universities (except Copenhagen Business School), and for PhD Students from NorDoc member faculties. All other participants must pay the course fee. Anyone can apply for the course, but if you are not a PhD student at a Danish university, you will be placed on the waiting list until enrollment deadline. This also applies to PhD students from NorDoc member faculties. After the enrollment deadline, available seats will be allocated to applicants on the waiting list. This course is free of charge for PhD students at Danish universities (except Copenhagen Business School), and for PhD Students from NorDoc member universities. All other participants must pay the course fee. Anyone can apply for the course, but if you are not a PhD student at a Danish university, you will be placed on the waiting list until enrollment deadline. This also applies to PhD students from NorDoc member universities. After the enrollment deadline, available seats will be allocated to applicants on the waiting list. Learning objectives A student who has met the objectives of the course will be able to: - Recognize the types of bias in epidemiologic studies that are amenable to quantitative bias analysis. - Conduct simple, multidimensional, and probabilistic bias analyses using summary data in Microsoft Excel™ and interpret the output. - Conduct basic probabilistic bias analysis in Microsoft Excel™ using a record level dataset and interpret the results. - Demonstrate a critical understanding of the assumptions underlying each approach to quantitative bias analysis. - Distinguish between probability distributions for use in quantitative bias analysis and implement each. - Discuss the strengths and limitations of each approach as applied to real datasets. Content Students of epidemiology are well versed in ways to reduce systematic error (bias) in the design of their studies and to describe random error in the analysis of their studies through confidence intervals and p values. However, students are rarely taught methodologies for quantifying systematic error in their studies. Quantitative bias analysis (QBA) provides a methodology for assessing the impact of bias on study results by making assumptions about the bias parameters. QBA allows for assessment of both the direction and magnitude of systematic error and gives an estimate of effect (or a series of estimates of effect) that would have occurred had the bias been absent, assuming the bias parameters are correct. Such analyses allow investigators to go beyond speculation about the bias in discussion section of manuscripts and can be a powerful tool for quantifying the impact of such biases. This course will cover simple and multidimensional bias analysis methods that can be used to gain a better understanding of the impact of unmeasured confounding, selection bias and misclassification (measurement error) on study results. These methods can be applied to nearly any dataset, even summary data presented in the literature. Such approaches lay the foundation for more complicated methods, but by themselves, they act as if the bias parameters are known with certainty. We will then continue with probabilistic bias analysis, which requires specification of probability distributions about the bias parameters and then uses Monte Carlo simulations methods to create intervals accounting for the uncertainty in the systematic error. Finally, we will finish with methods for combining the systematic error to create simulation intervals that account for the total error (systematic and random) in the study results. Participants The course is aimed at PhD students in public health and epidemiology. There will be a maximum of 20 students. 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: - Public Health and Epidemiology - Biostatistics and Bioinformatics - All graduate programmes Language English Form The course will consist of five 3-hour sessions. The course will include a combination of lectures, group work, discussions, and individual exercises. Course director Naja Hulvej Rod, Professor, Section of Epidemiology, Department of Public Health, University of Copenhagen, nahuro@sund.ku.dk Teachers Matthew Fox, Professor, Department of Epidemiology, Boston University School of Public Health, Boston University Dates August 26, 27, and 28, 2025 August 26 Morning (9 am to 12 noon): Reasoning under uncertainty and simple bias analysis methods (selection bias) Afternoon (1 to 4 pm): Simple bias analysis II (uncontrolled confounding) August 27 Morning (9 am to 12 noon): Simple and Multidimensional Bias Analysis (misclassification/information bias) Afternoon (1 to 4 pm): Probabilistic bias analysis (probability distributions, summary level datasets, creating summary intervals) August 28 Morning (9 am to 12 noon): Record level corrections and multiple Bias Analysis Course location University of Copenhagen, CSS, Øster Farimagsgade 5, 1353 Copenhagen K Registration Please register before April 1st, 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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