Graduate School of health and Medical Sciences

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Advanced Methods in Survival and Event History Analysis
Provider: Faculty of Health and Medical Sciences

Activity no.: 3310-26-00-00	There are 11 available seats
	Enrollment deadline: 02/10/2026

Date and time	02.11.2026, at: 08:00 - 26.11.2026, at: 15:00
Regular seats	12
Lecturers	Thomas Scheike
ECTS credits	5.60

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


Enrolment guidelines 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). All other participants must pay the course fee. Learning objectives A student who has met the objectives of the course will be able to: 1. Understand and explain core concepts of survival and event history analysis, including censoring, hazard functions, and time-to-event data structures. 2. Apply regression-based survival models, with particular emphasis on the Cox proportional hazards model, including model specification, estimation, diagnostics, and interpretation of results. 3. Analyze and interpret competing risks data, distinguishing between cause-specific and subdistribution hazard approaches and selecting appropriate methods for applied research questions. 4. Implement causal inference methods for survival data, including foundational concepts such as time-dependent confounding and advanced approaches such as marginal structural models. 5. Model recurrent event data, selecting and fitting appropriate models for repeated events and interpreting their results in applied settings. 6. Develop and evaluate prediction models for survival outcomes, including risk prediction, model validation, and assessment of predictive performance. 7. Understand the principles of group sequential trial designs, including interim analyses and stopping rules, and relate these methods to clinical research applications. 8. Translate methodological knowledge into practice, using statistical software to analyze real-world time-to-event data and critically evaluate survival analyses in the literature. Content This intensive course provides participants with a comprehensive introduction to advanced statistical methods for the analysis of time-to-event data. Delivered in two teaching blocks, the program covers both fundamental models and recent methodological developments, combining theoretical foundations with practical applications. Block 1 introduces participants to core models in survival analysis. Beginning with an overview presenting the concepts and demonstrating how the methods can be applied, the course proceeds to regression methods with a focus on the Cox proportional hazards model, before moving into the analysis of competing risks. The block concludes with an introduction to causal inference in survival data. Block 2 extends these concepts to more complex and applied settings. Topics include advanced approaches to causal inference, models for recurrent events, and prediction methods for survival outcomes. The course closes with an exploration of group sequential trials, linking methodological understanding to applications in clinical research. Each teaching day combines lectures with guided exercises, ensuring that participants gain both conceptual understanding and practical skills. The course is designed for graduate students, researchers, and professionals in biostatistics, who wish to deepen their knowledge of survival and event history analysis. Participants Participants are expected to have: A solid background in statistical inference, including likelihood-based estimation, hypothesis testing, and confidence intervals. Prior exposure to regression modeling, such as linear and generalized linear models. Familiarity with statistical software, preferably R, including data manipulation and basic model fitting. Graduate-level training in statistics, biostatistics, or equivalent professional experience. 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: Biostatistics and Bioinformatics All graduate programmes Language English Form A mix of forum lectures and computer exercises – bring your own laptop. Course director Thomas Scheike, Professor, Section of Biostatistics Teachers Staff from the Section of Biostatistics Dates 2, 3, 4, 5 November, 23, 24, 25, 26 November, 2026, all days 8.00-15.00 Course location CSS Registration Please register before 2 October 2026 Expected frequency If the course is recurrent and held at specific times each year, or you already know when the course is scheduled to be held again, you can state it here. 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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