Graduate School of health and Medical Sciences

Targeted Minimum Loss-based Estimation for Causal Inference in Biostatistics
Provider: Faculty of Health and Medical Sciences

Activity no.: 3340-21-00-00
	Enrollment deadline: 27/08/2021

Date and time	27.09.2021, at: 08:00 - 29.09.2021, at: 15:00
Regular seats	25
Course fee	2,520.00 kr.
Lecturers	Helene Charlotte Wiese Rytgaard
ECTS credits	2.10

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. Learning objectives A student who has met the objectives of the course will be able to: 1.Understand the roadmap of targeted learning, both from a theoreticaland an applied perspective 2.Describe the overall principle of statistical (asymptotic) inference based on TMLE 3.Translate certain biomedical applications into a mathematical formulation of the statistical estimation problem that needs to be solved 4.Use TMLE software in R to analyze randomized trial data and observational data Content Targeted minimum loss-based estimation (TMLE) is a general framework for estimation of causal effects that combines semiparametric efficiency theory and machine learning in a two-step procedure. The main focus of the course is to understand overall concept, the theory, and the application of TMLE. The course covers the following topics: • The roadmap of targeted learning. • Causal inference: Counterfactual notation, hypothetical interventions, g-formula, the average treatment effect (ATE). • Semiparametric models: Target parameter, nuisance parameter, efficient inuence function, asymptotic linearity, influence functions, statistical inference based on influence functions. • TMLE: Initial estimation, targeting step. • Super learning: Machine learning, loss-based cross-validation. • Examples: Survival outcome, time-dependent confounding, dynamic treatment regimes. Course material: A combination of research papers, textbook, and lecture notes. Participants The course is relevant for Ph.D.-students with sufficient background in mathematics and statistics. To participate in the practicals, the participants should have knowledge of the statistical software R. Max. 25 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 Three days with lectures and practicals. Course director Helene Charlotte Wiese Rytgaard, Assistant Professor, Section of Biostatistics, Department of Public Health. Teachers Helene Charlotte Wiese Rytgaard, Thomas Alexander Gerds, T.B.A. Dates 27, 28 and 29 September 2021, all days 8-15 Course location CSS Registration Please register before 27 August 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.

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