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Causal Machine Learning for Health Economics and Outcomes Research- Virtual
LEVEL: Intermediate
TRACK: Methodological & Statistical Research
LENGTH: 4 Hours | Course runs 2 consecutive days, 2 hours each day
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Wednesday, 10 July 2024 | Course runs 2 consecutive days, 2 hours per day
10:00AM–12:00PM Eastern Daylight Time (EDT)
14:00PM–16:00PM Coordinated Universal Time (UTC)
16:00PM–18:00PM Central European Summer Time (CEST)
Thursday, 11 July 2024 | Course runs 2 consecutive days, 2 hours per day
10:00AM–12:00PM Eastern Daylight Time (EDT)
14:00PM–16:00PM Coordinated Universal Time (UTC)
16:00PM–18:00PM Central European Summer Time (CEST)
DESCRIPTION
Reimbursement agencies are increasingly seeking evidence that goes beyond simple population averages to make well-informed decisions regarding the effectiveness and cost-effectiveness of medical interventions. Crucial tasks, such as identifying subpopulations benefiting the most from treatments, and optimizing resource allocation, call for innovative, data-adaptive approaches that can also handle potential confounding bias present in observational data. Causal machine learning (ML) methods have emerged as a powerful tool in this domain, with the potential to provide invaluable insights that inform healthcare decision making.
This course aims to equip participants with essential concepts and practical applications of advanced techniques in causal ML, specifically tailored to the needs of health economics and outcomes research, with a focus on comparative effectiveness and cost-effectiveness analysis. A combination of lectures and hands-on R software demonstrations will facilitate the acquisition of practical skills and knowledge. Participants will be guided through real-world data examples, allowing them to apply learned concepts in the context of health economics.
The course faculty has an extensive background in state-of-the-art techniques for health economics and outcomes research. Their expertise spans statistical and econometric methods applied to observational data, machine learning, Bayesian statistical analysis, and decision modeling, and have instructed relevant short courses before. To participate in hands-on activities, participants need a laptop with R software installed. Installation instructions will be sent prior to the course.
PREREQUISITE: Participants are expected to possess a basic understanding of regression methods and health economics concepts. Familiarity with R is recommended for the practical sessions.
Faculty
Noemi Kreif, PhD
Senior Research Fellow (Associate Professor)
University of York
York, England, UK
Julia Hatamyar, PhD
Research Fellow
University of York
York, England, UK
David Glynn, PhD
Research Fellow and Assistant Professor
University of York
York, England, UK
4 Hours | Course runs 2 consecutive days, 2 hours each day
ISPOR short courses are designed to enhance knowledge and techniques in core health economics and outcomes research (HEOR) topics as well as emerging trends in the field. Short courses offer 4 or 8 hours of premium scientific education and an electronic course book. Active attendee participation combined with our expert faculty creates an immersive and impactful learning experience. Short courses are not recorded and are only available during the live course presentation.
