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- Introduction to Outcomes Research
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- Modeling Health Care Costs – Part I: Characteristics of Health Care Costs
- Modeling Health Care Costs – Part II: Methods and Guidelines for Estimating Health Care Costs
- Modeling Health Care Costs – Part III: Estimation from Censored Data
- Introduction to Budget Impact Analysis (BIA) - Part I
- Introduction to Budget Impact Analysis (BIA) - Part II
- Markov Model Toolkit: Concepts, Assumptions and Examples
- An Introduction to Choice-Based Conjoint Analysis (Discrete Choice Experiments)
- Health-Related Quality of Life (HRQOL) – Basics
- Patient Reported Outcomes: Analysis and Interpretation
- Patient Reported Outcomes: Instrument Development
- Item Response Theory: A Conceptual Introduction
- Preparing for Multinational Clinical Trials: Translation and Cultural Adaptations of PRO Measures
- Evidence Generation for Patient Reported Outcome (PRO) Label Claims (FDA Requirements)
- Intermediate Retrospective Observational Study - Propensity Scoring Methods
- Use of Public Survey Data in Health Outcomes Research
- Using RWE to Improve Healthcare Decision-making in Asia Pacific
- Top 10 HEOR Trends of 2019
- The Use of MCDA for HTA Agencies
- MCDA - How to Guide of Different Methods for Assessment of Rare Disease HT
- Virtualizing Long-Term Follow-Up Studies for Gene Therapy Products
- Is the FDA's New Guidance a Watershed Moment for Real-world Evidence?
- Topics in Evaluating the Cost-Effectiveness of Gene Therapies
- Principles of Machine Learning for Prediction
- Distance Learning
ISPOR MEMBERS ONLY Educational Webinar: Principles of Machine Learning for Prediction
Tuesday, July 30, 2019
12:00PM EDT | 5:00PM BST |6:00PM CEST- 1 hour in duration
Click here for time zone conversion
Speakers: David J. Vanness, PhD
This webinar will introduce learners to the basic principles of machine learning (ML) for prediction in health economics and outcomes research (HEOR). A second webinar by Professor Sherri Rose, this winter, will further explore principles of ML for causal inference in HEOR.
The webinar will begin by framing the prediction problem and drawing both connections and contrasts with the related problems of explanation and causal inference. It will then confront the “Iron Law of Prediction” – which characterizes the goals of prediction in terms of “minimizing loss” – or the consequences of making poor predictions. The Iron Law sets up an interesting tradeoff where we may tolerate some bias (systematic over- or under-prediction) in order to reduce the variance of predictions. The webinar will then explore the principles of “cross validation” and the “tuning” of algorithms to minimize loss. Using a simulated dataset, the presenter will apply several ML algorithms to illustrate the principles of “complexity penalization,” “bagging,” “random feature selection,” and “boosting.” The first webinar will conclude by considering an empirical application of boosting to construct propensity scores – a method of prediction that is used to improve causal inference for other parameters of interest.
This webinar series will use a combination of mild mathematical formalization (yes, there will be equations) and visualization. While advanced training in statistics, epidemiology, or econometrics is not required, a basic comfort with statistical concepts will help learners get the most out of the sessions. The R code used to generate and analyze the simulated dataset will be available for download.
What You Will Learn:
- Describe the Iron Law of Prediction as a tradeoff between bias and variance of predictions to minimize the loss associated with making poor predictions
- Describe how cross-validation allows analysts to minimize loss
- Describe how the process of tuning ML algorithms is used to optimize their performance in minimizing loss
- Describe how the bagging, random feature selection, and boosting can be used to minimize loss.
This is a Members ONLY webinar.
Members will receive an official member invitation via Email.
Not a member?
David J. Vanness, PhD
Professor of Health Policy and Administration
The Pennsylvania State University, USA
Chair, ISPOR Statistical Methods in Health Economics and Outcomes Research Special Interest Group
Please note: On the day of the scheduled webinar, the first 500 registered participants will be accepted into the webinar. For those who are unable to attend, or would like to review the webinar at a later date, the full-length webinar recording will be made available at the ISPOR Educational Webinar Series webpage approximately 2 weeks after the scheduled Webinar.
Reservations are on a first-come, first-served basis for all ISPOR members.