Mission


To provide statistical leadership for strengthening the use of appropriate statistical methodology in health economics and outcomes research and improve the analytic techniques used in real world data analysis.

Goal


  • To review and recommend statistical methods used in outcomes research studies
  • To provide insight on the impact of using appropriate statistical methodology in a rigorous manner to enhance credible research
  • To better understand the limitations of analyses and methods used by the HEOR community

Background


Statistics are used in many outcomes research studies, in which understanding the proper use of the methods and approaches that have been developed around certain types of analyses is crucial to producing reliable and valid results that may be reproducible to the rest of the research community. Appropriate application of statistics is the foundation of credible research.

To answer a number of research questions, there are various statistical methods used in real world data analysis such as multiplicity, Bayesian analyses, estimation in disease state changes, model averaging, machine learning/artificial intelligence, and causal inferences, to name a few. However, there are large gaps in the statistical knowledge base as it relates to real world data analysis including the effect of missing values in claims databases, their impact on analyses, and appropriate methods for imputation.

Leadership


Rita Kristy, MS

Medical Affairs, Astellas Pharma
Northbrook, IL, United States

Gian Luca Di Tanna

Chair
Head of Statistics, The George Institute for Global Health
Sydney, NSW, Australia

David Vanness, PhD

Chair-elect
Professor, The Pennsylvania State University
University Park, PA, United States

Working Groups:


Key Project

Missing Data in Health Economics and Outcomes Research

Co-Chairs:

  • Necdet Gunsoy, PhD, MPH, Director of Analytics and Innovation for Value Evidence and Outcomes at GlaxoSmithKline (GSK), England, United Kingdom
  • Gianluca Baio, PhD, MSc , Reader in Statistics and Health Economics, University College London (UCL), England, United Kingdom

Member Engagement

Webinars

Machine Learning for Health Economics and Outcomes: Prediction and Causal Inference on Friday, November 15, 2019

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