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.
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.
Missing Data in Health Economics and Outcomes Research
Co-Chairs of Key Project:
Necdet Gunsoy, PhD, MPH, Manager 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
Webinars (coming soon)
Estimating Treatment Effects Using Observational Data
Longitudinal Data Analysis for Health Economics and Outcomes Research
Handling Missing Values in Real-World Data: Are There Challenges for Regulatory Decisions for Medical Products?
May 2018 – ISPOR 2018, Forum Presentation, Baltimore, MD, USA
Pragmatic Clinical Trials to Estimate Treatment Effects: Are They Worth the Effort?
November 2017 – ISPOR 20th Annual European Congress, Issue Panel Presentation, Glasgow, Scotland, UK
To join this SIG Working Group, see: Join a Special Interest Group.