Methods for addressing missing data in health economic evaluation

Overview

Missing data are ubiquitous in health economic evaluation. The major concern that arises with missing data is that individuals with missing information tend to be systematically different from those with complete data. As a result, cost-effectiveness inferences based on complete cases are often misleading. These concerns face health economic evaluation based on a single study, and studies that synthesise data from several sources in decision models. While accessible, appropriate methods for addressing the missing data are available in most software packages, their uptake in health economic evaluation has been limited.

Taught by the leading experts in missing data methodology, this course offers an in-depth description of both introductory and advanced methods for addressing missing data in economic evaluation. These will include multiple imputation maximum likelihood, hierarchical approaches, Bayesian analysis and sensitivity analysis strategies using pattern mixture models and selection models. The course will introduce the statistical concepts and underlying assumptions of each method, and provide extensive guidance on the application of the methods in practice. Participants will engage in practical sessions illustrating how to implement each technique with user-friendly software (State and R). We welcome participants bringing their own data and problems, and one session is dedicated to discussion of participants' case-studies.

Objectives

By the end of the course the participants will be able to:

· Recognise the key statistical concepts, underlying assumptions and the relative merits of different statistical methods for dealing with missing data in cost-effectiveness analysis

· Perform a descriptive analysis of incomplete cost-effectiveness data

· Apply multiple imputation methods to address missing data in economic evaluation studies

· Conduct sensitivity analyses to assess whether cost-effectiveness inferences are robust to alternative missing data assumptions

· Consider Bayesian methods for addressing the missing data and bringing in additional evidence to inform the missing data assumptions

· Report and interpret cost-effectiveness results in light of contextually plausible missing data assumptions

Target audience

· Health economists, statisticians, policy advisors, or other analysts with an interest in health economic evaluation.

· Participants will be interested in undertaking or interpreting cost-effectiveness analyses that use   patient-level data, either from clinical trials or observational data.

· Should be familiar with running Stata from the command line

· No prior knowledge of methods for handling missing data is assumed

Concessions