Reference-based multiple imputation for missing data sensitivity analyses in trial-based cost-effectiveness analysis.

ABSTRACT: Missing data are a common issue in cost-effectiveness analysis (CEA) alongside randomised trials and are often addressed assuming the data are 'missing at random'. However, this assumption is often questionable, and sensitivity analyses are required to assess the implications of departures from missing at random. Reference-based multiple imputation provides an attractive approach for conducting such sensitivity analyses, because missing data assumptions are framed in an intuitive way by making reference to other trial arms. For example, a plausible not at random mechanism in a placebo-controlled trial would be to assume that participants in the experimental arm who dropped out stop taking their treatment and have similar outcomes to those in the placebo arm. Drawing on the increasing use o

SUBMITTER: Leurent B 

PROVIDER: S-EPMC7004051 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature