A Scoping Review of Item-Level Missing Data in Within-Trial Cost-Effectiveness Analysis

Objectives: Cost-effectiveness analysis (CEA) alongside randomized controlled trials often relies on self-reported multi-item questionnaires that are invariably prone to missing item-level data. The purpose of this study is to review how missing multi-item questionnaire data are handled in trial-based CEAs.Methods: We searched the National Institute for Health Research journals to identify within-trial CEAs published between January 2016 and April 2021 using multi-item instruments to collect costs and quality of life (QOL) data. Information on missing data handling and methods, with a focus on the level and type of imputation, was extracted.Results: A total of 87 trial-based CEAs were included in the review. Complete case analysis or available case analysis and multiple imputation (MI) were the most popular methods, selected by similar numbers of studies, to handle missing costs and QOL in base-case analysis. Nevertheless, complete case analysis or available case analysis dominated sensitivity analysis. Once imputation was chosen, missing costs were widely imputed at item-level via MI, whereas missing QOL was usually imputed at the more aggregated time point level during the follow-up via MI.Conclusions: Missing costs and QOL tend to be imputed at different levels of missingness in current CEAs alongside ran-domized controlled trials. Given the limited information provided by included studies, the impact of applying different imputation methods at different levels of aggregation on CEA decision making remains unclear.