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

Baptiste Leurent,Manuel Gomes,Nicola Wiles,James R Carpenter,Suzie Cro

doi:10.1002/hec.3963

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 of this approach in other areas, this paper aims to extend and illustrate the reference‐based multiple imputation approach in CEA. It introduces the principles of reference‐based imputation and proposes an extension to the CEA context. The method is illustrated in the CEA of the CoBalT trial evaluating cognitive behavioural therapy for treatment‐resistant depression. Stata code is provided. We find that reference‐based multiple imputation provides a relevant and accessible framework for assessing the robustness of CEA conclusions to different missing data assumptions.

Highlights

Cost‐effectiveness analysis (CEA) of randomised trials provides an important source of information for decision making but is often limited by incomplete data collection
Under missing at random (MAR), participants in the cognitive behavioural therapy (CBT) arm had significantly higher Quality‐adjusted life‐years (QALYs) and costs than the usual care arm. This resulted in an incremental cost‐effectiveness ratio (ICER) of £11,260 per QALY and a 90.8% probability of CBT being cost‐effective at a willingness to pay threshold of £20,000 per QALY
It was assumed that participants dropping out from the CBT arm stopped engaging with the intervention and that their quality of life (QoL) and costs followed

Summary

Introduction

Cost‐effectiveness analysis (CEA) of randomised trials provides an important source of information for decision making but is often limited by incomplete data collection. Participants may withdraw before the end of the study or fail to complete a questionnaire This is common in longitudinal studies, where data are collected at multiple follow‐up points, as is often the case in CEA. The MAR assumption often provides a desirable starting point for missing data analyses as it implies that any differences between individuals with missing and complete information can be explained by differences in the observed data. This assumption may not always hold, as the missingness could depend on unobserved values, that is, data are missing not at random (MNAR; Little & Rubin, 2002). Participants in poorer health may be less likely to complete health‐related quality of life (QoL) questionnaires (e.g., EQ‐5D; EuroQol Group, 1990), conditional on their observed characteristics (Faria et al, 2014; Leurent et al, 2018)

Objectives

Results

Conclusion