Linear mixed models to handle missing at random data in trial-based economic evaluations

TL;DR

This paper explores the use of linear mixed effects models (LMMs) for handling missing data in trial-based economic evaluations, demonstrating their implementation and advantages over traditional methods like multiple imputation.

Contribution

It introduces LMMs as a simple, effective alternative for managing missing at random data in cost-effectiveness analyses, with practical implementation guidance.

Findings

LMMs provide unbiased estimates under MAR without imputation.

Application to a trial of antidepressants illustrates the method.

Implementation code is provided for R and Stata.

Abstract

Trial-based cost-effectiveness analyses (CEAs) are an important source of evidence in the assessment of health interventions. In these studies, cost and effectiveness outcomes are commonly measured at multiple time points, but some observations may be missing. Restricting the analysis to the participants with complete data can lead to biased and inefficient estimates. Methods, such as multiple imputation, have been recommended as they make better use of the data available and are valid under less restrictive Missing At Random (MAR) assumption. Linear mixed effects models (LMMs) offer a simple alternative to handle missing data under MAR without requiring imputations, and have not been very well explored in the CEA context. In this manuscript, we aim to familiarise readers with LMMs and demonstrate their implementation in CEA. We illustrate the approach on a randomised trial of antidepressant, and provide the implementation code in R and Stata. We hope that the more familiar statistical framework associated with LMMs, compared to other missing data approaches, will encourage their implementation and move practitioners away from inadequate methods.