  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.
