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

by   Andrea Gabrio, et al.

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.


page 10

page 11


Non-compliance and missing data in health economic evaluation

Health economic evaluations face the issues of non-compliance and missin...

A Bayesian Parametric Approach to Handle Missing Longitudinal Outcome Data in Trial-Based Health Economic Evaluations

Trial-based economic evaluations are typically performed on cross-sectio...

Pitfalls of adjusting for mean baseline utilities/costs in trial-based cost-effectiveness analysis with missing data

Failure to account for baseline utilities/costs imbalance between treatm...

Multiple Imputation Approaches for Epoch-level Accelerometer data in Trials

Clinical trials that investigate interventions on physical activity ofte...

A Full Bayesian Model to Handle Structural Ones and Missingness in Economic Evaluations from Individual-Level Data

Economic evaluations from individual-level data are an important compone...

R-miss-tastic: a unified platform for missing values methods and workflows

Missing values are unavoidable when working with data. Their occurrence ...

Please sign up or login with your details

Forgot password? Click here to reset