TY - JOUR

T1 - Linear mixed models to handle missing at random data in trial-based economic evaluations

AU - Gabrio, Andrea

AU - Plumpton, Catrin

AU - Banerjee, Sube

AU - Leurent, Baptiste

N1 - Medical Research Council and UK Foreign, Commonwealth and Development Office (FCDO), Grant/Award Number: MR/R010161/1; National Institute for Health Research Health Technology Assessment (NIHR HTA), Grant/Award Number: 04/11/02

PY - 2022/6

Y1 - 2022/6

N2 - 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 familiarize readers with LMMs and demonstrate their implementation in CEA. We illustrate the approach on a randomized trial of antidepressants, 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.

AB - 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 familiarize readers with LMMs and demonstrate their implementation in CEA. We illustrate the approach on a randomized trial of antidepressants, 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.

KW - cost-effectiveness analysis

KW - missing data

KW - mixed-effects

KW - Randomised trial

KW - repeated measures

U2 - 10.1002/hec.4510

DO - 10.1002/hec.4510

M3 - Article

C2 - 35368119

VL - 31

SP - 1276

EP - 1287

JO - Health Economics

JF - Health Economics

SN - 1057-9230

IS - 6

ER -