Multivariate Generalized Linear Mixed-Effects Models for the Analysis of Clinical Trial–Based Cost-Effectiveness Data

Felix Achana, Daniel Gallacher, Raymond Oppong, Sungwook Kim, Stavros Petrou, James Mason and Michael Crowther
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Felix Achana: Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford, UK
Daniel Gallacher: Warwick Evidence, Warwick Medical School, University of Warwick, Coventry, Warwickshire, UK
Raymond Oppong: Health Economics Unit, Institute of Applied Health Research, University of Birmingham, Birmingham, West Midlands, UK
Sungwook Kim: Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford, UK
Stavros Petrou: Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford, UK
Michael Crowther: Biostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, Leicestershire, UK

Medical Decision Making, 2021, vol. 41, issue 6, 667-684

Abstract: Economic evaluations conducted alongside randomized controlled trials are a popular vehicle for generating high-quality evidence on the incremental cost-effectiveness of competing health care interventions. Typically, in these studies, resource use (and by extension, economic costs) and clinical (or preference-based health) outcomes data are collected prospectively for trial participants to estimate the joint distribution of incremental costs and incremental benefits associated with the intervention. In this article, we extend the generalized linear mixed-model framework to enable simultaneous modeling of multiple outcomes of mixed data types, such as those typically encountered in trial-based economic evaluations, taking into account correlation of outcomes due to repeated measurements on the same individual and other clustering effects. We provide new wrapper functions to estimate the models in Stata and R by maximum and restricted maximum quasi-likelihood and compare the performance of the new routines with alternative implementations across a range of statistical programming packages. Empirical applications using observed and simulated data from clinical trials suggest that the new methods produce broadly similar results as compared with Stata’s merlin and gsem commands and a Bayesian implementation in WinBUGS. We highlight that, although these empirical applications primarily focus on trial-based economic evaluations, the new methods presented can be generalized to other health economic investigations characterized by multivariate hierarchical data structures.

Keywords: cluster randomised controlled trials; cost-effectiveness analysis; economic evaluation alongside randomised controlled trials; multicentre and multinational randomised controlled trials (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:41:y:2021:i:6:p:667-684

DOI: 10.1177/0272989X211003880

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