IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v41y2021i6p667-684.html
   My bibliography  Save this article

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

Author

Listed:
  • Felix Achana

    (Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford, UK
    Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, UK
    Warwick Evidence, Warwick Medical School, University of Warwick, Coventry, Warwickshire, 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
    Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, UK)

  • James Mason

    (Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, UK)

  • Michael Crowther

    (Biostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, Leicestershire, UK
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden)

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.

Suggested Citation

  • Felix Achana & Daniel Gallacher & Raymond Oppong & Sungwook Kim & Stavros Petrou & James Mason & Michael Crowther, 2021. "Multivariate Generalized Linear Mixed-Effects Models for the Analysis of Clinical Trial–Based Cost-Effectiveness Data," Medical Decision Making, , vol. 41(6), pages 667-684, August.
  • Handle: RePEc:sae:medema:v:41:y:2021:i:6:p:667-684
    DOI: 10.1177/0272989X211003880
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X211003880
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0272989X211003880?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Nicola J. Cooper & Paul C. Lambert & Keith R. Abrams & Alexander J. Sutton, 2007. "Predicting costs over time using Bayesian Markov chain Monte Carlo methods: an application to early inflammatory polyarthritis," Health Economics, John Wiley & Sons, Ltd., vol. 16(1), pages 37-56, January.
    2. Henningsen, Arne & Hamann, Jeff D., 2007. "systemfit: A Package for Estimating Systems of Simultaneous Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 23(i04).
    3. Richard Grieve & Richard Nixon & Simon G. Thompson & Charles Normand, 2005. "Using multilevel models for assessing the variability of multinational resource use and cost data," Health Economics, John Wiley & Sons, Ltd., vol. 14(2), pages 185-196, February.
    4. Andrew R. Willan & Eleanor M. Pinto & Bernie J. O'Brien & Padma Kaul & Ron Goeree & Larry Lynd & Paul W. Armstrong, 2005. "Country specific cost comparisons from multinational clinical trials using empirical Bayesian shrinkage estimation: the Canadian ASSENT‐3 economic analysis," Health Economics, John Wiley & Sons, Ltd., vol. 14(4), pages 327-338, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Christian E. H. Boehler & Joanne Lord, 2016. "Mind the Gap! A Multilevel Analysis of Factors Related to Variation in Published Cost-Effectiveness Estimates within and between Countries," Medical Decision Making, , vol. 36(1), pages 31-47, January.
    2. Andrew R. Willan & Matthew E. Kowgier, 2008. "Cost‐effectiveness analysis of a multinational RCT with a binary measure of effectiveness and an interacting covariate," Health Economics, John Wiley & Sons, Ltd., vol. 17(7), pages 777-791, July.
    3. Mark J. Sculpher & Karl Claxton & Mike Drummond & Chris McCabe, 2006. "Whither trial‐based economic evaluation for health care decision making?," Health Economics, John Wiley & Sons, Ltd., vol. 15(7), pages 677-687, July.
    4. David H. Bernstein & Christopher F. Parmeter, 2017. "Returns to Scale in Electricity Generation: Revisited and Replicated," Working Papers 2017-08, University of Miami, Department of Economics.
    5. Jon Anson, 2010. "Beyond Material Explanations: Family Solidarity and Mortality, a Small Area‐level Analysis," Population and Development Review, The Population Council, Inc., vol. 36(1), pages 27-45, March.
    6. Sean Pascoe & Peggy Schrobback & Eriko Hoshino & Robert Curtotti, 2023. "Impact of changes in imports and farmed salmon on wild-caught fish prices in Australia," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 50(2), pages 335-359.
    7. Joseph F. Levy & Marjorie A. Rosenberg, 2019. "A Latent Class Approach to Modeling Trajectories of Health Care Cost in Pediatric Cystic Fibrosis," Medical Decision Making, , vol. 39(5), pages 593-604, July.
    8. Karine Chevreul & Georges Haour & Sandy Lucier & Stephanie Harvard & Marie-Laure Laroche & Xavier Mariette & Alain Saraux & Isabelle Durand-Zaleski & Francis Guillemin & Bruno Fautrel, 2014. "Evolution of Direct Costs in the First Years of Rheumatoid Arthritis: Impact of Early versus Late Biologic Initiation - An Economic Analysis Based on the ESPOIR Cohort," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-9, May.
    9. Di Fang & Michael R. Thomsen & Rodolfo M. Nayga & Wei Yang, 2022. "Food insecurity during the COVID-19 pandemic: evidence from a survey of low-income Americans," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 14(1), pages 165-183, February.
    10. Maria Gheorghe & Susan Picavet & Monique Verschuren & Werner B. F. Brouwer & Pieter H. M. Baal, 2017. "Health losses at the end of life: a Bayesian mixed beta regression approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(3), pages 723-749, June.
    11. Lampe, Markus, 2009. "Effects of Bilateralism and the MFN Clause on International Trade: Evidence for the Cobden-Chevalier Network, 1860-1875," The Journal of Economic History, Cambridge University Press, vol. 69(4), pages 1012-1040, December.
    12. Kleiber Christian & Zeileis Achim, 2010. "The Grunfeld Data at 50," German Economic Review, De Gruyter, vol. 11(4), pages 404-417, December.
    13. Bernstein, David H. & Parmeter, Christopher F., 2019. "Returns to scale in electricity generation: Replicated and revisited," Energy Economics, Elsevier, vol. 82(C), pages 4-15.
    14. Thompson, Simon G. & Nixon, Richard M. & Grieve, Richard, 2006. "Addressing the issues that arise in analysing multicentre cost data, with application to a multinational study," Journal of Health Economics, Elsevier, vol. 25(6), pages 1015-1028, November.
    15. Mendez, Samara & Peacock, Jacob & The Humane League Labs, 2021. "Exploring the impact of plant-based milk alternatives in the US," OSF Preprints tdghp, Center for Open Science.
    16. Diani, Cecilia & Galimberti, Giuliano & Soffritti, Gabriele, 2022. "Multivariate cluster-weighted models based on seemingly unrelated linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
    17. Zeileis, Achim & Koenker, Roger, 2008. "Econometrics in R: Past, Present, and Future," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i01).
    18. Eleanor M. Pullenayegum & Kelly M. Sunderland & Jeffrey A. Johnson & Feng Xie, 2017. "Handling Regional Variation in Health State Preferences within a Country," Medical Decision Making, , vol. 37(3), pages 252-261, April.
    19. Hasibuan, Abdul Muis & Gregg, Daniel & Stringer, Randy, 2020. "Accounting for diverse risk attitudes in measures of risk perceptions: A case study of climate change risk for small-scale citrus farmers in Indonesia," Land Use Policy, Elsevier, vol. 95(C).
    20. Consolación Quintana-Rojo & Fernando E. Callejas-Albiñana & Miguel-Angel Tarancón & Pablo del Río, 2019. "Identifying the Drivers of Wind Capacity Additions: The Case of Spain. A Multiequational Approach," Energies, MDPI, vol. 12(10), pages 1-19, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:medema:v:41:y:2021:i:6:p:667-684. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.