The DOI matches its Crossref record; 19 of the 20 references it cites were found in a registry or an index.
Multiple regression of cost data: use of generalised linear models
Abstract
Objective: Choosing an appropriate method for regression analyses of cost data is problematic because it must focus on population means while taking into account the typically skewed distribution of the data. In this paper we illustrate the use of generalised linear models for regression analysis of cost data. Methods: We consider generalised linear models with either an identity link function (providing additive covariate effects) or log link function (providing multiplicative effects), and with gaussian (normal), overdispersed poisson, gamma, or inverse gaussian distributions. These are applied to estimate the treatment effects in two randomised trials adjusted for baseline covariates. Criteria for choosing an appropriate model are presented. Results: In both examples considered, the gaussian model fits poorly and other distributions are to be preferred. When there are variables of prognostic importance in the model, using different distributions can materially affect the estimates obtained; it may also be possible to discriminate between additive and multiplicative covariate effects. Conclusions: Generalised linear models are attractive for the regression of cost data because they provide parametric methods of analysis where a variety of non-normal distributions can be specified and the way covariates act can be altered. Unlike the use of data transformation in ordinary least-squares regression, generalised linear models make inferences about the mean cost directly.
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“Cost data and QALYs will be analysed using generalised linear models to account for non-normal distributions.83”
From Development, evaluation and implementation of a digital behavioural health treatment for chronic pain: study protocol of the multiphase DAHLIA project (2022) · cites this paper as [83]
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Njue, S. G., Mugo, M. G., Kibet, E., Kariuki, J. M., Kiptanui, E., Mbae, C. K., Otieno, P., Kariuki, S., & Were, F. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1828318
“Given the distributional properties of the data, a generalized linear model was considered appropriate, as it relaxes the assumptions of the OLS regression (50– 53).”
Terrell, K. A., Lage, M. J., Valentine, W. J., Paik, J. S., & Boye, K. S. (2026) · Advances in Therapy · doi:10.1007/s12325-026-03753-7
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Vitale, J., Palese, A., Pinardi, M., D’Alonzo, M., Lanza, M., Formica, D., & Pino, G. D. (2026) · IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT) · doi:10.1109/metroind4.0iot69397.2026.11653054
Krishnan, S. M., Gc, V. S., Bromilow, T., Moss, J. W. E., Booth, K., Sandhu, H. K., Manca, A., Iglesias-Urrutia, C. P., & the I‐WOTCH team (2026) · Addiction · doi:10.1111/add.70534
Metadata
- Authors
- Barber, J., & Thompson, S.
- Affiliations
- University College London (GB)
- MRC Biostatistics Unit (GB)
- Venue
- Journal of Health Services Research & Policy
- Publisher
- SAGE Publications
- Published
- 2004-10-02
- Volume
- 9
- Issue
- 4
- Pages
- 197-204
- ISSN
- 1355-8196 (ISSN-L)
- 1758-1060
- OpenAlex
- W1996146145
- Cited by
- 404
- References
- 20
- Citations by year
- 2026: 11 · 2025: 11 · 2024: 17 · 2023: 18 · 2022: 27 · 2021: 29 · 2020: 32 · 2019: 29 · 2018: 32 · 2017: 28 · 2016: 28 · 2015: 23 · 2014: 22 · 2013: 17 · 2012: 20
- FWCI
- 7.09
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Health Services Research & Policy (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- cost data, generalized linear models, skewed distributions, gamma distribution, inverse Gaussian distribution, log link function, treatment effects, baseline covariate adjustment, model selection criteria
- MeSH terms
- Costs and Cost Analysis, United Kingdom, Health Services Research, Normal Distribution, Linear Models
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