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The European Journal of Health Economics

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07.10.2015 | Original Paper

Choice of statistical model for cost-effectiveness analysis and covariate adjustment: empirical application of prominent models and assessment of their results

verfasst von: Theodoros Mantopoulos, Paul M. Mitchell, Nicky J. Welton, Richard McManus, Lazaros Andronis

Erschienen in: The European Journal of Health Economics | Ausgabe 8/2016

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Abstract

Context

Statistical models employed in analysing patient-level cost and effectiveness data need to be flexible enough to adjust for any imbalanced covariates, account for correlations between key parameters, and accommodate potential skewed distributions of costs and/or effects. We compare prominent statistical models for cost-effectiveness analysis alongside randomised controlled trials (RCTs) and covariate adjustment to assess their performance and accuracy using data from a large RCT.

Method

Seemingly unrelated regressions, linear regression of net monetary benefits, and Bayesian generalized linear models with various distributional assumptions were used to analyse data from the TASMINH2 trial. Each model adjusted for covariates prognostic of costs and outcomes.

Results

Cost-effectiveness results were notably sensitive to model choice. Models assuming normally distributed costs and effects provided a poor fit to the data, and potentially misleading inference. Allowing for a beta distribution captured the true incremental difference in effects and changed the decision as to which treatment is preferable.

Conclusions

Our findings suggest that Bayesian generalized linear models which allow for non-normality in estimation offer an attractive tool for researchers undertaking cost-effectiveness analyses. The flexibility provided by such methods allows the researcher to analyse patient-level data which are not necessarily normally distributed, while at the same time it enables assessing the effect of various baseline covariates on cost-effectiveness results.

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The formula for calculating the SMD for a continuous covariate (x) is: \(SMD_{x} = \frac{{\mu_{x1} - \mu_{x2} }}{{\sqrt {(var_{x1} + var_{x2} )/2} }}\), where \(\mu_{x1} , \mu_{x2}\) and \(var_{x1} , var_{x2}\) are the means and variances for each group [40].

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Titel: Choice of statistical model for cost-effectiveness analysis and covariate adjustment: empirical application of prominent models and assessment of their results
verfasst von: Theodoros Mantopoulos
Paul M. Mitchell
Nicky J. Welton
Richard McManus
Lazaros Andronis
Publikationsdatum: 07.10.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: The European Journal of Health Economics / Ausgabe 8/2016
Print ISSN: 1618-7598
Elektronische ISSN: 1618-7601
DOI: https://doi.org/10.1007/s10198-015-0731-8

Springer Medizin

Abstract

Context

Method

Results

Conclusions

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