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

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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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Notes

  1. 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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Acknowledgments

We wish to thank Dr Manuel Gomes for his comments and useful discussion on an earlier version of the paper, presented at the Health Economics Study Group (Sheffield, January 2014). We would also like to thank conference attendants for their suggestions on strengthening the paper. Dr. B. Kaambwa (Flinders University) has provided useful advice in analysing the TASMINH2 dataset. NJW was supported by an MRC Methodology Research Fellowship and the MRC ConDuCT Hub for Trials Methodology Research. TM was supported by an MRC ConDuCT Hub for Trials Methodology Research PhD studentship. At the time this work was conducted, PMM and LA were funded through National Institute for Health Research core funding to the Health Economics Unit, University of Birmingham.

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Authors and Affiliations

  1. School of Social and Community Medicine, University of Bristol, Bristol, UK

    Theodoros Mantopoulos & Nicky J. Welton

  2. Health Economics Unit, Public Health Building, University of Birmingham, Birmingham, B15 2TT, UK

    Paul M. Mitchell & Lazaros Andronis

  3. Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford, UK

    Richard McManus

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  1. Theodoros Mantopoulos
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Correspondence to Lazaros Andronis.

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Mantopoulos, T., Mitchell, P.M., Welton, N.J. et al. Choice of statistical model for cost-effectiveness analysis and covariate adjustment: empirical application of prominent models and assessment of their results. Eur J Health Econ 17, 927–938 (2016). https://doi.org/10.1007/s10198-015-0731-8

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