Abstract
The increasing complexity of health economics methodology has raised the need for technical methods to systematically use patient-level data and characterize uncertainty around the decision problem for decision makers. This chapter provides an introduction to these methods, focusing on trial-based statistical techniques and economic modeling methods for the purpose of health economic analysis. This chapter describes some differences between the more commonly used frequentist approach for clinical analysis and the developing use of Bayesian methods for health economic analysis. Statistical methods described include the use of power calculations, hypothesis testing, and regression analysis, and their relevance for economic analysis. More advanced statistical methods are also introduced, such as the area under the curve method for assessing incremental benefit, controlling for missing data and baseline characteristics, and using mapping algorithms for eliciting preference-based tariff scores when a preference-based measure has not been collected within a study. The second part of the chapter focuses on modeling methods designed to synthesize data from multiple sources when the economic analysis needs to go beyond a single source of primary data or for a longer time horizon. Multiple types of economic models are described, including decision trees, state transition models (including Markov chain models), microsimulation, and discrete event simulation. The chapter breaks down key elements of model design and offers recommendations on possible sources of data that may be used to derive parameter estimates. The conclusion of the chapter includes recommendations for appropriately reporting results of the statistical and modeling analyses carried out as part of an economic evaluation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jones AM, Rice N, d’Uva TB, Balia S. Applied health economics. Abingdon: Routledge; 2013.
Ades A, Lu G, Claxton K. Expected value of sample information calculations in medical decision modeling. Med Decis Mak. 2004;24(2):207–27.
Eckermann S, Willan AR. Expected value of information and decision making in HTA. Health Econ. 2007;16(2):195–209.
Al-Janabi H, Peters TJ, Brazier J, Bryan S, Flynn TN, Clemens S, et al. An investigation of the construct validity of the ICECAP-A capability measure. Qual Life Res. 2013;22(7):1831–40.
Coast J, Peters TJ, Natarajan L, Sproston K, Flynn T. An assessment of the construct validity of the descriptive system for the ICECAP capability measure for older people. Qual Life Res. 2008;17(7):967–76.
Malley JN, Towers A-M, Netten AP, Brazier JE, Forder JE, Flynn T. An assessment of the construct validity of the ASCOT measure of social care-related quality of life with older people. Health Qual Life Outcomes. 2012;10(1):1.
Barber J, Thompson S. Multiple regression of cost data: use of generalised linear models. J Health Serv Res Policy. 2004;9(4):197–204.
Buntin MB, Zaslavsky AM. Too much ado about two-part models and transformation?: comparing methods of modeling Medicare expenditures. J Health Econ. 2004;23(3):525–42.
Moran JL, Solomon PJ, Peisach AR, Martin J. New models for old questions: generalized linear models for cost prediction. J Eval Clin Pract. 2007;13(3):381–9.
Akaike H. Information theory and an extension of the maximum likelihood principle. In: Kotz, Samuel, Johnson, Norman L, editors. Breakthroughs in statistics. New York: Springer; 1992. p. 610–24.
Schwarz G. Estimating the dimension of a model. Ann Stat. 1978;6(2):461–4.
Ramsey JB. Tests for specification errors in classical linear least-squares regression analysis. J R Stat Soc Ser B Methodol. 1969:350–71.
Park RE. Estimation with heteroscedastic error terms. Econometrica. 1966;34(4):888.
Jarque CM, Bera AK. A test for normality of observations and regression residuals. Int Stat Rev/Revue Internationale de Statistique. 1987;55:163–72.
Bozdogan H. Model selection and Akaike’s information criterion (AIC): the general theory and its analytical extensions. Psychometrika. 1987;52(3):345–70.
Burnham KP, Anderson DR. Multimodel inference understanding AIC and BIC in model selection. Sociol Methods Res. 2004;33(2):261–304.
Ciarrochi J, Deane FP, Anderson S. Emotional intelligence moderates the relationship between stress and mental health. Personal Individ Differ. 2002;32(2):197–209.
Meltzer H, Bebbington P, Brugha T, Farrell M, Jenkins R. The relationship between personal debt and specific common mental disorders. Eur J Public Health. 2013;23(1):108–13.
Miller CJ, Grogan-Kaylor A, Perron BE, Kilbourne AM, Woltmann E, Bauer MS. Collaborative chronic care models for mental health conditions: cumulative meta-analysis and meta-regression to guide future research and implementation. Med Care. 2013;51(10):922.
Brazier J, Connell J, Papaioannou D, Mukuria C, Mulhern B, Peasgood T, et al. A systematic review, psychometric analysis and qualitative assessment of generic preference-based measures of health in mental health populations and the estimation of mapping functions from widely used specific measures. Health Technol Assess. 2014;18(34).
Lepage R, Billard L. Exploring the limits of bootstrap. New York: John Wiley & Sons; 1992.
Efron B. The jackknife, the bootstrap and other resampling plans: society for industrial and applied mathematics (SIAM). Philadelphia: Society for Industrial and Applied Mathematics; 1982.
Efron B. Better bootstrap confidence intervals. J Am Stat Assoc. 1987;82(397):171–85.
Efron B. Bayesian inference and the parametric bootstrap. Ann Appl Stat. 2012;6(4):1971.
Efron B, Tibshirani RJ. An introduction to the bootstrap. Boca Raton: CRC Press; 1994.
Briggs AH, Wonderling DE, Mooney CZ. Pulling cost-effectiveness analysis up by its bootstraps: a non-parametric approach to confidence interval estimation. Health Econ. 1997;6(4):327–40.
Gomes M, Grieve R, Nixon R, Edmunds W. Statistical methods for cost-effectiveness analyses that use data from cluster randomized trials a systematic review and checklist for critical appraisal. Med Decis Mak. 2012;32(1):209–20.
O’Hagan A, Stevens JW. Assessing and comparing costs: how robust are the bootstrap and methods based on asymptotic normality? Health Econ. 2003;12(1):33–49.
Hunter RM, Baio G, Butt T, Morris S, Round J, Freemantle N. An educational review of the statistical issues in analysing utility data for cost-utility analysis. PharmacoEconomics. 2015;33(4):355–66.
Committee for Medicinal Products for Human Use (CHMP) Guideline on adjustment for baseline covariates. European Medicines Agency. 2013. Available from: http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2013/06/WC500144946.pdf. Accessed 4 Oct 2016.
van Asselt AD, van Mastrigt GA, Dirksen CD, Arntz A, Severens JL, Kessels AG. How to deal with cost differences at baseline. PharmacoEconomics. 2009;27(6):519–28.
Little RJ, Rubin DB. Statistical analysis with missing data. New York: Wiley; 2002.
Raghunathan TE, Lepkowski JM, Van Hoewyk J, Solenberger P. A multivariate technique for multiply imputing missing values using a sequence of regression models. Surv Methodol. 2001;27(1):85–96.
Landerman LR, Land KC, Pieper CF. An empirical evaluation of the predictive mean matching method for imputing missing values. Sociol Methods Res. 1997;26(1):3–33.
White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med. 2011;30(4):377–99.
Longworth L, Rowen D. NICE DSU technical support document 10: the use of mapping methods to estimate health state utility values. Sheffield: Decision Support Unit, ScHARR, University of Sheffield; 2011. p. b4.
Dakin H. Review of studies mapping from quality of life or clinical measures to EQ-5D: an online database. Health Qual Life Outcomes. 2013;11(1):1.
Claxton K, Sculpher M, Drummond M. A rational framework for decision making by the National Institute for Clinical Excellence (NICE). Lancet. 2002;360(9334):711–5.
Stevenson MD, Scope A, Sutcliffe PA, Booth A, Slade P, Parry G, et al. Group cognitive behavioural therapy for postnatal depression: a systematic review of clinical effectiveness, cost-effectiveness and value of information analyses. Health Technol Assess. 2010;14(44):1–107. iii–iv
Siebert U, Alagoz O, Bayoumi AM, Jahn B, Owens DK, Cohen DJ, et al. State-transition modeling: a report of the ISPOR-SMDM modeling good research practices task force-3. Med Decis Mak. 2012;32(5):690–700.
Briggs A, Campbell H, Clarke P. Parametric survival models and decision models: relating continuous hazards to discrete-time transition probabilities. Glasgow: Health Economists’ Study Group; 2004.
Karnon J, Stahl J, Brennan A, Caro JJ, Mar J, Moller J. Modeling using discrete event simulation: a report of the ISPOR-SMDM modeling good research practices task force-4. Med Decis Mak. 2012;32(5):701–11.
Peveler R, Kendrick T, Buxton M, Longworth L, Baldwin D, Moore M, et al. A randomised controlled trial to compare the cost-effectiveness of tricyclic antidepressants, selective serotonin reuptake inhibitors and lofepramine. Health Technol Assess. 2005;9(16):1–134. iii
Centre for Reviews and Dissemination. Available from: http://www.crd.york.ac.uk/CRDWeb/. Accessed 6 Oct 2016.
School of Health and Related Research. Health utility database. Available from: http://www.scharrhud.org/. Accessed 06 Oct 2016.
Bangor University. Database of instruments for resource use measurement. Available from: http://www.dirum.org/. Accessed 06 Oct 2016.
Department of Health. NHS reference costs 2014 to 2015. Available from: https://www.gov.uk/government/publications/nhs-reference-costs-2014-to-2015. Accessed 16 Feb 2016.
Personal Social Services Research Unit. Unit costs of health and social care 2015. Available from: http://www.pssru.ac.uk/project-pages/unit-costs/2015/index.php. Accessed 16 Feb 2016.
British National Formulary. BNF 70. London: British Medical Association & The Royal Pharmaceutical Society; 2015.
Claxton K. Exploring uncertainty in cost-effectiveness analysis. PharmacoEconomics. 2008;26(9):781–98.
Briggs AH, Weinstein MC, Fenwick EA, Karnon J, Sculpher MJ, Paltiel AD, et al. Model parameter estimation and uncertainty analysis: a report of the ISPOR-SMDM modeling good research practices task force working group-6. Med Decis Mak. 2012;32(5):722–32.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Berdunov, V., Franklin, M. (2017). Introduction to Statistics and Modeling Methods Applied in Health Economics. In: Razzouk, D. (eds) Mental Health Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-55266-8_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-55266-8_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55265-1
Online ISBN: 978-3-319-55266-8
eBook Packages: MedicineMedicine (R0)