Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
The European Journal of Health Economics
Published in: The European Journal of Health Economics 8/2016

01-11-2016 | Original Paper

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

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

Published in: The European Journal of Health Economics | Issue 8/2016

Login to get access

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.
Appendix
Available only for authorised users
Footnotes
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].
 
Literature
1.
2.
Drummond, M.F., Sculpher, M.J., Torrance, G.W., O’Brien, B.J., Stoddart, G.L.: Methods for the economic evaluation of health care programmes (3rd edn). Oxford University Press, Oxford (2005)
3.
Senn, S.J.: Covariate imbalance and random allocation in clinical trials. J. Stat. Med. 8, 467–475 (1989)CrossRef
4.
Moher, D., Hopewell, S., Schulz, K.F., Montori, V., Gøtzsche, P.C., Devereaux, P.J., Elbourne, P.J., Egger, D.G., Altman, M.: CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials. Br. Med. J. 340, 869–897 (2010)CrossRef
5.
6.
Taves, D.R.: Minimization: a new method of assigning patients to treatment and control groups. Clin. Pharmacol. Ther. 15(5), 443–453 (1974)CrossRefPubMed
7.
Pocock, S.J., Simon, R.: Sequential treatment assignment with balancing for prognostic factors in the controlled clinical trial. Biometrics 31(1), 103–115 (1975)CrossRefPubMed
8.
Senn, S.J.: Testing for baseline balance in clinical trials. J. Stat. Med. 13, 1715–1726 (1994)CrossRef
9.
Hoch, J., Briggs, A., Willan, A.: Something old, something new, something borrowed, something blue: a framework for the marriage of health econometrics and cost-effectiveness analysis. Health Econ. 11, 415–430 (2002)CrossRefPubMed
10.
Manca, A., Hawkins, N., Sculpher, M.: Estimating mean QALYs in trial-based cost-effectiveness analysis: the importance of controlling for baseline utility. Health Econ. 14, 487–496 (2005)CrossRefPubMed
11.
Willan, A., Briggs, A.: Statistical analysis of cost-effectiveness data. Wiley, Chichester (2006)CrossRef
12.
13.
Nixon, R.M., Thompson, S.G.: Methods for incorporating covariate adjustment, subgroup analysis and between-centre differences into cost-effectiveness evaluations. Health Econ. 14, 1217–1229 (2005)CrossRefPubMed
14.
O’Hagan, A., Stevens, J.W.: A framework for cost-effectiveness analysis from clinical trial data. Health Econ. 10, 303–315 (2001)CrossRefPubMed
15.
Briggs, A., Gray, A.: The distribution of health care costs and their statistical analysis for economic evaluation. J. Health Serv. Res. Policy 3, 233–245 (1998)PubMed
16.
Tambour, M., Zethraeus, N., Johannesson, M.: A note on confidence intervals in cost-effectiveness analysis. Int. J. Technol. Assess. Health Care 14(3), 467–471 (1998)CrossRefPubMed
17.
Willan, A.R., Briggs, A.H., Hoch, J.S.: Regression methods for covariate adjustment and subgroup analysis for non-censored cost-effectiveness data. Health Econ. 13, 461–475 (2004)CrossRefPubMed
18.
Greene, W.H.: (2002) Econometric analysis (5th ed.). Prentice Hall, New Jersey (2002)
19.
Vazquez-Polo, F.J., Negrin, M., Gonzalez, B.: Using covariates to reduce uncertainty in the economic evaluation of clinical trial data. Health Econ. 14, 545–557 (2005)CrossRefPubMed
20.
Briggs, A.H.: A Bayesian approach to stochastic cost-effectiveness analysis. Health Econ. 8, 257–261 (1999)CrossRefPubMed
21.
Chaloner, K., Rhame, F.S.: Quantifying and documenting prior beliefs in clinical trials. Stat. Med. 20, 581–600 (2001)CrossRefPubMed
22.
O’Hagan, A., Stevens, J.W.: Bayesian assessment of sample size for clinical trials of cost-effectiveness. Med. Decis. Making 21, 219–230 (2001)CrossRefPubMed
23.
Thompson, S.G., Barber, J.A.: How should cost data in pragmatic randomised trials be analysed? Br. Med. J. 320(7243), 1197–1200 (2000)CrossRef
24.
Nixon, R.M., Wonderling, D., Grieve, R.D.: Non-parametric methods for cost-effectiveness analysis: the central limit theorem and the bootstrap compared. Health Econ. 19(3), 316–333 (2010)CrossRefPubMed
25.
Manning, W.G., Mullahy, J.: Estimating log models: to transform or not to transform? J. Health Econ. 20(4), 461–494 (2001)CrossRefPubMed
26.
O’Hagan, A., Stevens, J.W.: Assessing and comparing costs: how robust are the bootstrap and methods based on asymptotic normality? Health Econ. 12, 33–49 (2003)CrossRefPubMed
27.
Basu, A.: Extended generalised linear models: simultaneous estimation of flexible link and variance functions. Stata J. 5(4), 501–516 (2005)
28.
Mihaylova, B., Briggs, A., O’Hagan, A., Thompson, S.G.: Review of statistical methods for analysing healthcare resources and costs. Health Econ. 20(8), 897–916 (2011)CrossRefPubMed
29.
Moreno, E., Giron, F.J., Vazquez-Polo, F.J., Negrin, M.A.: Optimal healthcare decisions: the importance of the covariates in cost-effectiveness analysis. Eur. J. Oper. Res. 218, 512–522 (2012)CrossRef
30.
McManus, R.J., Mant, J., Bray, E.P., Holder, R., Jones, M.I., Greenfield, S., Kaambwa, B., Banting, M., Bryan, S., Little, P., Williams, B., Hobbs, F.D.R.: Telemonitoring and self-management in the control of hypertension (TASMINH2): a randomised controlled trial. The Lancet. 376(9736), 163-172 (2010)
31.
The EuroQol Group: EuroQol—a new facility for the measurement of health-related quality of life. Health Policy 16(3), 199–208 (1990)CrossRef
32.
33.
Billingham L.J., Abrams, K.R., Jones, D.R.: Methods for the analysis of quality-of-life and survival data in health technology assessment. Health Technology Assessment. 3(10) (1999)
34.
35.
Zellner, A.: An efficient method of estimating seemingly unrelated regression equations and tests for aggregation bias. J. Am. Stat. Assoc. 57, 348–368 (1962)CrossRef
36.
McCullough, P., Nelder, J.A.: Generalized linear models, 2nd edn. Chapman & Hall, London (1989)
37.
Anderson, D.R.: Model based inference in the life sciences. Springer, New York (2008)
38.
Stata: Stata programming reference manual, version 11. StataCorp, Texas (2009)
39.
Spiegelhalter, D.J., Thomas, A., Best, N.: WinBUGS, version 1.2. MRC Biostatistics Unit, Cambridge (1999)
40.
Gomes, M., Grieve, R., Nixon, R., Edmond, S., Carpenter, J., Thompson, S.G.: Methods for covariate adjustment in cost-effectiveness analysis that use cluster randomised trials. Health Econ. 21, 1101–1118 (2012)CrossRefPubMed
41.
Austin, P.C.: Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat. Med. 28, 3083–3107 (2009)CrossRefPubMedPubMedCentral
42.
Rosenbaum, P.R., Rubin, D.B.: Constructing a control-group using multivariate matched sampling methods that incorporate the propensity score. Am. Stat. 39, 33–38 (1985)
43.
Altman, D.G.: Comparability of randomised groups. Statistician 34(1), 125–136 (1985)CrossRef
44.
Zhou, X.H.: Inferences about population means of health care costs. Stat. Methods Med. Res. 11, 327–339 (2002)CrossRefPubMed
45.
Nixon, R.M., Thompson, S.G.: Parametric modelling of cost data in medical studies. Stat. Med. 23, 1311–1331 (2004)CrossRefPubMed
46.
Nixon, R.M., Wonderling, D., Grieve, R.D.: Non-parametric methods for cost-effectiveness analysis: the central limit theorem and the bootstrap compared. Health Econ. 19, 316–333 (2010)CrossRefPubMed
47.
Cantoni, E., Ronchetti, E.: A robust approach for skewed and heavy-tailed outcomes in the analysis of health care expenditures. J. Health Econ. 25(2), 198–213 (2006)CrossRefPubMed
48.
Basu, A., Manca, A.: Regression estimators for generic health-related quality of life and quality-adjusted life years. Med. Decis. Making 32, 56–69 (2012)CrossRefPubMed
49.
Kieschnick, R., McCullough, B.D.: Regression analysis of variates observed on (0,1): percentages, proportions and fractions. Stat. Model. 3, 193–213 (2003)CrossRef
50.
Smithson, M., Verkuilen, J.: A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychol. Methods 11(1), 54–71 (2006)CrossRefPubMed
51.
Higgins, J.P.T., Green, S. (eds.) Cochrane handbook for systematic reviews of interventions version 5.1.0 [updated March 2011]. The Cochrane Collaboration, 2011. Available from www.​cochrane-handbook.​org
52.
Welton, N.J., Sutton, A.J., Cooper, N.J., Abrams, K.R., Ades, A.E.: Evidence synthesis for decision making in healthcare. John Wiley: Chichester, UK (2012)
53.
Bernardo, J., Smith, A.F.M.: Bayesian theory. John Wiley: New York. (1994)
54.
55.
Cook, T.D., DeMets, D.L.: Introduction to statistical methods for clinical trials. Chapman & Hall/CRC, Boca Raton (2008)
56.
Pocock, S.J., Assmann, S.E., Enos, L.E., Kasten, L.E.: Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial reporting: current practice and problems. Stat. Med. 21, 2917–2930 (2002)CrossRefPubMed
57.
Sun, X., Ioannidis, J.P., Agoritsas, A., Alba, A.C., Guyatt, G.: How to use a subgroup analysis. Users’ guides to the medical literature. JAMA 311(4), 405–411 (2014)CrossRefPubMed
58.
Sculpher, M.J.: Heterogeneity in cost-effectiveness analysis. Pharmacoeconomics 26(9), 799–806 (2008)CrossRefPubMed
59.
Kaambwa, B., Bryan, S., Jowett, S., Mant, J., Bray, E., P., Hobbs, R.F.D., Holder, R., Jones, M.I., Little, P., Williams, B., McManus, R.J.: Telemonitoring and self-management in the control of hypertension (TASMINH2): a cost-effectiveness analysis. European Journal of Preventive Cardiology. 0(00), 1–14 (2013)
60.
Herdman, M., Gudex, C., Lloyd, A.: Development and preliminary testing of the new five level version of EQ-5D (EQ-5D-5L). Qual. Life Res. 20(10), 1727–1736 (2011)CrossRefPubMedPubMedCentral
Metadata
Title
Choice of statistical model for cost-effectiveness analysis and covariate adjustment: empirical application of prominent models and assessment of their results
Authors
Theodoros Mantopoulos
Paul M. Mitchell
Nicky J. Welton
Richard McManus
Lazaros Andronis
Publication date
01-11-2016
Publisher
Springer Berlin Heidelberg
Published in
The European Journal of Health Economics / Issue 8/2016
Print ISSN: 1618-7598
Electronic ISSN: 1618-7601
DOI
https://doi.org/10.1007/s10198-015-0731-8

Other articles of this Issue 8/2016

The European Journal of Health Economics 8/2016 Go to the issue

Original Paper

Direct cost of pars plana vitrectomy for the treatment of macular hole, epiretinal membrane and vitreomacular traction: a bottom-up approach

Original Paper

The simultaneous effects of pharmaceutical policies from payers’ and patients’ perspectives: Italy as a case study

Original Paper

The time horizon matters: results of an exploratory study varying the timeframe in time trade-off and standard gamble utility elicitation

Original Paper

Modeling the costs and long-term health benefits of screening the general population for risks of cardiovascular disease: a review of methods used in the literature

Original Paper

Is higher nursing home quality more costly?

Editorial

Reimbursable drug classes and ceilings in Italy: why not only one?