Cost Effectiveness Statistic: A Proposal to Take Into Account the Patient Stratification Factors

Ciro D'Urso^1,

¹ICT Department, Italian Senate, LUMSA University, Rome, Italy

Cite this paper

Ciro D'Urso. Cost Effectiveness Statistic: A Proposal to Take Into Account the Patient Stratification Factors. American Journal of Applied Mathematics and Statistics. 2013; 1(5):87-89. doi: 10.12691/AJAMS-1-5-2

Abstract

The formula here proposed can be used to conduct economic analysis in randomized clinical trials. It is based on a statistical approach and aims at calculating a revised version of the incremental cost-effective ratio (ICER) in order to take into account the key factors that can influence the choice of therapy causing confounding by indication. Let us take as an example a new therapy to treat cancer being compared to an existing therapy with effectiveness taken as time to death. A challenging problem is that the ICER is defined in terms of means over the entire treatment groups. It makes no provision for stratification by groups of patients with differing risk of death. For example, for a fair and unbiased analysis, one would desire to compare time to death in groups with similar life expectancy which would be impacted by factors such as age, gender, disease severity, etc. The method we decided to apply is borrowed by cluster analysis and aims at (i) discard any outliers in the set under analysis that may arise, (ii) identify groups (i.e. clusters) of patients with "similar" key factors.

Keywords

ICER, cost effectiveness analysis, cluster analysis, outlier identification

Copyright

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References

[1]	Drummond MF. Principles of economics appraisal in health care. Oxford University Press, 1980.
[2]	Sheldon R, et alt.. Effect of clinic risk stratification on cost-effectiveness of the implantable cardioverter-defibrillator: the canadian implantable defibrillator study. Circulation, 2001, 104:1622-1626.
[3]	Campbell MK and Torgenson DJ. Bootstrapping: estimating confidence intervals for cost effectiveness ratios. Q J Med, 1999; 92:177-182.
[4]	Abadie A and Imbens GW. On the Failure of the Bootstrap for Matching Estimators. Econometrica, 2008, Vol. 76, No. 6, pp. 1537-1557.
[5]	P. Davies and U. Gather, "The identification of multiple outliers," In Journal of the American Statistical Association, 88, 1993, p. 782-801.
[6]	John Dunagan, Santosh Vempala: "Optimal outlier removal in high-dimensional spaces". J. Comput. Syst. Sci. 68(2): 335-373, 2004.
[7]	M. Riani, S. Zani, "An iterative method for the detection of multivariate outliers", Metron, Vol.55, pp. 101-117, (1997).
[8]	Ester M, Kriegel HP, Sander J, Xiaowei Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Evangelos Simoudis, Jiawei Han, Usama M. Fayyad. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining 1996. AAAI Press. pp. 226-231.
[9]	http://www.gnu.org/software/octave/.
[10]	D. Polsky, H.A. Glick, et al., "Confidence intervals for cost-effectiveness ratios: a comparison of four methods", Health Economics, Vol.6, pp 243-252, 1997.