On the censored cost-effectiveness analysis using copula information. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Fontaine, Charles; Daurès, Jean-Pierre; Landais, Paul
• Is Part Of: BMC medical research methodology. Volume 17:Issue 1(2017)
• Page Start: 1
• Page End: 15
• Publication Date: 2017-12
• Subjects: Cost-effectiveness analysis -- Censored data -- Copulas -- Parametric models -- Subgroups analysis; Medicine -- Research -- Methodology -- Periodicals; 610.72
• DOI: 10.1186/s12874-017-0305-9
• Languages: English
• ISSNs: 1471-2288

Abstract

Abstract Background Information and theory beyond copula concepts are essential to understand the dependence relationship between several marginal covariates distributions. In a therapeutic trial data scheme, most of the time, censoring occurs. That could lead to a biased interpretation of the dependence relationship between marginal distributions. Furthermore, it could result in a biased inference of the joint probability distribution function. A particular case is the cost-effectiveness analysis (CEA), which has shown its utility in many medico-economic studies and where censoring often occurs. Methods This paper discusses a copula-based modeling of the joint density and an estimation method of the costs, and quality adjusted life years (QALY) in a cost-effectiveness analysis in case of censoring. This method is not based on any linearity assumption on the inferred variables, but on a punctual estimation obtained from the marginal distributions together with their dependence link. Results Our results show that the proposed methodology keeps only the bias resulting statistical inference and don't have anymore a bias based on a unverified linearity assumption. An acupuncture study for chronic headache in primary care was used to show the applicability of the method and the obtained ICER keeps in the confidence interval of the standard regression methodology. Conclusion For the cost-effectiveness literature, such a technique without any linearity assumption is a progress since it does not need the specification of a global linear regression model. Hence, the estimation of the a marginal distributions for each therapeutic arm, the concordance measures between these populations and the right copulas families is now sufficient to process to the whole CEA.