Comparative analyses of published cost effectiveness models highlight critical considerations which are useful to inform development of new models. (3rd March 2020)

• Record Type: Journal Article
• Authors: Rautenberg, T. A.; George, G.; Bwana, M. B.; Moosa, M. S.; Pillay, S.; McCluskey, S. M.; Aturinda, I.; Ard, K.; Muyindike, W.; Moodley, P.; Brijkumar, J.; Johnson, B. A.; Gandhi, R. T.; Sunpath, H.; Marconi, V. C.; Siedner, M. J.
• Is Part Of: Journal of medical economics. Volume 23:Number 3(2020)
• Page Start: 221
• Page End: 227
• Publication Date: 2020-03-03
• Subjects: Comparative analysis -- economic evaluation -- cost effectiveness modeling -- health economics methodology; I10; Medical care -- Cost control -- Periodicals; Medical economics -- Periodicals; 362.10941
• DOI: 10.1080/13696998.2019.1705314
• Languages: English
• ISSNs: 1369-6998

Abstract

Abstract: Background: Comparative analyses of published cost effectiveness models provide useful insights into critical issues to inform the development of new cost effectiveness models in the same disease area. Objective: The purpose of this study was to describe a comparative analysis of cost-effectiveness models and highlight the importance of such work in informing development of new models. This research uses genotypic antiretroviral resistance testing after first line treatment failure for Human Immunodeficiency Virus (HIV) as an example. Method: A literature search was performed, and published cost effectiveness models were selected according to predetermined eligibility criteria. A comprehensive comparative analysis was undertaken for all aspects of the models. Results: Five published models were compared, and several critical issues were identified for consideration when developing a new model. These include the comparator, time horizon and scope of the model. In addition, the composite effect of drug resistance prevalence, antiretroviral therapy efficacy, test performance and the proportion of patients switching to second-line ART potentially have a measurable effect on model results. When considering CD4 count and viral load, dichotomizing patients according to higher cost and lower quality of life (AIDS) versus lower cost and higher quality of life (non-AIDS) status will potentially capture differences between resistance testing and other strategies, which could be confirmed by cross-validation/convergent validation. A quality adjusted life year is an essential outcome which should be explicitly explored in probabilistic sensitivity analysis, where possible. Conclusions: Using an example of GART for HIV, this study demonstrates comparative analysis of previously published cost effectiveness models yields critical information which can be used to inform the structure and specifications of new models.