The aim of this article was to perform a detailed methodological review of models used to estimate the cost effectiveness of drug treatment regimens for HIV infection in Europe and North America and assess the relationship between the different modeling approaches or key structural assumptions and the results. Electronic searches in three databases (MEDLINE, EMBASE, and the Cochrane Library) identified the cost-effectiveness models. Modeling approaches and structural assumptions were abstracted for all models. For three case studies of multiple analyses that compared the cost effectiveness of two drug regimens using the same clinical data inputs, differences in results were compared with differences in modeling approaches and structural assumptions. Forty-one model publications were reviewed. Recent models included Monte Carlo simulations, Markov models, or discrete-event simulation models, all including multiple lines of therapy and capturing the long-expected duration of efficacy of highly active antiretroviral therapy. In the three case studies, assumptions about the duration of efficacy after the trial time period, whether differences between the two regimens persist after the trial time period, the sequence of regimens after initial regimen failure, and the cost and utility assigned to adverse events, but not the modeling approach, were the most important factors in explaining differences in the results. As the models and treatment pathways get more complex, models should be validated using clinical trial data and local observational databases. The results of sensitivity analyses testing the impact of the structural assumptions that might change the results as identified in this review should also be presented in modeling papers.
A methodological review of models used to estimate the cost effectiveness of antiretroviral regimens for the treatment of HIV infection
Mauskopf, J. (2013). A methodological review of models used to estimate the cost effectiveness of antiretroviral regimens for the treatment of HIV infection. PharmacoEconomics, 31(11), 1031-1050. https://doi.org/10.1007/s40273-013-0098-6