Martin Hoyle1, Rob Anderson. 1. Peninsula Technology Assessment Group (PenTAG), Peninsula Medical School, University of Exeter, Exeter, United Kingdom. martin.hoyle@pms.ac.uk
Abstract
BACKGROUND: Most health technology economic evaluations simulate only the prevalent cohort or the next incident cohort of patients. They therefore do not capture all future patient-related benefits and costs. OBJECTIVE: We show how to estimate and aggregate the incremental cost-effectiveness ratios (ICERs) for both currently eligible (prevalent) and future (incident) patient cohorts within the same model-based analysis. We show why, and in what circumstances, the prevalent and incident cohort ICERs are likely to differ. METHODS: Algebraic expressions were developed to capture all components of the ICER in hypothetical cohorts of all prevalent patients and future incident patients. Numerical examples are used to illustrate the approach. RESULTS: The ICER for the first (i.e., next) incident cohort is equivalent to the ICER for all future incident cohorts only when the discount rates for costs and benefits are the same; otherwise, when the discount rate for benefits is lower than for costs, the ICER for all future incident cohorts is lower than the ICER for the first incident cohort. Separate simulation of prevalent and incident patients treated for a hypothetical progressive chronic disease shows widely different ICERs according to which patient cohorts were included when the discount rates were equal. CONCLUSIONS: In many circumstances, both the prevalent cohort and all future incident cohorts should be modeled. The need for this approach will depend on the likely difference in the ICERs for prevalent and incident patients, the relative size of the 2 types of cohort, and whether costs and benefits are discounted at equal rates.
BACKGROUND: Most health technology economic evaluations simulate only the prevalent cohort or the next incident cohort of patients. They therefore do not capture all future patient-related benefits and costs. OBJECTIVE: We show how to estimate and aggregate the incremental cost-effectiveness ratios (ICERs) for both currently eligible (prevalent) and future (incident) patient cohorts within the same model-based analysis. We show why, and in what circumstances, the prevalent and incident cohort ICERs are likely to differ. METHODS: Algebraic expressions were developed to capture all components of the ICER in hypothetical cohorts of all prevalent patients and future incident patients. Numerical examples are used to illustrate the approach. RESULTS: The ICER for the first (i.e., next) incident cohort is equivalent to the ICER for all future incident cohorts only when the discount rates for costs and benefits are the same; otherwise, when the discount rate for benefits is lower than for costs, the ICER for all future incident cohorts is lower than the ICER for the first incident cohort. Separate simulation of prevalent and incident patients treated for a hypothetical progressive chronic disease shows widely different ICERs according to which patient cohorts were included when the discount rates were equal. CONCLUSIONS: In many circumstances, both the prevalent cohort and all future incident cohorts should be modeled. The need for this approach will depend on the likely difference in the ICERs for prevalent and incident patients, the relative size of the 2 types of cohort, and whether costs and benefits are discounted at equal rates.
Authors: James F O'Mahony; Joost van Rosmalen; Ann G Zauber; Marjolein van Ballegooijen Journal: Med Decis Making Date: 2012-08-27 Impact factor: 2.583
Authors: Arantzazu Arrospide; Montserrat Rue; Nicolien T van Ravesteyn; Merce Comas; Myriam Soto-Gordoa; Garbiñe Sarriugarte; Javier Mar Journal: BMC Cancer Date: 2016-06-01 Impact factor: 4.430
Authors: Sophie Whyte; Simon Dixon; Rita Faria; Simon Walker; Stephen Palmer; Mark Sculpher; Stefanie Radford Journal: Value Health Date: 2016-03-07 Impact factor: 5.725
Authors: Arantzazu Arrospide; Montserrat Rue; Nicolien T van Ravesteyn; Merce Comas; Nerea Larrañaga; Garbiñe Sarriugarte; Javier Mar Journal: BMC Cancer Date: 2015-10-12 Impact factor: 4.430