BACKGROUND: Type 2 diabetes mellitus (T2DM) is chronic and progressive and the cost-effectiveness of new treatment interventions must be established over long time horizons. Given the limited durability of drugs, assumptions regarding downstream rescue medication can drive results. Especially for insulin, for which treatment effects and adverse events are known to depend on patient characteristics, this can be problematic for health economic evaluation involving modeling. OBJECTIVES: To estimate parsimonious multivariate equations of treatment effects and hypoglycemic event risks for use in parameterizing insulin rescue therapy in model-based cost-effectiveness analysis. METHODS: Clinical evidence for insulin use in T2DM was identified in PubMed and from published reviews and meta-analyses. Study and patient characteristics and treatment effects and adverse event rates were extracted and the data used to estimate parsimonious treatment effect and hypoglycemic event risk equations using multivariate regression analysis. RESULTS: Data from 91 studies featuring 171 usable study arms were identified, mostly for premix and basal insulin types. Multivariate prediction equations for glycated hemoglobin A1c lowering and weight change were estimated separately for insulin-naive and insulin-experienced patients. Goodness of fit (R2) for both outcomes were generally good, ranging from 0.44 to 0.84. Multivariate prediction equations for symptomatic, nocturnal, and severe hypoglycemic events were also estimated, though considerable heterogeneity in definitions limits their usefulness. CONCLUSIONS: Parsimonious and robust multivariate prediction equations were estimated for glycated hemoglobin A1c and weight change, separately for insulin-naive and insulin-experienced patients. Using these in economic simulation modeling in T2DM can improve realism and flexibility in modeling insulin rescue medication.
BACKGROUND:Type 2 diabetes mellitus (T2DM) is chronic and progressive and the cost-effectiveness of new treatment interventions must be established over long time horizons. Given the limited durability of drugs, assumptions regarding downstream rescue medication can drive results. Especially for insulin, for which treatment effects and adverse events are known to depend on patient characteristics, this can be problematic for health economic evaluation involving modeling. OBJECTIVES: To estimate parsimonious multivariate equations of treatment effects and hypoglycemic event risks for use in parameterizing insulin rescue therapy in model-based cost-effectiveness analysis. METHODS: Clinical evidence for insulin use in T2DM was identified in PubMed and from published reviews and meta-analyses. Study and patient characteristics and treatment effects and adverse event rates were extracted and the data used to estimate parsimonious treatment effect and hypoglycemic event risk equations using multivariate regression analysis. RESULTS: Data from 91 studies featuring 171 usable study arms were identified, mostly for premix and basal insulin types. Multivariate prediction equations for glycated hemoglobin A1c lowering and weight change were estimated separately for insulin-naive and insulin-experienced patients. Goodness of fit (R2) for both outcomes were generally good, ranging from 0.44 to 0.84. Multivariate prediction equations for symptomatic, nocturnal, and severe hypoglycemic events were also estimated, though considerable heterogeneity in definitions limits their usefulness. CONCLUSIONS: Parsimonious and robust multivariate prediction equations were estimated for glycated hemoglobin A1c and weight change, separately for insulin-naive and insulin-experienced patients. Using these in economic simulation modeling in T2DM can improve realism and flexibility in modeling insulin rescue medication.
Authors: Chinmay Dwibedi; Emelia Mellergård; Amaru Cuba Gyllensten; Kristoffer Nilsson; Annika S Axelsson; Malin Bäckman; Magnus Sahlgren; Stephen H Friend; Sofie Persson; Stefan Franzén; Birgitta Abrahamsson; Katarina Steen Carlsson; Anders H Rosengren Journal: NPJ Digit Med Date: 2022-05-11
Authors: Sara Stafford; Peter G Bech; Adam Fridhammar; Nino Miresashvili; Andreas Nilsson; Michael Willis; Aiden Liu Journal: Appl Health Econ Health Policy Date: 2022-03-28 Impact factor: 3.686
Authors: Stephen C Bain; Brian B Hansen; Samuel J P Malkin; Solomon Nuhoho; William J Valentine; Barrie Chubb; Barnaby Hunt; Matthew Capehorn Journal: Diabetes Ther Date: 2019-12-12 Impact factor: 2.945