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, treatment intensification with multiple drug combinations and ultimately insulin rescue medication is routinely needed to achieve goals. Assumptions regarding downstream rescue medication can thus drive results. Especially for insulin, where treatment effects and adverse events are known to depend on patient characteristics (e.g. HbA1c lowering tends to be correlated with baseline HbA1c), this can be problematic for health-economic evaluation involving modeling. Given the long-time horizons involved, simulated rescue medication can be as important as the initial comparator agents. The objective was 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.
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.
Data from 91 studies featuring 171 usable study arms were identified, mostly for pre-mix and basal insulin types. Multivariate prediction equations for HbA1c 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 hypoglycemia events were also estimated, though considerable heterogeneity in definitions limits their usefulness. Using these in economic simulation modeling in T2DM can improve realism and flexibility in modeling insulin rescue medication.
Value in Health, 2017 Mar; 20 (3): 357-371
Published online: December 2016