The challenges and pitfalls of incorporating evidence from cardiovascular outcomes trials in health economic modelling of type 2 diabetes

Abstract

The clinical evidence base for evaluating modern type 2 diabetes interventions has expanded greatly in recent years, with numerous efficacious treatment options available (including dipeptidyl peptidase-4 inhibitors, glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors). The cardiovascular safety of these interventions has been assessed individually versus placebo in numerous cardiovascular outcomes trials (CVOTs), statistically powered to detect differences in a composite endpoint of major adverse cardiovascular events. There have been growing calls to incorporate these data in the long-term modelling of type 2 diabetes interventions because current diabetes models were developed prior to the conduct of the CVOTs and therefore rely on risk equations developed in the absence of these data. However, there are numerous challenges and pitfalls to avoid when using data from CVOTs. The primary concerns are around the heterogeneity of the trials, which have different study durations, inclusion criteria, rescue medication protocols and endpoint definitions; this results in significant uncertainty when comparing two or more interventions evaluated in separate CVOTs, as robust adjustment for these differences is difficult. Analyses using CVOT data inappropriately can dilute clear evidence from head-to-head clinical trials, and blur healthcare decision making. Calibration of existing models may represent an approach to incorporating CVOT data into diabetes modelling, but this can only offer a valid comparison of one intervention versus placebo based on a single CVOT. Ideally, model development should utilize patient-level data from CVOTs to prepare novel risk equations that can better model modern therapies for type 2 diabetes. K E Y W O R D S cardiovascular outcomes trials, cost-effectiveness, health economic modelling, type 2 diabetes 1 | INTRODUCTION Modern interventions for type 2 diabetes are associated with an almost overwhelming amount of clinical data and deciphering the ever-growing evidence base is a challenge. Medication classes including glucagon-like peptide-1 (GLP-1) receptor agonists and sodium-glucose cotransporter-2 (SGLT2) inhibitors, which are associated with reductions in glycated haemoglobin (HbA1c) and body weight, while also having a low risk of hypoglycaemia, have been evaluated versus an array of comparators in the diabetes treatment

Cost-Effectiveness of Newer Antidiabetic Drugs as Second-Line Treatment for Type 2 Diabetes: A Systematic Review

“…First, classical economic evaluation models of diabetes, such as UKPDS, may not meet all requirements because of the unique cardiovascular protective effects of GLP-1RA and SGLT-2i that are independent of their hypoglycemic effects. Therefore, there are growing appeals for encouraging the incorporation of new CVOT data on drug-mediated cardioprotection in the T2D economic model [ 46 , 47 ]. Second, most clinical trial data of hypoglycemic drugs are currently based on the Caucasian population, and there is a lack of data support for the Asian population.…”

Evaluating the Long-Term Cost-Effectiveness of Once-Weekly Semaglutide 1 mg Versus Liraglutide 1.8 mg: A Health Economic Analysis in the UK

“…However, these outcomes are not easily incorporated into current models for type 2 diabetes, which rely on progression and risk equations developed long before modern diabetes therapies were available [ 60 ]. A major pitfall is the potential double counting of benefits, as the mechanisms of action behind the outcomes observed in CVOTs are not currently known and could be in part due to changes in physiological parameters already applied in the analysis [ 57 , 58 ]. Moreover, there is a risk of inappropriate application of hazard ratios derived from populations with high cardiovascular risk or prior cardiovascular disease in more general populations with type 2 diabetes [ 59 , 60 ].…”