IDegLira Versus Insulin Glargine U100: A Long-term Cost-effectiveness Analysis in the US Setting

Baseline risk factors and patient characteristics were based on all patients included in the DUAL V study, with mean baseline cohort characteristics shown in Table 1 [12]. DUAL V was a phase 3, multinational, 26-week, open-label study comparing the safety and efficacy of IDegLira and insulin glargine U100 in patients with T2DM with HbA1c of 7–10% who were receiving stable doses of insulin glargine U100 and metformin. In total, 557 patients were randomly allocated to the two treatment arms in a 1:1 ratio. Of these, 39 were residing in the USA with characteristics well matched to participants from other countries. The primary endpoint was change from baseline in HbA1c at 26 weeks, with secondary endpoints of change from baseline in body weight at 26 weeks and number of treatment-emergent hypoglycemic episodes during 26 weeks. IDegLira was initiated at a starting dose of 16 dose steps and titrated to a maximum of 50 dose steps, while doses of insulin glargine were titrated from a mean starting dose of 32 IU with no maximum dose. Approximately 40% of patients in the IDegLira arm received the maximum 50 dose steps after 26 weeks of treatment. Of these patients, 68% achieved an HbA1c level less than 7% compared with 74% of those who used less than the maximum allowed IDegLira dose.

Treatment effects applied in the first year of the analysis were based on the DUAL V study, with inputs taken from the 26-week end of trial data (Table 2) [12]. IDegLira was associated with statistically significant improvements in glycated hemoglobin (HbA1c), systolic blood pressure, total cholesterol, body mass index (BMI), and non-severe hypoglycemic events. It should be noted that HbA1c changes presented for each treatment arm by Lingvay et al. are not adjusted for differences between the IDegLira and insulin glargine arms at baseline, but the estimated treatment difference presented is adjusted for these differences [12]. The present analysis used the adjusted values for each treatment arm, reflecting the estimated treatment difference between the arms presented by Lingvay et al. Patients were assumed to receive IDegLira or insulin glargine U100 for 5 years, before intensifying therapy to basal bolus insulin (insulin glargine U100 plus three times daily insulin aspart). During the period in which patients received initial treatments, HbA1c remained constant, as this is in line with long-term studies of patients with T2DM receiving insulin [13‐17]. After intensification at 5 years, HbA1c, BMI, hypoglycemic event rates, and annual costs of T2DM interventions were the same in both treatment arms, with immediate abolition of relative treatment effects representing a conservative modelling approach. Alternative approaches to treatment switching and long-term parameter progression were evaluated in sensitivity analyses.

To capture the impact of diabetes-related complications on quality of life, utilities were taken from published sources (Table 3). Doses of IDegLira and insulin glargine U100 used to calculate annual costs of treatment were taken from the DUAL V trial at 26 weeks based on wholesale acquisition costs (Table 4) [12, 18]. Costs of self-monitoring of blood glucose (SMBG) testing were based on an analysis of insurance claims in the USA [19]. Cost of diabetes-related complications were taken from US-specific sources and inflated to 2015 values if necessary using the consumer price index for health (Table 5) [19].

A number of sensitivity analyses were conducted to identify key drivers of outcomes in the base case analysis and the robustness of this. The influence of time horizon on the model outcomes was assessed by running the analysis over 20 and 10 years. It should be noted that these analyses do not capture all late-stage, long-term outcomes, as some patients were still alive at the end of these simulations. The effect of discount rates on future costs and clinical outcomes were investigated through analyses in which they were set (symmetrically) to 0% and 6% per annum. The key drivers of clinical outcomes were assessed by abolishing the differences in individual clinical parameters between the IDegLira arm and the insulin glargine U100 arm in turn. An additional analysis with only the statistically significant differences between IDegLira and insulin glargine U100 was conducted (Table 2).

Two alternative approaches to HbA1c progression were explored (see electronic supplementary material). In the first, no HbA1c changes were applied following the treatment effects applied in the first year of the analysis. This attempts to capture the legacy effect, where an early improvement in HbA1c has a benefit in the later years of life, even if the HbA1c difference no longer persists. In the second, the United Kingdom Prospective Diabetes Study (UKPDS) HbA1c progression equation was applied in both arms of the simulation. HbA1c increases over time in both arms of the analysis, with the HbA1c benefit in the IDegLira arm gradually reduced. Analyses were run with the upper and lower 95% confidence interval of the HbA1c change seen in the IDegLira arm of DUAL V applied, with all other parameters in the IDegLira and insulin glargine U100 arms remaining unchanged. The base case analyses assumed that the BMI difference between the treatment arms was abolished on treatment switching, and an alternative to this was explored in a sensitivity analysis with the difference maintained for the duration of the analysis. The influence of treatment switching was assessed in analyses with treatment switching brought forward to 3 years in both arms, pushed back to 7 years in both arms, and no treatment switching.

The effect of over- or underestimating the direct cost of treating diabetes-related complications was investigated in two scenarios. In the first, the cost of treating complications was increased by 10%, and in the second the cost was reduced by 10%. The impact of applying alternative disutilities for severe and non-severe hypoglycemic events was assessed by using the values published by Currie et al. (−0.0118 per severe hypoglycemic event and −0.0035 per non-severe hypoglycemic event) [20]. A scenario was investigated in which 28% of patients in the insulin glargine U100 arm were assumed to require twice-daily insulin, incurring the cost of a further needle for subcutaneous injection, based on a 5-year parallel group study of insulin glargine U100 versus neutral protamine Hagedorn (NPH) insulin [21]. To investigate the impact of consumables on cost-effectiveness outcomes, a scenario was evaluated with costs of needles and SMBG testing excluded. The effect of the cost of basal insulin was investigated in an analysis with the cost of insulin glargine U100 replaced with the cost of NPH insulin.

In February 2014, an update to the IMS CORE Diabetes Model incorporating data from the UKPDS 82 was released, and an analysis using this version of the model has been conducted. While a validation study of the revised model has been published, the model proprietors suggest that the update is used in a sensitivity analysis, with the previous version being used in the base case [22]. Similarly, version 9.0 of the IMS CORE Diabetes Model was released in summer 2015, and this was used in a sensitivity analysis. To date, no validation studies or user guide for the updated version of the model have been released and therefore it was not considered appropriate to use version 9.0 of the model for the base case analysis.

Probabilistic sensitivity analysis (PSA) was performed using the predefined function in the IMS CORE Diabetes Model. Cohort characteristics, treatment effects, complication costs, and utilities were sampled from distributions and the simulation was run using a second-order Monte Carlo approach. Cohorts of 1000 patients were run through the model 1000 times for the PSA, as results were not subject to random statistical variation with these settings.

In line with good practice guidance for economic evaluation of T2DM interventions, outcomes were projected over patient lifetimes to capture all relevant long-term complications and associated costs, and assess their impact on life expectancy and quality-adjusted life expectancy [23]. Future clinical benefits and costs were discounted at 3% annually, based on health economic guidance for the USA [24]. In the USA, cost-effectiveness thresholds have been defined by the Institute for Clinical and Economic Review, with incremental cost-effectiveness ratios (ICERs) under $100,000 per QALY gained described as high care value, ICERs in the range $100,000–150,000 per QALY gained described as intermediate care value, and ICERs over $150,000 described as low care value. The Institute for Clinical and Economic Review is a non-profit organization that is becoming increasingly influential in healthcare decision-making in the USA [25].

The base case analysis was performed using version 8.5+ of the IMS CORE Diabetes Model (IMS Health, Basel, Switzerland), a validated, non-product-specific diabetes policy analysis tool (version 9.0 was used in a sensitivity analysis) [26]. The model is based on a series of interdependent submodels that simulate the complications of diabetes (angina, myocardial infarction, congestive heart failure, stroke, peripheral vascular disease, diabetic retinopathy, macular edema, cataract, hypoglycemia, ketoacidosis, lactic acidosis, nephropathy and end-stage renal disease, neuropathy, foot ulcer and amputation, and non-specific mortality). Each submodel has a semi-Markov structure and uses time, state, time-in-state, and diabetes type-dependent probabilities derived from published sources. Monte Carlo simulation using tracker variables overcomes the memory-less properties of the standard Markov model, and allows interconnectivity and interaction between individual complication submodels. Long-term outcomes projected by the model have been validated against real-life data in 2004 and more recently in 2014 [22, 27].

This article does not contain any new studies with human or animal subjects performed by any of the authors.