PharmacoEconomics DOI 10.1007/s40273-016-0433-9

ORIGINAL RESEARCH ARTICLE

Cost Effectiveness of IDegLira vs. Alternative Basal Insulin Intensification Therapies in Patients with Type 2 Diabetes Mellitus Uncontrolled on Basal Insulin in a UK Setting Melanie J. Davies1 • Divina Glah2 • Barrie Chubb2 • Gerasimos Konidaris3 Phil McEwan4

•

Ó Springer International Publishing Switzerland 2016

Abstract Objectives Once-daily insulin degludec/liraglutide (IDegLira) is the first basal insulin and glucagon like peptide-1 receptor agonist combined in one delivery device. Our aim was to investigate the cost effectiveness of IDegLira vs. basal insulin intensification therapies for patients with type 2 diabetes mellitus uncontrolled on basal insulin (glycosylated haemoglobin; HbA1c [7.5 %; 58 mmol/mol) in a UK setting. Research Design and Methods Baseline cohort and clinical parameters were sourced from a pooled analysis comparing IDegLira with basal insulin plus liraglutide and basal-bolus therapy, and from the DUALTM V trial comparing IDegLira with up-titrated insulin glargine (IGlar; LantusÒ). The CORE Diabetes Model simulated lifetime costs and outcomes with IDegLira vs. these comparators from a UK healthcare payers’ perspective. All costs were expressed in 2015 GBP. Sensitivity analyses were performed to assess the impact of key parameters in the model. Results Treatment with IDegLira resulted in mean increases in quality-adjusted life-years (QALYs) of 0.12, 0.41 and 0.24 Electronic supplementary material The online version of this article (doi:10.1007/s40273-016-0433-9) contains supplementary material, which is available to authorized users. & Divina Glah [email protected]

vs. basal insulin plus liraglutide, basal-bolus therapy and uptitrated IGlar, respectively. IDegLira was associated with lower costs of £971 and £1698 vs. basal insulin plus liraglutide and basal-bolus therapy, respectively, and increased costs of £1441 vs. up-titrated IGlar. IDegLira was dominant, i.e., both more effective and less costly vs. basal insulin plus liraglutide and basal-bolus therapy, and highly cost effective vs. up-titrated IGlar with an incremental costeffectiveness ratio of £6090/QALY gained. Conclusions Once-daily IDegLira may be considered a cost-effective treatment option for prescribers, to improve glycaemic control for type 2 diabetes patients uncontrolled on basal insulin without an increased risk of hypoglycaemia or weight gain, and without adding to their injection burden. Key Points for Decision Makers IDegLira provides a simple and cost-effective treatment option vs. current insulin intensification options for patients with type 2 diabetes mellitus uncontrolled on basal insulin in the UK. IDegLira improves glycaemic control for patients with type 2 diabetes uncontrolled on basal insulin, without an increased risk of hypoglycaemia or weight gain, and without adding to their injection burden.

1

Diabetes Research Centre, Leicester General Hospital, University of Leicester, Leicester, UK

2

Novo Nordisk Ltd, 3 City Place, Beehive Ring Road, Gatwick, West Sussex RH6 0PA, UK

1 Introduction

3

IMS Health, London, UK

4

Centre for Health Economics, Swansea University, Swansea, Wales, UK

Type 2 diabetes mellitus accounts for approximately 90 % of all diabetes cases and an estimated 80–85 % of type 2 diabetes is related to obesity [1]. Type 2 diabetes is

M. J. Davies et al.

characterised by insulin resistance (decreased tissue response to insulin) and a progressive loss of b-cell function resulting in insulin deficiency [2]. Owing to the progressive nature of type 2 diabetes, many patients will eventually require insulin to achieve glycaemic control. When initiating insulin therapy, National Institute for Health and Care Excellence (NICE) guidance recommends adding a basal insulin to metformin as a first step; however, many patients will need to intensify this treatment regimen over time. When basal insulin has been titrated to an acceptable fasting glucose but glycosylated haemoglobin (HbA1c) remains above target, guidelines recommend adding an injectable therapy to cover post-prandial glucose excursions. Options include the addition of one to three daily injections of a rapidacting mealtime insulin to basal insulin (basal-bolus therapy), switching from basal insulin to a twice-daily premixed insulin regimen, or the addition of a glucagon like peptide-1 receptor agonist (GLP-1 RA) to basal insulin [3]. Despite these guidelines [3, 4], approximately 64 % of patients with type 2 diabetes on a basal-only insulin regimen have suboptimal glycaemic control (HbA1c C7 % [58 mmol/mol]) [5]. It has been reported that 60 % of type 2 diabetes patients in the UK are not intensified in a timely manner; patients were maintained on basal insulin and oral glucose-lowering therapies for prolonged periods (mean follow-up 2.9 years) despite having poor glycaemic control [6]. The failure to intensify insulin regimens when required may have a negative impact on long-term outcomes for patients. There is a need to develop strategies to reduce the delay in intensifying therapy for suitable patients on basal insulin. Barriers to good glycaemic control and intensification of insulin therapy in these patients include fear of hypoglycaemia, weight gain and complex treatment regimens [7, 8]. Hypoglycaemia and weight gain are common side effects of insulin treatment [8, 9]. Hypoglycaemia has a major impact on a patient’s life in terms of physical, mental and social functioning [9], and fear of hypoglycaemia may provoke patients to reduce their insulin dose [10]. Physicians acknowledge that many insulin-treated patients do not have adequate glycaemic control, and 70 % would treat more aggressively were hypoglycaemia not a concern [7]. More than 50 % of patients worry about weight gain [11] and this is associated with poorer diabetes control and adherence to treatment [8, 11]. Clinical guidelines recommend that physicians consider the risk of weight gain when making pharmacological treatment decisions for type 2 diabetes patients [3]. Poor adherence to insulin regimens is common in patients with diabetes, and there is evidence to suggest that dosing complexity/frequency has an impact on patient adherence to treatment [7]. Poor adherence may

limit the ability to achieve glycaemic targets [12], and have a negative impact on long-term outcomes. IDegLira is a co-formulation of a long-acting basal insulin analogue (insulin degludec; IDeg) and a GLP-1 receptor agonist (liraglutide) combined and administered in one single injection device. It is a once-daily treatment indicated for the treatment of adults with type 2 diabetes to improve glycaemic control in combination with oral glucose-lowering medicinal products when these alone or combined with a GLP-1 RA or basal insulin do not provide adequate glycaemic control [13]. It can be administered at any time of day, preferably at the same time of the day. IDegLira was developed to take advantage of the combined effects of IDeg and liraglutide on glycaemic control through the complementary mechanisms of action. The suggested place in the type 2 diabetes treatment pathway for IDegLira is when patients are uncontrolled on basal insulin and require treatment intensification. Thus, IDegLira is an alternative option to the following treatments that are currently being used to intensify basal insulin treatment: Addition of bolus insulin (three times daily) to basal insulin (basal-bolus regimen); addition of GLP-1 RA to basal insulin (loose combination of basal insulin ? GLP-1 RA); and up-titration of basal insulin (basal-only therapy) [4]. The IDegLira clinical development programme (the DUALTM programme) comprises two phase IIIa (DUAL I and II) and five phase IIIb trials (DUAL III-VII) in patients with type 2 diabetes. Core efficacy and safety evidence for IDegLira in patients with type 2 diabetes uncontrolled on basal insulin is provided by DUAL II (NCT01392573) [14] and DUAL V (NCT01952145) [15]. DUAL II compared IDegLira with IDeg; IDegLira was superior to IDeg at equivalent insulin doses in terms of lowering HbA1c, confirmed hypoglycaemia was numerically lower, and change in body weight was significantly more favourable with IDegLira vs. IDeg [14]. DUAL V investigated the efficacy of IDegLira vs. up-titration of insulin glargine (IGlar; LantusÒ) in patients with type 2 diabetes uncontrolled on IGlar at trial entry. IDegLira was superior to IGlar in terms of lowering HbA1c, change in body weight, and hypoglycaemia. Despite a superior reduction in HbA1c, the rate of confirmed hypoglycaemia was 57 % lower with IDegLira [15]. There are currently no direct head-to-head clinical trials of IDegLira vs. basal-bolus therapy or GLP-1 RA added to basal insulin; therefore, a statistical indirect comparison (pooled analysis) was conducted to establish an estimate of the treatment effects of IDegLira vs. these treatment regimens in type 2 diabetes patients uncontrolled on basal insulin [16]. The methodology and results of the pooled analysis have been previously published [16]. The pooled

Cost Effectiveness of IDegLira in a UK Setting

analysis shows that IDegLira achieves a significantly greater decrease in HbA1c vs. basal-bolus therapy and GLP-1 RA added to basal insulin, with lower hypoglycaemia rates and a greater reduction in weight vs. basalbolus therapy [16]. Clinical evidence suggests that IDegLira has advantages over the current intensification options; however, decision making based on both clinical and economic evidence is essential to optimise resource use and service delivery for patients with type 2 diabetes. The objective of our study was to investigate the cost effectiveness of IDegLira vs. the alternative options being used to intensify basal insulin therapy, in patients with type 2 diabetes uncontrolled on basal insulin, from a UK healthcare payers’ perspective.

complications and associated costs to assess their impact on life expectancy and quality-adjusted life expectancy, as recommended in the NICE reference case [18]. The model takes into account mortality as a result of diabetes-related complications. Non-specific mortality was taken from the National Life Tables for United Kingdom (England) 2011–2013 from the Office for National Statistics [22], adjusted to account for mortality caused by diseases explicitly modelled in the Core Diabetes Model. Costs were estimated from a UK healthcare payer perspective and expressed in 2015 GBP. Outcomes captured all direct health effects on the patient. Future costs and clinical outcomes were discounted at 3.5 % per annum in line with health economic guidance for the UK setting [23]. 2.2 Clinical Data

2 Materials and Methods 2.1 Model Overview This cost-utility analysis compares IDegLira with relevant intensification therapies; IGlar (LantusÒ) ? 39 insulin aspart (IAsp) (basal-bolus therapy); basal insulin (IGlar or insulin detemir [IDet]) ? liraglutide 1.8 mg (GLP-1 RA added to basal insulin); and up-titration of IGlar (basal insulin) in type 2 diabetes patients uncontrolled on basal insulin. Substituting the cost of IGlar with the cost of Neutral Protamine Hagedorn (NPH) insulin was investigated as a sensitivity analysis. The main outcome measure, the incremental cost-effectiveness ratio (ICER), is the cost per quality-adjusted life-year (QALY) gained [17]. The ICER allows comparison of the value of alternative treatment options for a specific therapeutic indication and is the preferred outcome measure of health technology assessment bodies, such as NICE [18]. An ICER threshold of £20,000–£30,000 per QALY gained is generally considered to represent acceptable value for money in the UK [19]. Long-term clinical and economic outcomes were estimated using the IMS Health CORE Diabetes Model version 8.5, an internet-based interactive computer model developed to determine the long-term health outcomes and economic consequences of implementing interventions in the treatment of diabetes [20, 21]. The architecture, assumptions, features and capabilities of the model have been previously published [21]. The model is a validated, non-productspecific, diabetes policy analysis tool that allows extrapolation of results from short-term trials to long-term outcomes. It accounts for diabetes therapy, oral hypoglycaemic medications, screening and treatment strategies for microvascular complications, treatment strategies for end-stage complications and multifactorial interventions (Fig. 1). In the base-case analyses, a lifetime (40-year) time horizon was used to capture all relevant long-term

The analysis of IDegLira vs. up-titration of basal insulin used data from the DUAL V head-to-head trial [15]. The analyses vs. basal-bolus therapy and GLP-1 RA added to basal insulin were based on data from the pooled analysis [16]. The pooled analysis was conducted prior to the availability of data from DUAL V, and also contains an arm vs. up-titration of basal insulin; however, as head-tohead data are now available for this comparison it has been used in the economic model. It should be noted, however, that the estimated treatment difference (ETD) in terms of HbA1c reduction for IDegLira vs. up-titration of basal insulin from DUAL V is similar to that from the pooled analysis (1.68 vs. 1.03; ETD = 0.65 and 1.81 vs. 1.13; ETD = 0.67), and thus the choice of dataset is unlikely to have an impact on the overall ICER result. A simulated cohort of patients was defined, with baseline risk factors based on the baseline characteristics of patients randomised to IDegLira in the DUAL II study [14] for the analyses vs. basal-bolus therapy, and GLP-1 RA added to basal insulin, and the DUAL V study [15] for the analysis vs. up-titration of basal insulin (Table 1). Patients treated with IDegLira from the DUAL II study contributed to the pooled analysis used to inform the economic analyses. The DUAL II and V patient populations are representative of patients with type 2 diabetes uncontrolled on basal insulin. Treatment effects used in the CORE Diabetes Model were based on data from the DUAL V trial [15] for the comparison with up-titration of basal insulin, and from the pooled analysis [16] for the comparisons with basal-bolus therapy and GLP-1 RA added to basal insulin (Table 2). Patients receiving IDegLira, IGlar OD or basal insulin ? liraglutide were assumed to receive that treatment until their HbA1c rose above 7.5 % (58 mmol/mol) and then they were switched to basal-bolus therapy (IGlar OD ? 39 IAsp). This assumption recognises that

M. J. Davies et al.

Fig. 1 Flow diagram of the CORE Diabetes Model. ACE angiotensin-converting enzyme, ARB angiotensin receptor blocker, CHF congestive heart failure, CVD cardiovascular disease, MI myocardial infarction, PVD peripheral vascular disease. Source: Ref. [20]

intensification to basal-bolus therapy will be required for patients to maintain glycaemic control over the long term. Patients already receiving basal-bolus therapy were assumed to remain on this for the duration of their lifetime. Following application of the treatment effects based on the trial data, all primary and secondary treatment variables, with the exception of body mass index (BMI), were assumed to follow the natural progression algorithms built into the CORE Diabetes Model, based on the UK Prospective Diabetes Study (UKPDS) or Framingham data (as described by Palmer et al. [21]). For BMI, the treatment effect was assumed to remain constant while on treatment, this was a conservative approach as in reality weight may be put back on gradually over time. Upon reaching the 7.5 % HbA1c threshold, the treatment effects of basal-bolus therapy were applied to all patients based on data from the pooled analysis [16]. 2.3 Costs and Resource Use Sources for costs of complications were derived from a systematic literature review (see the Supplementary Appendix for further information). If the source identified by the review was a source that updates annually, the latest costs were used. Where costs were not reflective of the current year at the time of the analysis, they were inflated to current values using calculations based on the most

updated pay and prices index from the latest version of the Unit Costs of Health and Social Care [26]. A recent publication by Alva [27] was used in the sensitivity analyses to account for updated diabetes-related costs. Costs were estimated from the perspective of the UK National Health Service. Direct costs captured included pharmacy costs, costs associated with diabetes-related complications and concomitant patient management costs. All costs were expressed in 2015 GBP. The most commonly used needles and test strips were identified from English Prescription Cost Analysis data from 2014 [28]. Treatment costs were based on clinical data (DUAL V [15] or the pooled analysis [16]) from which the cohort characteristics were taken. Patients were assumed to be receiving metformin in addition to the study medication. Patients receiving IDegLira, basal insulin ? liraglutide or IGlar were assumed to use one self-monitored blood glucose (SMBG) test per day (comprising one SMBG test strip and one lancet), and patients receiving IGlar plus 39 IAsp were assumed to use four SMBG tests per day, as per UK recommendations [29]. The daily cost of needles was also applied to the treatment costs; one needle was assumed for each injection. Unit drug costs used to calculate annual costs of treatment were taken from the Monthly Index of Medical Specialties, February 2015. The cost of diabetes-related complications in the year of the event and the annual follow-up costs (applied in each year

Cost Effectiveness of IDegLira in a UK Setting Table 1 Baseline cohort characteristics Characteristic

IDegLira vs. basal-bolus therapy and GLP-1 RA added to basal insulin

IDegLira vs. up-titration of basal insulin

DUAL IIc cohort (patients receiving IDegLira)

DUAL Vd cohort (all patients)

Demographics and risk factors, mean (SD) Start age (years)

56.8 (8.9)

58.8 (9.5)

Duration of diabetes mellitus (years) Percentage male (%)

10.3 (6.0) 56.3

11.5 (7.0) 50.3

HbA1c (%)

8.7 (0.7)

8.3 (0.9)

SBP (mmHg)

132.4 (14.8)

133.0 (13.2)

Total cholesterol (mg/dL)

182.0 (45.5)

181.0 (42.9)

HDL cholesterol (mg/dL)

43.4 (11.0)

46.8 (11.6)

LDL cholesterol (mg/dL)

101.9 (37.1)

99.9 (35.4)

Triglycerides (mg/dL)

196.8 (148.0)

182.9 (203.0)

BMI (kg/m2)

33.6 (5.7)

31.7 (4.5)

Percentage smokers (%)

16.1

13.8

Cigarettes per daya

12.7

12.7

Alcohol consumption (fl oz/week)b

4.66

4.50

White

70.9

51.7

Black

4.5

2.0

Hispanic Native American

8.0 0

43.1 0.0

Asian/Pacific Islander

16.6

3.2

Ethnic group, %

BMI body mass index, HbA1c glycosylated haemoglobin, GLP-1 RA glucagon like peptide-1 receptor agonist, HDL high-density lipoprotein, LDL low-density lipoprotein, SBP systolic blood pressure, SD standard deviation a

Derived from [24]

b

Derived from [25]

c

DUAL II is a randomised, controlled, double-blind, multinational, treat-to-target trial in which IDegLira was compared with IDeg over 26 weeks of treatment in patients with type 2 diabetes uncontrolled on basal insulin [14]

d

DUAL V is a randomised, open-label, multinational, treat-to-target trial in which IDegLira was compared with IGlar over 26 weeks of treatment, in patients with type 2 diabetes uncontrolled on IGlar [15]

of the simulation subsequent to the event) were identified through literature reviews and searches of National Health Service reference costs. Unit costs of medications were taken from the British National Formulary 68, September 2014. Costs were inflated to 2015 values using calculations based on the most updated pay and prices index from the latest version of the Unit Costs of Health and Social Care [26]. A summary of the costs of medicines and complications is provided in the Supplementary Appendix (Tables S1–S3).

illustrate challenges in creating such a utility set’’. In the review conducted by Beaudet et al., health state utilities were measured using the EQ-5D questionnaire and taken from a UK population with type 2 diabetes (mainly the UKPDS). The only change from the utility values in Beaudet et al. was the use of a more recent reference for disutilities associated with hypoglycaemia (Evans et al. 2013 [31]) that was not captured in the systematic review as it was published after the review was conducted. See the Supplementary Appendix (Table S4) for utility values.

2.4 Utilities 2.5 Sensitivity Analyses In the base-case analyses, health state utility values for disease and treatment-related outcomes were identified through the findings of a recently published systematic literature review by Beaudet et al. [30]. The objective of this systematic review was to ‘‘identify a set of utility values consistent with the NICE reference case, and to critically discuss and

To assess the impact of the key parameters in the model, several sensitivity analyses were performed (Table 5). These analyses varied model assumptions, or replaced a base-case parameter with an alternative published data point. Specific analyses were conducted relating to NPH insulin, as UK

M. J. Davies et al. Table 2 Treatment effects applied in patients previously uncontrolled on basal insulin Parameter (mean (SD))

HbA1c (%)

IDegLira vs. basal-bolus therapy and GLP-1 RA added to basal insulin (from the pooled analysis [16])

IDegLira vs. up-titration of IGlar (from DUAL V [15])

IDegLira

Basal insulin ? liraglutide

IGlar ? 39 IAsp

IDegLira

IGlar OD

-1.68 (0.94)

-1.33 (0.94)c

-1.39 (0.94)c

-1.81 (1.08)

-1.13 (0.98)c

-3.71 (11.80)

-0.15 (11.80)c

c

SBP (mmHg)

-6.84 (12.95)

-4.68 (12.95)

?1.83 (12.95)

Total cholesterol (mg/dL)

-10.44 (29.67)

-13.26 (29.67)

-5.80 (29.67)

-5.34 (33.25)

?2.95 (33.26)c

HDL cholesterol (mg/dL)

?0.47 (6.74)

-0.74 (6.74)

?0.40 (6.74)

?1.14 (8.19)

?1.23 (8.18)

LDL cholesterol (mg/dL)

-7.56 (24.07)

-9.86 (24.07)

-3.13 (24.07)

-4.12 (26.23)

?2.58 (26.23)c

Triglycerides (mg/dL)

-18.61 (78.66)

-16.56 (78.66)

-16.14 (78.66)

-16.03 (128.29)

-7.57 (128.28)

BMI (kg/m2)

-1.02 (1.21)

-1.27 (1.21)

?1.42 (1.21)c

-0.50 (1.22)

?0.70 (1.22)c

a

d

Severe hypoglycaemia event rate (events/100 PYE) [95 % CI]

0.42

0

2.35

0.00

0.70

Non-severe hypoglycaemiab event rate (events/100 PYE) [95 % CI]

121.88

124.08

1056.31c

223.00

505.00c

Actual daily basal insulin (U) at EOT

37.80 (27.05)

36.63 (27.05)

62.43 (27.05)c

41 (NR)

66 (NR)

Actual daily bolus insulin (U) at EOT

53.61 (NR)

BMI body mass index, CI confidence interval, EOT end-of-trial, HbA1c glycosylated haemoglobin, HDL high-density lipoprotein, LDL lowdensity lipoprotein, NR not reported, OD once daily, PYE patient-years of exposure, SD standard deviation a b

Severe hypoglycaemia defined as that requiring the assistance of another individual Non-severe hypoglycaemia can be self-managed

c

Statistically significant difference to IDegLira

d

No severe hypoglycaemic events were observed with basal insulin ? liraglutide

guidelines recommend that patients with type 2 diabetes commence NPH insulin treatment before progressing to insulin analogues, such as IGlar or IDet [3]. NPH insulin is associated with a lower cost, but an increased rate of hypoglycaemia compared with IGlar [32], and these two features were included in the sensitivity analyses. Alternative pricing analyses were also conducted to investigate cost effectiveness vs. basal insulin ? liraglutide when the cost of the GLP-1 RA is lower, although treatment effects were not varied. A series of incremental discounts (up to 50 %) were applied to the acquisition price of liraglutide in the basal insulin ? liraglutide arm.

Probabilistic sensitivity analyses (PSA) were conducted in the CORE Diabetes Model. Continuous input parameters and regression coefficients were sampled from a distribution with the specified standard deviation or standard error (Table 3). For the PSA, 500 bootstrap iterations of 25,000 patients were simulated. 2.6 Subgroup Analyses: IDegLira vs. Up-titration of IGlar Additional analyses were conducted to evaluate the cost effectiveness of IDegLira vs. up-titration of IGlar in

Table 3 Distributions used in the CORE Diabetes Model to sample each parameter Parameter

Distribution

Events

Probability for first MI, first stroke, angina, heart failure

The coefficients applied in respective risk regression equations are sampled. While the sampling distribution for the coefficients is normal (Gaussian), the resulting distribution of probabilities is beta

Cohort

Normal distribution is used for all parameters except for triglycerides for which gamma distribution is used

Treatment effect

Start age, duration of diabetes, HbA1c, SBP, total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides, BMI Change from baseline in: HbA1c, SBP, total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides, BMI

Costs

Statins, ACE inhibitor, aspirin, all health state costs

Log-normal

Utilities

All utility values used in the base case

Beta

Beta

ACE angiotensin-converting enzyme, BMI body mass index, HbA1c glycosylated haemoglobin, HDL high-density lipoprotein, LDL low-density lipoprotein, MI myocardial infarction, SBP systolic blood pressure

Cost Effectiveness of IDegLira in a UK Setting

defined patient subgroups with different baseline BMI or HbA1c. These analyses used the HbA1c reduction (effect) for patients with a baseline HbA1c [9 %; baseline BMI \25; and baseline BMI [35. All other parameters remained the same apart from the HbA1c reduction as a consequence of the different baseline HbA1c or BMI. 2.7 Contribution of Clinical Effects to QALYs Gained Additional analyses were conducted to investigate the contribution of individual clinical effects of IDegLira to the outcomes observed. The impact of key treatment effects (HbA1c, BMI, SBP, hypoglycaemia and lipids) upon incremental QALY change were isolated in these analyses, and their proportionate impact on the QALY change was quantified. In each analysis, all other treatment inputs besides the isolated treatment effect were set to be equivalent to the level of the comparator, hence different contributions were expected with different comparators. The results of these analyses are presented as approximate relative impacts of the base-case benefit.

3 Results 3.1 Base-case Analysis 3.1.1 IDegLira vs. Basal Insulin ? Liraglutide Treatment with IDegLira was associated with a mean increase in quality-adjusted life expectancy of 0.123 QALYs, and total cost savings of £971 over a patient’s lifetime, compared with basal insulin ? liraglutide (Table 4). IDegLira was the dominant treatment (more effective and less costly). The cost saving with IDegLira was driven predominantly by the avoided treatment of diabetes-related complications and the lower treatment acquisition costs. 3.1.2 IDegLira vs. IGlar ? 39 IAsp Treatment with IDegLira was associated with a mean increase in quality-adjusted life expectancy of 0.414 QALYs, and total cost savings of £1698 over a patient’s lifetime, compared with IGlar plus 39 IAsp (Table 4). IDegLira was the dominant treatment. The cost saving with IDegLira was driven predominantly by the avoided treatment of diabetes-related complications and the lower treatment acquisition costs. 3.1.3 IDegLira vs. Up-titrated IGlar Treatment with IDegLira was associated with a mean increase in quality-adjusted life expectancy of 0.237

QALYs, and costs of £1441 over a patient’s lifetime, compared with up-titrated IGlar (Table 4). The increased cost was driven by the higher acquisition cost of IDegLira vs. IGlar; however, this was partially offset by reduced costs as a result of avoided diabetes-related complications. IDegLira was associated with an ICER of £6090 per QALY gained vs. IGlar, which falls well below the willingness-to-pay threshold of £20,000/QALY gained. Thus, IDegLira is estimated to be a cost-effective treatment when compared with up-titration of IGlar. Total costs in this analysis are slightly lower than the other two analyses, which may be a reflection of the different patient cohorts used in the simulations. The DUAL V trial [15] may represent patients with slightly less advanced type 2 diabetes than patients in DUAL II [14], that is, patients uncontrolled on basal insulin who have not fully exhausted the opportunity for further uptitrating their basal insulin dose. This may explain slightly lower costs associated with diabetes-related complications. 3.2 Probabilistic Sensitivity Analysis Assuming a willingness-to-pay threshold of £20,000 per QALY gained, PSA indicated there was a 99 % probability that IDegLira would be cost effective vs. basal insulin ? liraglutide, a 100 % probability that IDegLira would be cost effective vs. IGlar ? 39 IAsp, and a 98 % probability that IDegLira would be cost effective vs. uptitration of IGlar (Fig. 2). 3.3 One-way Sensitivity Analyses 3.3.1 IDegLira vs. Basal Insulin ? Liraglutide One-way sensitivity analyses demonstrated that the results were largely robust to changes in input parameters (Table 5). The parameters with the greatest impact on cost effectiveness were a 50 % reduction in liraglutide price, a treatment switch at an HbA1c threshold of 6.5 %, and the application of the cost of IDegLira at a maximum dose. However, none of these key drivers had sufficient impact to alter the cost effectiveness of IDegLira, which remained dominant or well below the cost-effectiveness threshold of £20,000/QALY. Discounts in increasing incremental intervals of 10 % were applied to the acquisition price of liraglutide in the comparator arm, to show the impact on the ICER of a lower GLP-1 RA price. IDegLira remained dominant up to a 30 % discount in the price of liraglutide (Table 5). Even at a 50 % discount in the price of liraglutide, IDegLira was highly cost effective, with an ICER of £3129/QALY.

M. J. Davies et al. Table 4 Base-case analysis Mean (SD)

IDegLira

IGlar plus liraglutide

Difference

IDegLira vs. basal insulin ? liraglutide Discounted life expectancy (years)

12.960

12.873

0.087

Discounted quality-adjusted life expectancy (QALYs)

7.499

7.376

0.123 -971

Discounted direct costs (£)

54,814

55,785

Treatment

22,934

23,560

-626

Management

1975

1963

12

CVD

10,864

10,982

-118

Renal

3334

3438

-104

Ulcer/amputation/neuropathy

11,944

11,994

-50

Eye

2998

3043

-45

Hypoglycaemia

766

804

-38

ICER (life expectancy)

IDegLira dominant

ICER (QALYs)

IDegLira dominant

Mean (SD)

IDegLira

IGlar plus 39 IAsp

Difference

Discounted life expectancy (years)

12.960

12.654

0.306

Discounted quality-adjusted life expectancy (QALYs)

7.499

7.086

0.414

Discounted direct costs (£)

IDegLira vs. IGlar ? 39 IAsp

54,814

56,512

-1698

Treatment

22,934

22,617

317

Management

1975

1933

42

CVD

10,864

11,463

-599

Renal

3334

3342

-8

Ulcer/amputation/neuropathy Eye

11,944 2998

12,865 3389

-921 -391

Hypoglycaemia

766

903

-137

ICER (life expectancy)

IDegLira dominant

ICER (QALYs)

IDegLira dominant

Mean (SD)

IDegLira

Up-titrated IGlar

Difference

Discounted life expectancy (years) Discounted quality-adjusted life expectancy (QALYs)

12.291 7.364

12.089 7.127

0.202 0.237

Discounted direct costs (£)

IDegLira vs. up-titrated IGlar

49,605

48,164

1441

Treatment

22,159

20,054

2105

Management

1897

1869

28

CVD

11,867

12,135

-268

Renal

1301

1364

-63

Ulcer/amputation/neuropathy

8811

9028

-217

Eye

2833

2908

-75

Hypoglycaemia

737

807

-70

ICER (life expectancy)

£7130/life-year gained

ICER (QALYs)

£6090/QALY gained

CVD cardiovascular disease, ICER incremental cost-effectiveness ratio, QALY quality-adjusted life-year, SD standard deviation

Cost Effectiveness of IDegLira in a UK Setting

Fig. 2 Probabilistic sensitivity analysis: cost-effectiveness scatterplots and cost-effectiveness acceptability curves. QALYs quality-adjusted lifeyears

-988

2

0.123

0.123

0.123

Dominant

Dominant

Dominant

Dominant

Dominant

2786

Dominant

Dominant

Dominant

Dominant

Dominant

Dominant

Dominant

Dominant

Dominant

Dominant

Dominant

-2256

-1497

-1899

2438

3114

-385

-884

-1457

-1671

-1242

-1695

-1729

-1494

-1822

-2226

-1393

-1698

-971 -1765

Disutility of -0.0061/unit [25 kg/m2 [36] NICE guidance cohort applieda [37]

Only statistically significant differences between IDegLira and comparator applied

Removing key drivers for comparisons with basal insulin ? lira and basal-bolus therapy

-1145

-971

CORE multiplicative approach applied

UKPDS 82 equations applied [35]

-971

Hypo utilities from Currie et al. [34]

0.118 0.065

0.096

0.126

0.109

Dominant Dominant

Dominant

Dominant

Dominant

-1691

-1698 -1717

-1964

-1698

-1698

Other (base case—hypo utilities from Evans et al. [31]; disutility of -0.01/unit BMI above 25 kg/m from Lee et al. [33]

-1193

-936

Cost of complications from Alva et al. [27]

-1005

Cost of complications -10 %

0.132

0.123

-563 -570

0.123

0.123

0.082

343

-769

Cost of complications ?10 %

Alternative cost of complications

NPH cost applied with NPH hypo rates

Alternative treatment costs and effects (base case = IGlar costs and hypo rates)

NPH cost applied

IDegLira maximum dose

Defined daily dose in comparator arm

BMI benefit maintained after switch Alternative treatment costs (base case = trial doses)

-902

-479 -1828

Treatment switch at HbA1c 6.5 %

Treatment switch at HbA1c 8.5 %

Alternative BMI progression (base case = BMI benefit abolished on switch)

0.080

-1789 0.116

0.067

-1210

Treatment switch at 5 years

0.068

0.100

0.181

Treatment switch at 3 years

Alternative treatment switching (base case = treatment switch at HbA1c 7.5 %)

-753

0.105

-1148

6 % discount rate

0.077

0.123

-1027

-971

0.408

0.407 0.577

0.346

0.442

0.367

0.414

0.414

0.414

0.942

0.414

0.414

0.414

0.720

0.577

0.368

0.534

0.420

0.336

0.606

0.365

0.250

0.414

D QALY

Dominant

Dominant Dominant

Dominant

Dominant

Dominant

Dominant

Dominant

Dominant

2588

7528

Dominant

Dominant

Dominant

Dominant

Dominant

Dominant

Dominant

Dominant

Dominant

Dominant

Dominant

Dominant

ICER (£)

D Cost (£)

D Cost (£)

D Cost (£)

D QALY

IDegLira vs. IGlar ? 39 IAsp

IDegLira vs. basal insulin ? liraglutide

0 % discount rate

Alternative discount rate (base case = 3.5 %)

Time horizon = 20 years

Time horizon = 10 years

Alternative time horizon (base case = 40 years)

Base case

Table 5 Sensitivity analyses

1385

1441 3755

1201

1441

1441

1093

1507

1374

2106

2172

2417

2166

1444

4162

182

3390

2072

1314

1926

1086

1244

1441

D Cost (£)

0.233

0.232 0.357

0.184

0.245

0.214

0.237

0.237

0.237

0.276

0.237

0.237

0.237

0.354

0.328

0.147

0.264

0.230

0.188

0.357

0.213

0.131

0.237

D QALY

5951

6208 10,529

6546

5879

6733

4618

6370

5809

7622

9181

10,215

9154

4082

12,693

1237

12,823

9017

6977

5400

5106

9485

6090

ICER (£)

IDegLira vs. up-titration of IGlar

M. J. Davies et al.

One-way sensitivity analyses demonstrated that the results were largely robust to changes in input parameters (Table 5). The parameters with the greatest impact on cost effectiveness were the application of NPH cost and rates of hypoglycaemia, the maintenance of BMI benefit following treatment switch, the application of NPH cost, the application of a 0 % discount rate to costs and outcomes, and altering the time horizon of the model to 10 years. However, none of these key drivers impact sufficiently to alter the cost effectiveness of IDegLira based on a cost-effectiveness threshold of £20,000/QALY. 3.3.3 IDegLira vs. Up-titrated IGlar One-way sensitivity analyses demonstrated that the results were largely robust to changes in input parameters (Table 5). The parameters with the greatest impact on cost effectiveness were the maintenance of the BMI benefit after treatment switch, altering the time horizon of the model to 10 years, the application of a 0 % discount to costs and outcomes, and a treatment switch at 5 years. However, these key drivers had little impact on the cost effectiveness of IDegLira at a cost-effectiveness threshold of £20,000/QALY. 3.4 Subgroup Analyses

Cohort as defined in the NICE type 2 diabetes guideline [3]

HbA1c effect when baseline BMI \ 25

HbA1c effect when baseline BMI C 35

HbA1c effect when HbA1c [ 9 %

3.3.2 IDegLira vs. IGlar ? 39 IAsp

a

3129 0.123 Liraglutide price -50 %

Subgroup analyses for IDegLira vs. up-titrated IGlar

385

Dominant

925 0.123 114 Liraglutide price -40 %

Dominant 0.123

0.123 -157

-428 Liraglutide price -20 %

Liraglutide price -30 %

Dominant 0.123 Liraglutide price -10 %

-700

D Cost (£)

BMI body mass index, HbA1c glycosylated haemoglobin, ICER incremental cost-effectiveness ratio, NICE National Institute for Health and Care Excellence, NPH Neutral Protamine Hagedorn, QALY quality-adjusted life-year, UKPDS UK Prospective Diabetes Study

4678

5487

0.272

0.269

1274

1474

344 0.173 60

ICER (£) D QALY D Cost (£) ICER (£) D Cost (£) D Cost (£)

D QALY

IDegLira vs. IGlar ? 39 IAsp IDegLira vs. basal insulin ? liraglutide

D QALY Pricing analyses conducted for basal insulin ? lira comparison

Table 5 continued

IDegLira vs. up-titration of IGlar

Cost Effectiveness of IDegLira in a UK Setting

Subgroup analyses were conducted to evaluate the cost effectiveness of IDegLira vs. up-titration of IGlar in defined patient subgroups with different baseline BMI or HbA1c. IDegLira remained cost effective vs. up-titration of IGlar, irrespective of baseline BMI. In patients with a baseline HbA1c [ 9 %, IDegLira became more cost effective vs up-titrated IGlar (Table 5). 3.5 Contribution of Clinical Effects to QALYs Gained The isolated impact of the individual treatment effects of IDegLira was calculated as a proportion of the total incremental QALY change (Supplementary Appendix, Table S6). In comparison with basal insulin ? liraglutide, HbA1c was shown to have the greatest impact on incremental QALY change (89 %); however, this should be interpreted in the context of the small total incremental QALY change in the base case. For the comparison with IGlar ? 3 9 IAsp, HbA1c (32 %), and hypoglycaemia (27 %) had the greatest impact on incremental QALY change. HbA1c (55 %) also had the greatest impact in the comparison with up-titrated IGlar.

M. J. Davies et al.

4 Discussion NICE recommends a number of treatment options for patients with type 2 diabetes uncontrolled on basal insulin therapy who need treatment intensification, including addition of a prandial insulin, addition of a GLP-1 RA or up-titration of current basal insulin [3]. IDegLira, a coformulation of basal insulin and GLP-1 RA in one pen, provides an alternative intensification option for patients uncontrolled on basal insulin therapy, and takes advantage of the complementary mechanisms of action of IDeg and liraglutide to offer effective glycaemic control without increased risk of hypoglycaemia or weight gain [14, 16]. This long-term health economic evaluation suggests that, from a UK healthcare payer perspective, IDegLira is a highly cost-effective treatment option vs. current insulin intensification therapies. IDegLira is dominant over IGlar ? 39 IAsp (basal-bolus), and basal insulin ? liraglutide (GLP-1 RA added to basal insulin), as it is less costly and more effective. Compared with up-titrated IGlar, IDegLira is more effective and has an increased cost, but remains cost effective with an ICER of £6090/QALY, well below the commonly accepted willingness-to-pay threshold of £20,000–£30,000/QALY in the UK. The patient populations modelled in this evaluation were representative of patients with type 2 diabetes uncontrolled on basal insulin therapy; therefore, the results are generalisable to these populations in clinical practice. Extensive sensitivity analyses demonstrate that the results are largely robust to changes in input parameters. In the comparison with basal-bolus therapy, the only scenarios where IDegLira was no longer the dominant treatment were where costs and hypoglycaemia rates associated with NPH insulin were substituted with those of IGlar. NPH insulin is associated with a lower cost, but an increased rate of hypoglycaemia compared with IGlar [32]; however, even with just the lower cost of NPH insulin applied, without accounting for the higher rate of hypoglycaemia, IDegLira remained cost effective with an ICER of £7528/ QALY gained. Similarly in the comparison with basal insulin ? liraglutide, IDegLira remained highly cost effective in all scenarios. In the comparison with up-titrated IGlar, IDegLira also remained cost effective in all scenarios evaluated, with a maximum ICER of £12,823/ QALY gained. Subgroup analyses demonstrated that IDegLira is cost effective vs. up-titrated IGlar at different baseline HbA1c or BMI levels. PSA showed a very high probability that IDegLira would be cost effective vs. all three comparators at the £20,000/QALY willingness-to-pay threshold (Fig. 2). This evaluation used clinical inputs from DUAL V [15] for a head-to head comparison vs. up-titration of IGlar and

a pooled analysis for an indirect estimate of the treatment effects of IDegLira compared with IGlar ? 39 IAsp and basal insulin ? liraglutide [16]. Although the latter is an indirect statistical comparative method, which could be considered a limitation of this economic evaluation, the use of such evidence synthesis approaches, using robust methodologies, is becoming increasingly important (and accepted) for health technology assessment in Europe [38]. The methodology used in the pooled analysis is recognised by the European Network for Health Technology Assessment (EUNETHTA) guidelines on how to conduct indirect analyses [39] and has been used previously [40]. A limitation of the study (common to a number of health economic analyses) was the reliance on short-term clinical trial data to make long-term projections. However, this remains one of the essential tenets of health economic modelling and is arguably one of the best available options to inform decision making in the absence of long-term clinical trial data. Whilst there is always an element of clinical uncertainty around the accuracy of such an approach, every effort was made in the present analysis to minimise this, primarily by using a diabetes model that has been extensively published and validated against real-life data, both on first publication and recently following a series of model updates [20]. IDegLira has been shown to be an effective treatment option for suitable type 2 diabetes patients uncontrolled on basal insulin, with a reduced risk of hypoglycaemia and weight gain vs. IGlar and basal-bolus therapy [15, 16], which may address the challenges associated with intensifying therapy [15]. From an adherence perspective, IDegLira may also be advantageous as it is associated with less nausea than typically observed with GLP-1 RAs, a likely result of the gradual increase in the dose of the liraglutide component of IDegLira during dose titration [41]. The low rate of nausea with IDegLira may be a key differentiator over GLP-1 RA added to basal insulin. IDegLira is a once-daily, fixed-ratio co-formulation of IDeg and liraglutide administered via a pre-filled pen for subcutaneous injection. The once-daily dosing of IDegLira means that patients have a simple treatment option with reduced treatment complexity, owing to one to two fewer injections than GLP-RA added to basal insulin and up to three fewer than basal-bolus regimens. Furthermore, the combination of IDeg and liraglutide in a single pen device means that patients will only need to perform a single dose adjustment, and resource use costs (e.g., needles) will be lower than basal-bolus therapy and GLP-1 RA added to basal insulin. IDegLira also allows patients to use the GLP-1 RA dose equivalent to the basal insulin dose needed in the fixed combination. This means that patients use the dose they

Cost Effectiveness of IDegLira in a UK Setting

need to achieve glycaemic control and local health economies pay for the dose the patient uses, rather than the dose indicated by the licence of the GLP-1 RA mono component.

5 Conclusion Our analysis suggests that once-daily IDegLira is a simple, clinically and highly cost-effective treatment option vs. current insulin intensification options for type 2 diabetes patients uncontrolled on basal insulin in a UK setting. Acknowledgments The authors acknowledge the assistance of Paige Iversen (IMS Health) for assistance with modelling, and DRG Abacus for medical writing and editorial support. Author contributions KG performed the analysis and interpretation of results. MJD, DG, BC and PE contributed to study design, conduct/data collection and interpretation of results. All authors contributed to the writing, review and final approval of this manuscript.

Compliance with Ethical Standards Funding This study was funded by Novo Nordisk. Conflicts of interest DG and BC are employees of Novo Nordisk. MJD has acted as consultant, advisory board member and speaker for Novo Nordisk, Sanofi-Aventis, Lilly, Merck Sharp and Dohme, Boehringer Ingelheim, AstraZeneca and Janssen and as a speaker for Mitsubishi Tanabe Pharma Corporation and Takeda Pharmaceuticals International Inc. She has received grants in support of investigator and investigator initiated trials from Novo Nordisk, Sanofi-Aventis and Lilly. PE has received consulting support and research funding from Bristol Myers Squibb, Novo Nordisk, Merck Sharp and Dohme, Otsuka Pharmaceutical and Takeda.

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