Lee Stern, MS

Renal cell carcinoma (RCC) is diagnosed in approximately 58,000 individuals each year in the United States

 and accounts for more than 90% of all primary malignant renal tumors.

 These statistics refl ect the steady rise in the incidence rate of kidney cancers over the past 3 decades.

 It has been estimated that 20% to 30% of patients with RCC present with metastatic disease.

Metastatic renal cell carcinoma (mRCC) is diffi cult to treat, with a 5-year survival rate of less than 10%.

 Additionally, RCC is costly, with US-based studies suggesting the estimated annual cost of RCC to be as high as $4.4 billion. In those with metastatic disease, the estimate ranges from $107 to $556 million per year.

Several targeted chemotherapeutic agents have emerged over the past 4 years. Specifically, vascular endothelial growth factor tyrosine kinase inhibitors such as sunitinib and sorafenib have become the standard of care, replacing systemic therapies such as interferon-alfa. Despite the recent advances in treatment options, disease progression in RCC patients is typically only delayed; the disease is not cured. Trials investigating the effi cacy of sunitinib and sorafenib demonstrated an increase in progression-free survival (PFS) ranging from 2.7 months to 6 months.

Bevacizumab, another drug option, has shown a benefit in PFS in a pivotal phase III trial in combination with interferon-alfa versus interferon-alfa alone; however, overall survival results have not yet been reported.

While there has been some improvement over prior therapies, the majority of patients will face inevitable disease progression. Therefore, there is a significant unmet need among RCC patients who experience treatment failure on these newer agents. Everolimus (Afinitor; Novartis Pharmaceuticals Corporation, East Hanover, New Jersey) is a once-daily, orally administered agent with documented effi cacy for the treatment of patients with advanced RCC after they fail to respond to or become intolerant to treatment with sunitinib or sorafenib.

Everolimus is an inhibitor of the mammalian target of rapamycin (mTOR), a component of an intracellular signaling pathway that regulates cellular metabolism, growth, proliferation, and angiogenesis.

 A pivotal, randomized, phase III trial of patients with mRCC whose disease had progressed on sunitinib, sorafenib, or both demonstrated a median PFS of 4.9 months for everolimus-treated patients compared with 1.9 months for those receiving best supportive care alone (hazard ratio 0.33, 95% confi dence interval 0.25-0.43; 

Although novel, targeted oncology treatments have resulted in incremental improvements in survival, they may be viewed as a potential source of substantial economic burden on the healthcare system, requiring payers and physicians to weigh costs and benefits when determining a treatment strategy.

 In addition, payers can expect to incur relatively high costs during the last few months of life, during which time patients are receiving numerous expensive salvage therapies that ultimately may not affect their overall survival. Therefore, health plans will likely benefit from an examination of the incremental budget impact of a given treatment regimen upon its approval. The purpose of this study is to determine the impact that the introduction of everolimus may have on a health plan’s budget by using a health economic model that considers the various treatments currently available to patients with mRCC within the bounds of explicit model assumptions.

A budget impact analysis was developed using Microsoft Excel (Microsoft Corporation, Redmond, Washington) to evaluate the current cost of therapy for RCC from April 2008 to March 2009 and October 2009 to September 2010 for a hypothetical health plan population of 1 million members. These time points were selected to account for markets before and after the approval of everolimus in mRCC (March 2009). Because there may be a delay associated with the uptake of everolimus immediately following approval, a 6-month period (April 2009 to September 2009) was considered as a lag period and was not included for base case post-everolimus launch market (see 

Population and Treatments Under Consideration

Medication utilization data and prevalence rate for mRCC were applied to a hypothetical health plan of 1 million insured adults. The analysis considered only pharmacotherapeutic treatments for mRCC (ie, systemic chemotherapy and targeted treatments). The number of eligible patients was calculated based on mRCC epidemiology estimates (

). Market share data for the different treatments were based on recent surveillance studies for RCC and real-world market share data (Novartis Pharmaceuticals Corporation, research market share data on file, proprietary and unpublished data, 2010). The Novartis data were obtained from patient-level drug utilization information, which was collected from 36 states electronically on a monthly basis. Treatment duration as well as the proportion of patients who moved from fi rst-line to subsequent lines was based on a retrospective analysis of claims data performed by Intercontinental Marketing Services (IMS) Health, which has been used previously for other studies.

 Information on treatment durations was available for both pre—and post–everolimus launch periods from IMS Health analysis. However, in order to keep the durations of each treatment consistent across both pre-and post-everolimus periods, treatment durations recorded during the pre-everolimus market were applied to the post-everolimus market. For drugs for which treatment duration data were not available in the pre-everolimus period (eg, everolimus, pazopanib), the treatment duration recorded in the post-everolimus period was applied.

To establish the total cost of treatment with a chemotherapeutic agent, the model considered the wholesale acquisition cost of each treatment, the recommended dose, administration costs, and the duration of treatment. Treatment schedule and dosing assumptions were based on the prescribing information for each product.

 Treatment administration costs were added to every infused treatment, and were obtained from Current Procedural Terminology (CPT) codes.

The total administration cost for each regimen was estimated by multiplying unit 12 administrative costs by the mean treatment duration. Details on the treatment schedules, unit costs, costs per dose, cost per week, and administration costs for the treatments considered are provided in 

The model accounted for the differing safety profi les associated with each treatment. Treatment-related grade 3/4 adverse event incidence estimates were adopted from prescribing information or pivotal trials for each agent.

 The costs of treating the adverse events were based on independent cost analyses.

Adverse events for which no cost literature was available had treatment costs estimated based on National Comprehensive Cancer Network guidelines

 Details of specific calculations are available upon request. Costs calculated to treat adverse events were assumed for the entire course of chemotherapeutic treatment within a particular line of treatment. Costs for managing adverse events are shown in 

. These costs along with incidence rates from prescribing information and pivotal trials were used to calculate the total cost of treating adverse events for treatments considered in the model (Table 2).

As underlying best supportive care was assumed to be the same across comparators, the cost of best supportive care was not included in this analysis.

Total cost for each medication across the first, second, and third line of treatment was estimated by applying the drug acquisition, administration, and adverse event costs to the hypothetical mRCC population for the preeverolimus and post-everolimus scenarios. Across both the pre- and post-everolimus scenarios, mRCC incidence, treatment comparators, and costs (drug, administration, and adverse event management) were all assumed to remain the same; only the relative market share for each comparator varied. The eligible relative market share of patients treated with everolimus as second- or thirdline therapy in the postlaunch scenario was 11.63% and 27.73%, respectively.

The analysis focused on evaluating budgetary differences between the pre—and post–everolimus launch markets at 3 levels: (1) the aggregate level, (2) the aggregate budget impact stratified by treatment comparators, and (3) the budget impact per member per year (PMPY) and per member per month (PMPM).

Sensitivity analyses were performed, where (1) thepost-everolimus launch period was changed from October 2009—September 2010 to April 2009–March 2010 (which included the initial postapproval uptake lag period) and (2) the post–everolimus launch period was changed to January 2010–December 2010 (which assumed a 9-month everolimus uptake lag period instead of 6 months) (see eAppendix A). Additional sensitivity analyses were performed. They assumed that (3) administration costs were zero, (4) adverse event costs were zero, and (5) treatment duration for all comparators was the same as that for everolimus within each line of treatment. Market share of everolimus for second- and third-line treatment for the April 2009–March 2010 postlaunch period (sensitivity analysis 1) was 7.77% and 25.12%, respectively. For the January 2010–December 2010 postlaunch period (sensitivity analysis 2), market share was 12.80% and 27.82%, respectively. Sensitivity analyses 3, 4, and 5 were performed primarily to remove the effect of administration costs, adverse event costs, and treatment durations from the aggregate drug budgetary impact.

Supplementary scenario analyses were conducted where market share of everolimus in the postlaunch period was exclusively captured from the market share of sorafenib (scenario 1) and sunitinib (scenario 2). Another scenario analysis, where everolimus market share was captured from temsirolimus (64.0%), bevacizumab (26.4%), and sorafenib (9.6%), was also performed. These analyses were conducted for both second and third lines of treatment. Market share data for the base case and all the scenarios are shown in

With an incidence rate of 0.0203% and a hypothetical plan of 1 million covered individuals, a population of 203 mRCC patients was calculated, of whom 90% (182 patients) received first-line active treatment for mRCC. Longitudinal analysis of IMS Health data for patients in 2008 showed that 28.2% (52 patients) of patients who received first-line therapy moved to second-line therapy; 24.1% (12 patients) of these patients additionally received a third-line therapy. Similarly for 2009, 21.8% (40 patients) of patients receiving first-line therapy also received second- line therapy, while only 13.5% (5 patients) of these patients received third-line therapy (Table 1).

In the pre—everolimus launch market scenario, the total cost of drugs, administration, and adverse event management, across all lines of treatment, was found to be $7,050,157. In the market following the introduction of everolimus, the total cost decreased to $6,741,642, yielding a savings of $308,516. 

reports the cost stratified by different therapies within each line of treatment. Based on the model calculations, with the availability of everolimus after October 2009, the budget impact PMPM cost was −$0.03 and the budget impact PMPY cost was—$0.31.

Results for the sensitivity and additional scenario analyses are presented in 

. Sensitivity analyses that accounted for the extended postapproval lag period (January 2010 to December 2010) and the assumption that all comparators had the same treatment duration as that of everolimus (analyses 2 and 5, respectively) both resulted in additional savings of more than $70,000 (increased PMPY of >$0.07). However, the sensitivity analyses that examined the inclusion of the postapproval uptake lag period (April 2009 to March 2010) and set the adverse event management costs to zero dollars (analyses 1 and 4, respectively) resulted in less savings than the base case analysis. No substantial differences were found across all additional scenario analyses (scenarios 1, 2, and 3), where total savings ranged from $233,237 to $254,274. Both PMPY and PMPM costs for all the 3 additional scenarios appeared to be similar to those of the base case analysis.

This model estimated the annual budget impact of treating patients with mRCC in order to show the range of possible budget impacts in a market without everolimus compared with a market where everolimus was used in different amounts by different types of patients. The model results demonstrate the potentially favorable impact that the introduction of everolimus could have on a health plan’s budget.

Additional analyses were conducted to assess scenarios whereby the introduction of everolimus as a next-line therapy to sunitinib (scenario 1), sorafenib (scenario 2), or temsirolimus, bevacizumab, and sorafenib (scenario 3) resulted in a favorable aggregate budget impact compared with a treatment algorithm that excluded everolimus altogether. At the PMPM level, including everolimus in the treatment algorithm is cost neutral. The difference in total cost is mostly attributed to the difference in the cost of anticancer drugs.

There are several limitations to this analysis. This model did not take into account or correct for differences in populations from study to study. Consequently, results might have been different if data had been extracted from a unified study. For instance, because adverse event rates were taken from a series of different studies, variation in the adverse event rates assigned to each drug might have been more directly attributable to differences in patient populations. However, a sensitivity analysis was undertaken to assess the budget impact of everolimus when adverse event costs were excluded. The findings suggest an incremental PMPM budget impact of −$0.02, which is in line with the base case results.

Also, it was assumed that treatment durations recorded for comparator agents in the pre-everolimus market would be the same in the post-everolimus market. This assumption was made in order to prevent potential differences in treatment duration from biasing the model results. However, it should be acknowledged that this assumption could have led to either an overestimation or an underestimation of overall costs. To address this issue, a sensitivity analysis was performed, whereby all agents were assigned the duration of treatment recorded for everolimus. The results of this sensitivity analysis show that even with this adjustment, the budget impact results are similar to the base case —$0.04 incremental PMPM for the sensitivity analysis versus –$0.03 for the base case. Additionally, costs for best supportive care and palliative care were not taken into account in this model; therefore, the change in proportion of overall costs is likely overestimated in each scenario.

Of note, the market share data were collected on a monthly basis only, and patients were not followed longitudinally. Therefore, the analysis was performed using patient-months to account for patient entry/dropout during the course of the data collection. To take into account the lag time involved in drug uptake immediately following approval and to avoid underestimating everolimus use in the current scenario, market share data 6 months following approval of everolimus were used for the base case analysis. However, sensitivity analyses that included the 6-month lag period and an extended lag period (9 months) resulted in findings comparable to those in the base case. Moreover, with increased market share of everolimus (relative to the base case) in the extended lag period analysis, the savings incurred were more than those observed in the base case.

The analysis did not consider surgical options for 2 main reasons. Because the target population of this analysis was patients with mRCC, only a small proportion would have qualified for surgery.

Additionally, surgical procedures do not directly compete with everolimus therapy because the proportion of patients receiving surgery would remain the same before and after the introduction of everolimus to this disease landscape. If surgery were to be included as a comparator, the results would remain the same.

Despite the potential generalization of some of the inputs given the only available data, it is important to consider the relatively small overall impact that mRCC has on managed care budgets compared with other types of cancer or other chronic diseases. Even if the results of this study were underestimated or overestimated, the analysis shows that providing patients with additional treatment options such as everolimus would not result in a substantial increase in a health plan’s overall budget.

The future management of oncology therapy in mRCC may require quantifi cation of value through evidencebased medicine, justifying the margin of benefit with each cycle. It is an opportunity to develop clinical pathways based on the efficacy, safety, and cost of second- or third-line treatment options. With new treatment options available in the future, it will become important to understand who can benefit with meaningful improvement in overall survival and to identify through biomarkers the sequence of therapies needed to maximize value.

Articles

A decision-analytical model shows that everolimus has a favorable budget impact when introduced into the treatment algorithm for metastatic renal cell carcinoma.

Budget Impact of Everolimus in Treating Metastatic Renal Cell Carcinoma

Recent data estimate that 10.7% of Americans 20 years and older have diabetes, with 90% to 95% of these having type 2 diabetes mellitus (T2DM).

 The incidence of diabetes in adults (ages 18-79 years) nearly tripled from 493,000 in 1980 to more than 1.5 million in 2007 in the United States.

 As a result, the 2007 direct and indirect costs attributed to diabetes amounted to $174 billion in the United States alone

 and are likely to rise in the future with increasing disease prevalence.

Despite the availability of multiple antidiabetic agents, approximately 43% of treated T2DM patients do not achieve glycemic control as defined by the American Diabetes Association (ADA) treatment guidelines

 One barrier to effective glycemic control may be poor adherence to oral antidiabetic agents. Poor adherence can result from the occurrence of medication side effects and/or tolerability issues,

The ADA recommends the use of a stepwise approach to target glycemic control, including the use of combination therapy when monotherapy plus lifestyle and therapeutic changes are insufficient. Furthermore, treatment goals should be individualized, balancing potential efficacy with such factors as safety, tolerability, ease of use, and adherence. For certain patients, healthcare providers may consider goals lower than the general A1C goal of less than 7% if these goals can be attained without significant hypoglycemia or other adverse effects.

 The impact of hypoglycemia on patients varies widely, from events with mild but bothersome symptoms to an excessive decrease in blood glucose, which can result in more severe complications including coma, cardiac arrhythmias, or myocardial ischemia.

 Hypoglycemia or fear of hypoglycemia may also affect patient behavior, leading to more frequent food consumption resulting in weight gain, which in turn can increase risk of cardiovascular events over time.

 Other side effects associated with diabetes control agents, such as potential increases in body weight, can have a negative impact on patient health by increasing the risk of cardiovascular disease, a major cause of morbidity and mortality in the diabetic population.

Currently available oral antidiabetic agents may have comparable efficacy in terms of glycemic control; however, health outcomes associated with individual therapies may vary in side effect profiles and durability of glycemic control.

 The ADA guidelines currently recommend metformin as first-line therapy for T2DM patients, with sulfonylureas or thiazolidinediones as second-line therapy for patients requiring further glycemic control. However, the sulfonylurea class is associated with weight gain and increased risk of hypoglycemia.

 When used in combination with metformin, saxagliptin, an oral dipeptidyl peptidase-4 inhibitor, has demonstrated improvement in glycemic indices similar to that of other combinations with metformin.

 Yet unlike other agents, saxagliptin has been shown to be weight neutral, with low risk of hypoglycemic events relative to sulfonylureas.

In a 52-week, multicenter, randomized, phase 3b trial, Goke et al

 found that treatment with saxagliptin plus metformin compared with glipizide plus metformin was associated with a significantly smaller proportion of patients with at least 1 hypoglycemic event (13/428 or 3.0% vs 156/430 or 36.3%) and a divergent impact on body weight, with an adjusted mean change from baseline equal to —1.1 kg with saxagliptin versus +1.1 kg with glipizide. In addition, the study showed a significantly smaller rise in the A1C percentage from week 24 to week 52 with saxagliptin versus glipizide (0.001% vs 0.004%, respectively), indicating a sustained glycemic effect beyond week 24. Additional findings from Kahn et al, who evaluated the durability of glycemic control of recently diagnosed T2DM patients receiving glucose-lowering monotherapy, showed glyburide had the highest cumulative incidence of failure compared with rosiglitazone and metformin, defined as a confirmed level of fasting plasma glucose of more than 180 mg/dL at 5 years.

 We hypothesized that saxagliptin may play a role in decreasing morbidity and reducing associated healthcare costs given its favorable glycemic durability and tolerability profile.

The objective of the current analysis was to estimate the long-term cost-effectiveness of saxagliptin compared with sulfonylurea (glipizide) added to metformin in T2DM patients from the US payer perspective.

Patient Population and Treatment Strategies

Patient characteristics were based on US demographic data found in the literature and were also comparable to characteristics of the Goke et al trial population.

 When information on a specific variable was not available, then the Goke et al trial was used as a reference. The model cohort included T2DM patients between the ages of 25 and 64 years whose diabetes was not controlled on metformin alone and who needed add-on treatment to achieve glycemic control. 

 highlights the baseline characteristics of the modeled patients.

Treatment comparators were metformin plus saxagliptin versus metformin plus glipizide as intensive second-line therapy. Patients inadequately controlled on either treatment arm (ie, reaching an A1C threshold level of 7.5%) received rescue therapy with NPH insulin as add-on treatment.

 The effect of A1C in the first year with insulin was assumed to be −1.1%.

 Treatment effects for each regimen, derived from the literature and the Goke et al trial,

 were applied to the simulated cohort of patients within the model. A 4% discontinuation rate of study medications due to adverse events was assumed for both treatment arms

 and patients who discontinued were assumed to enter rescue therapy.

The current analysis is based on a US adaptation of a previously published model.

 The Cardiff Long-Term Cost Utility Model, a stochastic simulation model, was designed to evaluate the impact of new diabetes therapies, based on the United Kingdom Prospective Diabetes Study (UKPDS) 68.

 The model was subsequently adapted to a number of other payer systems (Granstrom O, Bergenheim K, McEwan P, Sennfalt K, Henriksson M. Cost-effectiveness of saxagliptin [Onglyza] in type 2 diabetes in Sweden, unpublished observations, 2009).

 In this model, baseline diabetes-related parameters, disutility values associated with side effects and injection, and cost values were based on US-specific data. UKPDS-based life tables and risk equations for predicting risks of diabetes-related complications were used.

The lifetime horizon was chosen in order to capture all relevant events and related complications associated with diabetes progression, and subjects were simulated in yearly time increments. A 5-year analysis was also conducted to capture the payer perspective and to examine the shorter-term outcomes of interest. Evidence from the Goke et al clinical trial as well as the UKPDS study and published literature were used to model disease progression over time in patients.

 Direct costs and disutility values attributed to hypoglycemia and weight gain were adapted to US Medicare reimbursement values.

Rates of hypoglycemia events were taken from the Goke clinical trial

 and included all hypoglycemia episodes from mild events characterized as “having an awareness of the event but easily tolerated,” moderate events classified as “discomfort enough to cause some interference with usual activity,” and severe events, defined as an “inability to carry on usual activity.”

Because hypoglycemia has a significant impact on a patient’s health-related quality of life during an episode, the disutility

 associated with the event itself was captured in the model. A patient experiencing frequent symptomatic or severe hypoglycemic events may also develop a fear for future events, which is considered in the disutility value (

 In this model, cost of long-term cardiovascular disease as it relates to weight gain is driven by the association between body weight and congestive heart failure. Weight may have additional indirect impact due to changes in low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, fasting insulin, and blood pressure that occurs with weight gain.

Probability of weight gain was obtained from a health maintenance organization (HMO) population,

 and annual weight change values were derived from the saxagliptin clinical trial reported by Goke et al.

 In addition to its direct association with increased cardiovascular risk, weight gain associated with diabetes treatment may also affect patient health-related quality of life, resulting in utility losses. The impact of weight gain on utilities was therefore also considered in the model

 (Table 2). The health related quality of life weight changes were modeled linearly for transparency, where each unit change in body mass index or body weight was associated with identical utility weight.

 A treatment’s durability measure is the length of time at which the A1C effect is sustained. In this analysis, durability was considered as a nonlinear A1C drift based on the divergent A1C durability in the long-term extension study of saxaglipin.

 The constant drift values of 0.208% for the sulfonylurea arm and 0.052% for the saxagliptin arm were applied to the model as an assumption, to project long-term differences between the 2 treatments in the absence of long-term data. Existing evidence also supports the better durability of agents within the dipeptidyl peptidase-4 class over 2 years relative to other drug classes, including glipizide.

 Furthermore, because patients on second-line therapy who reach a predefined A1C threshold of 7.5% are switched to add-on insulin rescue therapy, the analysis also considered the disutility associated with the injection of insulin

 Table 3 provides the direct costs used in the model for diabetes-related events. Direct diabetes-specific costs  included medication acquisition cost, administration devices, and costs associated with the treatment of diabetes-related events such as ischemic heart disease, myocardial infarction, congestive heart failure, stroke, amputation, blindness, and end-stage renal disease. Fatal, nonfatal, and maintenance costs were considered in these cost values.

Treatment costs in the model assumed a $6.11 daily cost

 of branded saxagliptin for the first 10 years, and then assumed a $1.00 cost per day as the best estimate for a generic oral antidiabetic medication based on current market price standards. Daily costs of sulfonylurea and metformin were assumed to be $1.47 and $0.36, respectively. 

 The daily cost of insulin, assuming a 50/50 mix and a 1 mL injection once per day, was set at $9.97

 and included the cost of syringe ($0.12 per syringe).

The clinical study reported mild, moderate, and severe hypoglycemic events.

 Of patients in the glipizide plus metformin group, 1.6% had severe hypoglycemic events compared with none of the patients in the saxagliptin plus metformin group. Other studies have also found low levels of hypoglycemic events in patients on the saxagliptin plus metformin regimen.

 The Medicare National Average Allowance for outpatient cost of $125 was applied only to the 1.6% of patients who experienced severe hypoglycemic events. Because the other events did not require medical assistance, these events were assumed to accrue no additional costs.

 The $246.93 cost of weight gain was based on an HMO population and estimated the total healthcare cost for every 1% increase in weight.

 For each year in which weight gain occurred, this cost was applied.

Indirect costs, which are typically considered to be associated with loss of productivity or activity impairment, were not considered in this model. All costs were inflated to 2009 values using the medical care component of the consumer price index reported by the US Bureau of Labor Statistics.

 Economic and clinical outcomes were discounted at a rate of 3%.

The main differences observed in the Goke clinical trial14 between the metformin plus saxagliptin and metformin plus glipizide arms were in their side effect profiles, with patients in the saxagliptin arm experiencing fewer hypoglycemic events, lower risk of severe hypoglycemic events, and a small weight decrease, whereas the glipizide arm had a higher occurrence of hypoglycemia events and weight gain. 

 presents the results for the base case cost-effectiveness analysis evaluating the impact of metformin plus saxagliptin versus metformin plus sulfonylurea, while considering the disutility associated with weight gain and all hypoglycemia episodes, as well as the cost attributed to weight gain and specifically severe hypoglycemic events.

The number of all hypoglycemia episodes was significantly lower for saxagliptin patients. For the hypothetical cohort of 1000 T2DM patients, compared with patients treated with sulfonylurea, patients treated with saxagliptin avoided 1243 hypoglycemic events in the first 5 years, at an incremental cost of $5703 per hypoglycemic event avoided and 1201 hypoglycemic events at an incremental cost of $2308 per event avoided in the 40-year follow-up. In addition, there were 10.53 cardiovascular events avoided at 40 years, with an incremental cost of $263,355 per event avoided for the saxagliptin-treated patients. The side effect benefi ts attributed to saxagliptin translated into a 5-year quality-adjusted life-year (QALY) gain equal to 0.53 and an incremental cost per QALY gained of $13,366 compared with sulfonylurea plus metformin. The lifetime (40-year) QALY gain was 2.65 per patient at a reduced cost per QALY of $1047, indicating a higher decrement in quality of life over time for the sulfonylurea-treated patients.

In order to test the impact of each input parameter on the results, 1-way sensitivity analyses (in the form of distinct scenarios) and probabilistic sensitivity analyses were performed. 

 outlines the various scenario analyses and the subsequent findings. The scenarios focused on the drivers of the model including the cost/disutility of hypoglycemia and weight gain.

When evaluating sensitivity around the subgroup of hypoglycemic events accruing cost, attributing a cost value only to events requiring medical assistance did not signifi cantly change the outcome. A Medicare National Average Allowance

 for an emergency department visit of $170 was applied to the 0.9% of metformin plus glipizide patients

 requiring medical assistance, resulting in a 5-year cost per QALY of $13,597 and a 40-year cost per QALY of $1513. These results indicate that the differences from the base case analysis may primarily be driven by the disutility associated with weight gain and hypoglycemic events, with the impact of overall costs being more modest.

Including only the cost and disutility associated with hypoglycemia resulted in 5-year and 40-year cost differences of $7664 and $4431, respectively, compared with the base case analysis values of $7094 and $2772. In addition, 1.33 QALYs were added in the long term compared with 0.28 QALYs at 5 years.

We subsequently examined the impact of treatment durability on results. When accounting for a potential delay in switching to insulin rescue therapy, while assuming a −1.1% effect

 on A1C in the first year on insulin, patients on saxagliptin avoided 1409 hypoglycemic events in the long term compared with patients on glipizide, resulting in 1.27 QALYs added and a 40-year incremental cost of $1770 per patient compared with $4497 at 5 years.

When the durability analysis included cost and disutility due to weight gain in addition to hypoglycemia events, patients in the saxagliptin arm avoided 1409 hypoglycemic events and 47.74 cardiovascular events compared with patients on glipizide. In addition, 2.29 QALYs were added, while incremental costs were $144 per patient. Therefore, taking into account saxagliptin’s improved durability and the impact of cost and disutility of hypoglycemic events and weight gain, saxagliptin was found to be a cost-effective option compared with glipizide as an add-on to metformin, at a low cost.

In order to take into account the uncertainty around estimated parameters in the base-case model, a probabilistic sensitivity analysis was conducted on the 40-year model (Table 5). The sensitivity analysis was based on 1000 simulations of the cohort. Inputs that were updated with US-specifi c values (eg, baseline demographics and event probability, cost, and disutility associated with weight gain and hypoglycemia) were varied across their respective bounds (or distributions). However, other variables found in the model were not varied (eg, disutility associated with diabetes-related events). Results showed a mean incremental cost-effectiveness ratio (ICER) of —$1980.08, with saxagliptin plus metformin appearing to be dominant. This may be due to the fact that only select US-specific parameters were varied.

The current model provided long-term cost-effectiveness data for saxagliptin as add-on to metformin versus glipizide in a population of US T2DM patients. The model results indicate that saxagliptin, used in combination with metformin as second-line treatment for T2DM, is a cost-effective treatment option. The benefi t of saxagliptin treatment is largely found in its favorable side effect profi le compared with that of glipizide with respect to weight gain and hypoglycemic events. Such side effects can lead to additional treatment complications such as low adherence, an important factor in successful treatment. For example, a retrospective cohort study conducted by Rozenfeld et al demonstrated that a 10% increase in oral medication adherence was associated with a 0.1% reduction in A1C level.

 Over time, however, as more patients on both treatments go onto insulin rescue therapy, the attenuated benefi t results in a diminishing number of hypoglycemic events avoided. The long-term gains in QALY measures and improved cost per QALY are more sensitive to disutility associated with undesirable side effects and modestly sensitive to cost allocation.

Since drug acquisition costs are higher with saxagliptin plus metformin compared with sulfonylurea plus metformin, total costs for the saxagliptin patients tend to be slightly higher; however, improvement in QALYs and mitigation of the increased costs from reduced diabetes-related events compared with sulfonylurea plus metformin cause the cost-effectiveness ratio to decline as the model time horizon is lengthened.

 has shown that the benefits of incremental glycemic control may be associated with significant increases in cost of T2DM treatment. Sinha et al,

 who conducted an economic analysis in which they compared both exenatide and sitagliptin with glyburide as second-line therapies to metformin, concluded that exenatide and sitagliptin are not cost-effective. Comparison of the 2 studies showed that differences in the ICER values were primarily driven by the assumptions around the cost of treatment and the disutility values attributed to hypoglycemia. The present model considered a $6.11 cost per day for saxagliptin. After 10 years of therapy, the generic price was then considered at a value of $1 per day versus the average $6.51 per day used in the Sinha et al study. Sinha and colleagues also used a value of 0.0064 disutility due to hypoglycemia compared with 0.08 in this analysis. The Sinha et al study is limited in consideration of the potential cost/utility benefits of therapeutic side effects. In addition, disutility values used in this study

 differ from those used in other utility studies such as that of Currie et al,

 because of factors such as the inclusion criteria for the patient population, varying definitions for hypoglycemia severity, and the period for which hypoglycemia was evaluated. When assessing ICER values of various therapies, the cost and disutility attributed to weight gain, hypoglycemia, and the relative durability of a given therapy’s treatment effect can have a substantial impact on the cost-effectiveness results and should be considered.

As with all economic analyses, there are limitations to the current modeling analysis. Given the younger age range of the population, using Medicare costs may be a less accurate representation of the potential costs. However, the Medicare fee schedule is a well-recognized benchmark for cost data in the United States

 and likely provides a conservative estimate of costs associated with both treatments. Furthermore, using private payer costs may change the overall costs, but would not impact the cost difference.

The present study excluded the cost for patients with mild or moderate hypoglycemia events and included the cost of severe hypoglycemia events as an outpatient cost, in order to make a moderate cost assumption. Considering that inpatient hospitalization is more costly than outpatient treatment, limiting the analysis to outpatient cost is likely a conservative estimation of the cost savings attributed to avoiding hypoglycemic events (Granstrom O, Bergenheim K, McEwan P, Sennfalt K, Henriksson M. Cost-effectiveness of saxagliptin [Onglyza] in type 2 diabetes in Sweden, unpublished observations, 2009).

In addition, patients often do not seek treatment for all hypoglycemia events, which could lead to an underestimation of costs in the study due to lack of event reporting. Further underestimation of cost savings may result from the exclusion of indirect costs such as those attributable to patient education, support, and monitoring.

Finally, all events can affect patient lives through both physical discomfort and psychological distress such as fear of hypoglycemia. A potential limitation may be overestimating the impact of hypoglycemia events on disutility, since fear of hypoglycemia is considered in the disutility values used for this analysis.

Saxagliptin in combination with metformin provides a durable, well-tolerated, and cost-effective treatment option

 for T2DM and may address some of the unmet medical need in diabetes treatment attributable to undesirable side effects of hypoglycemia and weight gain. Additionally, enhanced glycemic control decreases the risk and prevents the potential high costs of future hypoglycemic events compared with glipizide.

While most diabetes treatments demonstrate comparable glycemic control, the variable side effect profiles of diabetes treatments may have an impact on patients’ health-related quality of life. These side effects can also lead to differential long-term costs to the US healthcare system, thereby emphasizing the importance of the lifetime analysis. Therefore, it is important to take into consideration all aspects of new therapeutic agents to determine the most appropriate treatment for each individual patient.

Saxagliptin, in combination with metformin, provides a durable, well-tolerated, and cost-effective treatment option for patients with type 2 diabetes mellitus, from a US payer perspective.