Cary P. Gross, MD

: To characterize clinical trials supporting FDA approval of new cancer therapeutics and of supplementary indications and off-label indication inclusions on DRUGDEX, a Medicare-referenced compendium, each of which can guarantee reimbursement by Medicare.

: Cross-sectional study using publicly available documents for cancer therapeutics initially approved by the FDA from 2005-2012. Trials supporting approval were identified, characterizing randomization, blinding, comparator, end point, number of patients, and duration.

: Between 2005-2012, FDA approved 37 new cancer therapeutics for 39 indications, based on 50 supportive trials. These therapeutics subsequently received 21 FDA supplementary indication approvals and 16 DRUGDEX off-label indication inclusions, based on 22 and 37 supportive trials, respectively. Of 109 total trials, 53.2% (95% CI, 43.9%-62.3%), 28.4% (95% CI, 20.8%-37.5%), and 53.2% (95% CI, 43.9%-62.3%) were randomized, double-blinded, and used a comparator, respectively. There was a median of 383 (interquartile range, 178 to 623) patients among aggregated supportive trials, whereas 38.2% (95% CI, 28.1%-50.6%), 69.7% (95% CI, 58.7%-78.9%), and 18.4% (95% CI, 11.3%-28.6%) were supported by at least 1 trial that lasted ≥6 months, used a comparator group, or used overall survival as a primary end point, respectively. There were few substantive differences in the aggregated clinical trial evidence supporting FDA new drug and supplementary indication approvals and DRUGDEX off-label indication inclusions.

: The evidence supporting DRUGDEX off-label indications was similar to that used for FDA approval of new and supplementary cancer therapeutic indications, all of which had limitations for informing clinical decision making.

Understanding the evidence supporting new drug and supplementary indication approvals by the FDA is important for cancer care. Because many cancer patients face life-threatening disease for which there is not necessarily an effective treatment, anecdotal evidence can exert a strong influence on clinical decisions,

 making rigorous and objective evaluation by the FDA essential. The FDA approves the use of cancer therapeutics for specific medical indications only when “adequate and well-controlled investigations” can demonstrate the therapeutic’s safety and efficacy.

 Although physicians can use therapeutics “off-label”—that is, for an indication not specifically approved by the FDA&mdash;FDA approval guarantees an indication’s reimbursement by CMS.

 Reimbursement, in turn, may influence how cancer therapeutics are used in an era of increasingly expensive cancer treatment.

For cancer therapeutics, there are 2 ways by which pharmaceutical manufacturers can ensure reimbursement for indications beyond those initially approved by the FDA. First, manufacturers can apply to the FDA for a second (or third, and so on) “supplementary indication” by providing supportive clinical evidence of a therapeutic’s efficacy and safety when used for the new indication. Second, manufacturers can seek the inclusion of off-label indications on Medicare-referenced compendia, which are comprehensive drug guides that list off-label indications deemed appropriate based on expert review of biomedical literature. The Omnibus Budget Reconciliation Act of 1993 required that CMS reimburse off-label cancer therapeutic use endorsed by these compendia.6 Because some private insurers are required to follow the same compendia per state legislation,

 inclusion of an off-label indication on compendia can secure coverage without FDA indication approval.

Concerns have been raised regarding the quality of clinical evidence supporting the FDA approval of new cancer therapeutics,

 particularly the reliance on surrogate markers of disease as opposed to clinical outcomes.

 One prior study found that new drug and supplementary indication approvals for cancer drugs were supported by trials focused on clinical outcomes with similar frequency, but that trials supporting supplementary indication approvals more frequently used active comparators (ie, compared the new therapeutic with an available therapeutic alternative).

 However, no studies have comprehensively assessed the quality of evidence supporting the inclusion of off-label indications on compendia for cancer therapeutics, even though a 2009 review by the Agency of Healthcare Research and Quality raised concerns regarding whether compendia used consistent standards and methodologies.

 Our objective was to characterize and compare the clinical trials supporting new drug and supplementary indication approvals by the FDA and off-label indication inclusion on DRUGDEX, a Medicare-referenced compendium, focusing on trial number, size, design, duration, and end points. We used DRUGDEX, the compendium managed by Micromedex, because all of its supporting documentation is publicly available, it may list the greatest number of off-label indications among Medicare-referenced compendia,

 and its classification of indications as on-label versus off-label facilitated comparison with the FDA approval process.

Drugs@FDA is a publicly accessible database available through the FDA’s website that lists regulatory actions, such as approvals and drug labeling changes, for all currently approved prescription therapeutics.

 The database also contains documents relevant to each therapeutic’s regulatory history, including approval letters and other correspondence, medical reviews for new drug applications and biologic license applications, and current and historical drug labels. Clinical evidence supporting each FDA-approved indication is summarized on these labels. The Drugs@FDA database was downloaded on March 6, 2015, and October 12, 2015.

Micromedex DRUGDEX® (Truven Health Analytics) is 1 of 5 compendia currently recognized by the CMS under the Omnibus Budget Reconciliation Act.

 The most recent version of the compendium is available online by subscription. Data regarding revision history were not available on request. Each therapeutic’s monograph in DRUGDEX contains information on dosing, pharmacokinetics, safety, and clinical applications. Clinical applications include information on both FDA-approved and off-label indications, with a description of supporting evidence reviewed. DRUGDEX monographs were downloaded on March 9, 2015, and October 12, 2015.

We limited our sample to novel therapeutics originally approved by the FDA between January 1, 2005, and December 31, 2012, for the treatment of cancer, as identified in a previous study (Figure).

 This time period allows at least 2 (and up to 10) years of follow-up for therapies to acquire supplementary indications and DRUGDEX off-label indications. Our sample excluded generic drugs, reformulations, and nontherapeutic agents, as well as non-anticancer agents (eg, plerixafor or tbo-filgrastim). Each therapeutic was categorized by original FDA approval year and as a pharmacologic or biologic agent.

Identifying Supplementary FDA Indications

We searched Drugs@FDA to identify supplementary indications for adults approved through March 6, 2015, and updated our search on October 12, 2015. Supplementary indications were identified by searching for regulatory decisions modifying the “Indication and Usage” section of the drug label. We included both supplementary indications approved through an additional new drug application or biologic license application, as well as those approved through an “efficacy supplement” to the therapeutic’s original new drug application or biologic license application.

 We excluded regulatory decisions that limited the scope of prior indications, or qualified or reworded a prior indication without significantly changing its scope. We excluded supplementary indications that only expanded a therapeutic’s use to pediatric patients, to facilitate comparison against off-label indications reimbursed by Medicare due to their inclusion on DRUGDEX.

Identifying Off-Label Indications in DRUGDEX

We last searched DRUGDEX on October 12, 2015, to identify off-label indications currently listed with a Class I, IIa, or IIb strength of recommendation or efficacy, per CMS criteria for what constitutes a “medically accepted indication.”

We characterized each identified indication by approval type (FDA new drug, FDA supplementary, or DRUGDEX off-label). Indications were classified into “hematologic,” “solid tumor, curative intent,” and “solid tumor, noncurative intent.” “Noncurative intent” refers to indications that explicitly mention metastatic or advanced disease, salvage therapy, second-line use or use after other therapies, or failure of prior therapies (eg, disease that is refractory, resistant, relapsed, or progressive after prior therapy). We also identified whether FDA supplementary and DRUGDEX off-label indications treated the same target cancer type as the original FDA indication.

Identification of Evidence Supporting Therapeutic Efficacy

We identified the clinical evidence of therapeutic efficacy supporting FDA new drug and supplementary approvals and DRUGDEX off-label indications. For FDA new drug approvals, we used medical reviews to identify “pivotal efficacy trials” supporting the indication’s approval, as described in a previous study.

 For FDA supplementary indications, medical review documents were generally not available. Instead, we included all trials listed on FDA drug labels issued at the time of an indication’s approval as supportive evidence for that indication. Per FDA guidance, labels should include “clinical studies that provide primary support for effectiveness…provide other important information about a drug’s effectiveness…and prospectively evaluate important safety end points.”

 To obtain further details for trials listed on labels, we performed literature searches to identify associated publications. For DRUGDEX off-label indications, supportive evidence and associated publications are summarized online on each therapeutic’s monograph. We included all trials listed on the monograph as evidence of clinical efficacy, but excluded retrospective studies, review papers, and trials focused only on safety. When multiple cited publications referred to the same trial, we counted it as only 1 trial.

Each supportive trial was categorized by its use of randomization, double-blinding, and comparator group. Trials using “active comparators” compared the efficacy of the study therapeutic against an available therapeutic alternative; trials using “placebo comparators” compared the study therapeutic against placebo; and trials with “no comparator” included single-arm trials and multi-arm trials comparing the study therapeutic to itself only. We classified primary end points as “overall survival” or “surrogate marker of disease.” Using a framework based on prior literature and an Institute of Medicine report,

 surrogate markers of disease included measures of disease progression and other radiologic or pathologic markers of response. We also categorized trials by the number of overall patients and those assigned to the intervention group. We recorded the median duration of treatment for each trial, calculated as the time between when the first dose of the drug was administered and when the primary end point was measured. Median duration of exposure was used as a proxy for trial duration in trials which measured time to outcomes (such as overall survival or time to progression). When the trial had multiple arms, we calculated a weighted mean of the median durations of each arm. Two investigators (NSD, JSR) performed the abstraction for FDA new drug approvals, while a third (KWS) performed the abstraction for supplementary FDA approvals and DRUGDEX off-label indications. Any abstraction for which there was uncertainty was reviewed with 2 investigators (NSD, JSR) to establish consensus.

Using descriptive statistics, we characterized the overall sample of supportive trials identified in our study. Next, we used descriptive statistics to characterize features of trials aggregated at the indication level (ie, summarizing all trials used to support each indication). We then used χ2 or Kruskal-Wallis tests as appropriate to examine differences among supportive trials and indications. In addition, we calculated the median time from the original FDA new drug approval to the approval of the first FDA supplementary indication; dates for off-label indication endorsement were not available within DRUGDEX. Analyses were performed using Microsoft Excel 2011 and JMP version 12 (SAS Institute, Inc; Cary, North Carolina). All statistical tests were 2-tailed and used a type I error rate of 0.01 to account for multiple comparisons across 5 therapeutic agent and indication characteristics.

Between 2005 and 2012, the FDA approved 37 new cancer therapeutics, of which 21 (56.8%) were pharmacologic agents and 16 (43.2%) were biologic agents (Table 1). These 37 therapeutics were originally approved for 39 indications. Fifteen (38.5%) and 24 (61.5%) of the indications were for hematologic malignancies and solid tumors, respectively, and all 24 solid tumor indications were classified as noncurative intent.

Of the 37 novel therapeutics, 17 (45.9%) subsequently received an FDA supplementary indication approval, for a total of 21 supplementary indications (Supplementary Table). Seven supplementary indications (33.3%) were for hematologic malignancies, while 14 (66.7%) were for solid tumors, and 11 of these solid tumor indications were classified as noncurative intent. Ten (47.6%) treated the same cancer type as the original FDA indication. Of these, 8 (80.0%) expanded treatment to an earlier line of therapy or changed the accompanying regimen. One of the 10 (10.0%) expanded the use of pertuzumab to the neoadjuvant setting for breast cancer, while another 1 (10.0%) expanded the use of romidepsin from cutaneous to peripheral T-cell lymphoma. A median of 920 days (interquartile range [IQR], 583-1521) passed between the original FDA new drug approval and the approval of the first supplementary indication.

Inclusion of Off-Label Indications in DRUGDEX

Of the 37 novel therapeutics, 10 (27.0%) had off-label indications included on DRUGDEX, for a total of 16 off-label indications (Supplementary Table). Six off-label indications (37.5%) were for hematologic malignancies, while 10 (62.5%) were for solid tumors, and all 10 solid tumor indications were classified as noncurative intent. Six (37.5%) treated the same cancer type as the original FDA indication. Of these 6 indications, 4 (66.6%) changed the line of therapy and/or accompanying regimen, 1 (16.6%) expanded the use of nilotinib to blastic phase chronic myelogenous leukemia (in addition to chronic and accelerated phase disease), and 1 (16.6%) expanded the use of lapatinib to inflammatory breast cancer.

Characteristics of Supportive Trials by Approval Type

In total, 50 trials supported new drug approvals, 22 trials supported FDA supplementary indication approvals, and 37 trials supported DRUGDEX off-label indications (Table 2). Of the 109 total trials identified, 53.2% (95% CI, 43.9%-62.3%) were randomized, 28.4% (95% CI, 20.8%-37.5%) were double-blinded, 53.2% (95% CI, 43.9%-62.3%) used any comparator (either active or placebo), and 19.3% (95% CI, 12.9%-27.7%) used an active comparator. Use of randomization differed by approval type (P = .01), with fewer trials supporting FDA new drug approvals and DRUGDEX inclusions using randomization than trials supporting FDA supplementary indication approvals. Similarly, use of double-blinding and any comparator differed by approval type (P = .009 and P = .01, respectively), with fewer trials supporting FDA new drug approvals and DRUGDEX inclusions using double-blinding and any comparator than trials supporting FDA supplementary indication approvals, although there was no difference in use of an active comparator (P = .57). Surrogate markers were used in 87.2% (95% CI, 79.6%-92.2%) of trials, which did not vary by approval type (P = .22).

There were significant differences in the number of total patients and intervention patients enrolled in trials supporting FDA new drug approvals, FDA supplementary drug approvals, and DRUGDEX off-label indications (total patients, 264, 414, and 86, respectively; intervention patients, 191, 214, and 63, respectively; both P <.001; Table 3). Overall, trials had a median length of 18.4 weeks (IQR, 12.4-31.6), and 30.3% of trials (95% CI, 22.4%-39.5%) lasted more than 6 months. Neither measure of trial duration varied by approval type (P = .32 and P = .06, respectively).

Characterization of Aggregate Trial Features Supporting Approved Indications, by Approval Type

FDA new drug approvals, FDA supplementary indication approvals, and DRUGDEX off-label indication inclusions were supported by a median of 1.0 trial (IQR, 1.0-1.0), 1.0 trial (IQR, 1.0-1.0), and 2.0 trials (IQR, 1.0-3.0) per indication, respectively (Table 4). All indications had at least 1 supportive trial. FDA new drug and supplementary indication approvals cited 1 or 2 trials each, with the exception of the original approval of dasatanib, which cited 6. DRUGDEX off-label indication inclusions cited a median of 2 trials (IQR, 1-3), with a maximum of 7 trials for the first-line use of bendamustine for non-Hodgkin lymphoma.

Indications were supported by total and intervention group patient populations of 383 (IQR, 178-623) and 239 (IQR, 153-397) across aggregated supportive trials, neither of which varied by approval type (P = .47 and .31, respectively). Only 38.2% (95% CI, 28.1%-50.6%) and 6.6% of indications (95% CI, 2.8%-14.5%) were supported by trials lasting more than 6 and 12 months, respectively. This did not vary by approval type (P = .95 and .81, respectively). Patient-time exposure among aggregated supportive trials was 8516 patient-weeks (IQR, 2645-15,744), which did not vary by approval type, either (P = .14).

While 69.7% of indications (95% CI, 58.7%-78.9%) were supported by a trial with any comparator (either active or placebo), only 26.3% of indications (95% CI, 17.7%-37.2%) were supported by a trial with an active comparator. Neither use of any nor an active comparator varied by approval type (P = .09 and .04, respectively). Only 18.4% of indications (95% CI, 11.3%-28.6%) were supported by a trial using overall survival as a primary end point: 23.1% (95% CI, 12.6%-38.3%) of FDA new drug approvals, 14.3% (95% CI, 5.0%-34.6%) of FDA supplementary indication approvals, and 12.5% (95% CI, 3.5%-36.0%) of DRUGDEX off-label indications. This did not vary by approval type (P = .56). No hematologic indications were supported by a trial using overall survival as a primary end point.

Our study characterized and compared the clinical trial evidence underlying the original FDA approval of new cancer therapeutics, the subsequent FDA approval of supplementary indications for these therapeutics, and the inclusion of off-label indications for these therapeutics on the Medicare-referenced compendium, DRUGDEX. Some may consider the quality of evidence supporting cancer therapeutic approvals to have limitations for informing clinical decision making, as only 70% of indications were supported by a trial using an active or placebo comparator, 30% by a trial lasting more than 6 months, and 18% by a trial using overall survival as a primary end point. However, when viewed in aggregate, new drug and supplementary indication approvals by the FDA and off-label indication inclusions by DRUGDEX were generally supported by similar bodies of clinical trial evidence.

At the individual trial level, we found some differences across approval types, potentially suggesting that the trials supporting FDA supplementary indication approvals were of higher quality, as they had the highest rates of randomization, double-blinding, and use of any comparator, and had the largest patient populations and duration of study. While these differences were not significant on aggregate, it does suggest that once a cancer therapeutic is originally approved for use, the FDA evidentiary standards for supplementary indication approvals are higher than those used by DRUGDEX for off-label indication inclusions. Further studies should examine the standards used by other compendia. If their standards are lower, from an evidentiary standpoint, the decision to allow experts outside of FDA to determine indications for which Medicare will pay for therapy may be eroding incentives to produce rigorous evidence that supports FDA supplementary indication approvals.

The strength of clinical evidence supporting these indications has important implications for informing cancer care clinical decision making. Although oncologists routinely use cancer therapeutics for indications outside those approved by the FDA or included on compendia,

 the formal endorsement of new indications through these pathways provides an additional expert review of safety and efficacy, and defines the prevailing standard of cancer care by helping secure reimbursement. The vast majority of indications in our study were only supported by trials using surrogate markers of disease, as opposed to overall survival, even though recent research has cast doubt on the mortality benefit of cancer therapeutics approved on the basis of surrogate markers.

 Moreover, no trial in our sample used patient-reported outcomes as a primary end point. Our finding that nearly one-third of indications lacked support by a trial using a comparator group further highlights weaknesses in the quality of trials used as supportive evidence. Nearly three-fourths of indications also lacked active comparator data, raising questions regarding the comparative effectiveness of these treatments, though some may have targeted disease for which no therapeutic alternative exists. FDA and Medicare should consider whether supplementary indication approvals and compendia off-label indication inclusions should be permitted on the basis of evidence that is focused on surrogate markers of disease or without active or placebo comparator; given that these medications are already approved for use by patients, higher standards could be required.

The development of new indications for already-approved therapeutics, whether it be through the approval of supplementary indications by the FDA or inclusion of off-label indications on compendia, may complement the costly process of new drug development, particularly for advanced or rare diseases. Indeed, more than half of the new indications in our study expanded therapeutics’ use to a different target cancer type, and most of them treated advanced disease. As clinical trials begin to systematically match already-approved therapeutics to different target cancers based on tumor genomic abnormalities,

 supplementary indications and compendia off-label indications will likely play an increasingly important role in defining the standard of care. However, both the FDA and compendia review processes, as well as the reimbursement policies referencing their decisions, must balance the urgency of identifying new treatments with the need to ensure drug safety and efficacy. Prior work has highlighted the FDA’s flexible standards for the approval of new cancer therapeutics.

 This flexibility may be beneficial when the cancers being treated are life-threatening and lack other effective treatment options, but it also invites criticism that the evidentiary threshold for approval is too low, especially given concerns that some new and expensive treatments offer only marginal benefits over existing therapies.

 Our findings suggest that similar uncertainties may apply to FDA supplementary indications and compendia off-label indications.

Ultimately, it is vital that the pharmaceutical industry produces data that matter for patients. Stringent postmarketing requirements could theoretically ensure that the industry conducts rigorous studies evaluating whether FDA-approved indications improve survival.

 Unfortunately, recent studies suggest that these requirements are not consistently enforced and completed on a timely basis,

 that one-third of FDA cancer therapeutic approvals based on surrogate markers have not been followed up with a study examining overall survival,

 and that less than 5% of FDA approvals based on surrogate markers, across all indications, have not been followed up with a randomized, double-blind study demonstrating improved clinical outcomes.

 Furthermore, no external regulatory mechanisms currently exist to evaluate off-label therapeutic use endorsed by compendia. Some authors have proposed expanded use of “coverage-under-evidence development” as a solution,

 in which CMS reimburses off-label therapeutic use on the condition that treated patients are enrolled into a clinical trial evaluating the therapeutic’s safety and efficacy.

Our study has several limitations. First, we only characterized the inclusion of off-label indications on DRUGDEX, 1 of several CMS-referenced compendia. While the National Cancer Comprehensive Network (NCCN) compendium probably exerts greater influence on clinical decision-making, it does not clearly label endorsed indications as off-label or list any supportive evidence, making direct comparison against FDA approvals difficult. Although the NCCN guidelines (upon which the compendium is based) may cite relevant studies in its discussion section, study citation is not uniform and systematic, precluding its use for this study.

Second, we identified supportive evidence differently for each approval type, due to the varying regulatory information available: We used medical review documents for FDA new drug approvals, drug labels for FDA supplementary approvals (as has been done previously

), and trials cited by DRUGDEX online for endorsed off-label indications. We likely identified the strongest evidence considered for FDA approvals, as we found a median of 1 supportive trial for both new drug and supplementary indications. However, we may have overstated DRUGDEX’s evidentiary standards, as we had access to the supportive evidence currently listed online, but not the evidence listed at the time an off-label indication was first endorsed.

Third, we did not assess measures of evidentiary quality other than trial design, such as effect sizes or safety, nor did we assess factors that might reasonably change the evidentiary threshold for approval, such as the severity of the target cancer and the availability of other effective treatments.

Articles

Evidentiary standards for new or supplementary cancer therapeutic indication approvals by the FDA are consistent with off-label indication inclusions on Medicare-referenced compendia.

Cancer Therapeutic Clinical Trials Supporting FDA Approval and Compendia Inclusion

: Although tyrosine kinase inhibitor (TKI) agents have significantly changed clinical practice for metastatic renal cell carcinoma (RCC), the economic burden of targeted agents remains poorly described. Thus, we sought to investigate the economic burden from the total costs, private insurance reimbursement, and out-of-pocket costs for oral TKI agents among metastatic RCC patients.

: Using a large private health insurance database, we identified patients who were diagnosed with metastatic RCC and received sorafenib or sunitinib from 2005 to 2010 in the United States. The primary outcomes were total costs, amount reimbursed, and out-of-pocket costs for 6 months of treatment with a TKI agent.

Methods: General estimated equations were used to determine the adjusted costs and reimbursement adjusting for patient age, gender, Elixhauser comorbidity score, and geographic region.

: Among 506 patients in the study sample, 389 (76.9%) patients received sunitinib and 117 (23.1%) patients received sorafenib. On multivariable analysis, sunitinib and sorafenib had similar adjusted total costs ($19,233 vs $19,030; P = .11) and amount reimbursed ($18,443 vs $18,314; P = .21). Patients were also responsible for similar out-of-pocket costs for 6 months of sunitinib or sorafenib ($1129 vs $1142; P = .67), which accounted for 4% of the total costs for both TKIs. From 2005 to 2010, there were minimal changes to each cost outcome over time.

: Metastatic RCC patients experienced high out-of-pocket costs for oral targeted agents from 2005-2010. Our findings have important implications since patients are becoming increasingly responsible for higher co-payments. High out-of-pocket costs may make the accessibility of targeted agents challenging, or may introduce economic hardship to metastatic RCC patients.

It is estimated that prescription drug expenditures are responsible for more than approximately $260 billion annually, or almost 10% of the $2.8 trillion spent on US healthcare each year.

 To limit the rate of growth in US healthcare spending, the Affordable Care Act (ACA) has, in part, shifted some of the cost burden to patients by increasing deductibles or co-pays for hospital services and prescription drugs, respectively.

 With respect to prescription drugs, for example, private health insurances have begun to introduce tiered plans to encourage lower growth in spending by limiting access to or raising out-of-pocket costs for certain expensive medications.

Against this backdrop, in the United States, more than 60,000 patients are diagnosed with kidney cancer each year. Of these, many present with metastatic disease, or recur or progress following primary treatment.

 Therefore, it is not surprising that the economic burden from renal cell carcinoma (RCC) is significant, and estimated to cost as much as $5.2 billion annually.

 One contributor to the high costs in healthcare is the development and rapid dissemination of targeted agents for RCC. Tyrosine kinase inhibitors (TKIs), for example, are expensive oral drugs that have now become the standard of care as first-line agents for metastatic RCC, based on clinical practice guidelines and several clinical trials demonstrating modest gains in overall survival compared with traditional cytokine therapy.

 The total costs of sunitinib and sorafenib, both commonly used first-line oral TKI agents in the clinical setting of metastatic RCC, have been estimated to range from $6990 to $7945 for a 1-month supply.

It is essential to recognize that, among metastatic RCC patients, key knowledge gaps may exist about the economic burden presented by oral TKI agents. More specifically, many patients are largely unaware of the costs attributable to TKI agents prior to commencing treatment. However, the out-of-pocket costs for oral TKI agents must be accurately determined to properly assess potential economic burden and to ensure that these medications are financially accessible to patients diagnosed with metastatic RCC. Therefore, using a large private US health insurance claims database, we sought to investigate the economic burden from total costs, health insurance reimbursement, and out-of-pocket costs among patients treated for metastatic RCC

Data for all patients diagnosed with metastatic RCC were abstracted from the IMS LifeLink Health Plan Claims Database, which was provided by IMS Health. The database covers approximately 55 million privately insured patients and more than 80 health plans in the United States. It includes all claims and encounters from outpatient office visits, hospitalizations and operative procedures, ambulatory procedures, emergency department visits, and home health services. In addition, the IMS database accurately captures data on the total costs of each outpatient medication as well as payment from private insurance for each prescription.

In this study, patients diagnosed between 2005 and 2010 were identified from the IMS data. We adapted a validated methodology for case ascertainment of RCC patients with receipt of TKI agents, with or without cytoreductive nephrectomy, from a claims-based algorithm using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes and Current Procedural Terminology-4 (CPT-4) procedure codes.

To limit the patient sample to those presenting with metastatic RCC, we identified all patients with ICD-9-CM codes with 2 or more claims for a primary diagnosis of kidney cancer (189.0 and 189.9), and a concomitant claim for metastatic disease to the secondary malignant neoplasm of lymph nodes (196.x), respiratory system (197.0, 197.1, 197.2, 197.3), digestive system (197.4, 197.5, 197.8), retroperitoneum and peritoneum (197.6), liver (197.7), other urinary organs (198.1), skin (198.2), brain or spinal cord (198.3, 198.4), bone (198.5) and adrenal (198.7), or disseminated disease (199.x). In addition, we restricted inclusion to those patients with claims of kidney cancer and metastatic disease within 3 months of each other. Furthermore, we only included patients with at least 1 year of continuous enrollment prior to and 6 months after the initial ICD-9-CM claim of kidney cancer or metastatic disease.

Based on this claims-based methodology, we initially identified 7371 patients aged 18 to 46 years with metastatic RCC. After removing patients without at least 1 year of continuous enrollment prior to diagnosis and 6 months post, 3195 patients remained. We further restricted our analytic cohort to 506 patients with outpatient claims for sunitinib or sorafenib within the 6 months after diagnosis.

Clinical information included as covariates were patient age at the time of diagnosis, gender, comorbidities, receipt of surgery, geographic region of care (east, midwest, south, or west), and year of diagnosis. Secondary diagnosis codes enumerated an Elixhauser comorbidity score for each patient, using all claims 1 year prior to the diagnosis of metastatic kidney cancer.

To identify those patients who underwent surgery, hospitalization claims were used to identify patients who underwent open or laparoscopic radical nephrectomy by ICD-9-CM (55.5, 55.51, 55.52, 55.54) or CPT-4 (50220, 50230, 50236, 50546, 50548) codes, or partial nephrectomy by ICD-9-CM (55.4) or CPT-4 (50240) codes. For first-line systemic agents for metastatic RCC, National Drug Code or Healthcare Common Procedure Coding System identifiers from the outpatient and pharmacy claims were used to identify sunitinib or sorafenib. We also restricted the patient sample to those with outpatient pharmacy claims for sunitinib or sorafenib within 6 months of the diagnosis of metastatic kidney cancer.

The primary outcome of this study was patient out-of-pocket costs for sorafenib and sunitinib at 6 months following the diagnosis of metastatic kidney cancer. Evaluating each cost or reimbursement outcome was based on a 6-month period. This period represented, for sunitinib, 3 cycles of treatment, in which a single cycle was consistent with 4 weeks of therapy followed by 2 weeks off therapy, or, for sorafenib, 6 months of continuous treatment. Secondary outcomes were the total costs and the amount reimbursed by private health insurance plans for each TKI agent at the same time interval. To define out-of-pocket costs, we calculated the differences between the total costs of each TKI agent and the amount reimbursed across all private health insurance plans. The total costs, amount reimbursed, and out-of-pocket costs were adjusted to 2010 US dollars using the National Income and Product Accounts table of the Gross Domestic Product price index.

Bivariate associations of patient variables for all targeted agents were tested by Pearson’s χ2 test or Fisher's exact test, where appropriate. We then fit generalized estimating equation (GEE) models to ascertain the differences in unadjusted and adjusted total costs, amount reimbursed, and out-of-pocket costs at 6 months for each oral TKI agent of interest. For the multivariable analysis, the GEE model adjusted for patient age at the time of diagnosis, gender, comorbidities, and geographic region of care (east, midwest, south, or west). To account for the skewed distribution of costs, we specified a gamma distribution and log link in the GEE models and determined adjusted total costs, amount reimbursed, and out-of-pocket cost.

 Nonparametric bootstrapping was used to estimate the 95% CIs for each cost outcome.

 SAS version 9.3 (SAS Institute Inc, Cary, North Carolina) was used to perform all statistical analyses. A 2-sided P-value of ≤.05 was used to determine statistical significance.

Among the 506 patients diagnosed with metastatic RCC from 2005 to 2010, 389 (76.9%) patients received sunitinib and 117 (23.1%) patients received sorafenib. Table 1 provides the clinical and pathologic characteristics among our privately insured cohort. Overall, approximately a quarter of patients underwent nephrectomy (23.5%). Minimal differences existed between the sunitinib or sorafenib groups, with the exception of geographic region: sunitinib was used more commonly in the midwest and east, while sorafenib was more commonly used in the south and west. At 6 months, the unadjusted total costs were significantly higher for patients receiving sunitinib compared with sorafenib ($20,077 vs $16,153; P <.0001). While private health insurance reimbursed more for sunitinib compared with sorafenib ($19,332 vs $15,328; P <.0001), patients ultimately paid more out-of-pocket costs for sunitinib than sorafenib at 6 months ($1213 vs $1023; P <.0001).

After adjusting for patient characteristics and geographic region, our analysis demonstrated that the total costs, amount reimbursed, and out-of-pocket costs were substantial, but similar for sunitinib and sorafenib at 6 months on multivariable analysis (Table 2). For example, the adjusted total costs for sunitinib and sorafenib at 6 months were $19,233 and $19,030, respectively (P = .11). Although private health insurances covered an equally substantial amount for sunitinib and sorafenib at 6 months on multivariable analysis ($18,443 vs $18,314; P = .21), patients were responsible for covering 4% of out-of-pocket costs, amounting to $1129 and $1142, respectively (P = .67). In addition, the adjusted total costs and amount reimbursed changed minimally over time for sunitinib (Figure 1A) and sorafenib (Figure 1B). Likewise, the out-of-pocket costs remained unchanged for each agent over time, with patients responsible for approximately $900 to $1300 for 6 months of sunitinib or sorafenib, respectively (Figure 2).

In this study, we sought to comprehensively investigate the costs attributable to oral TKI agents among patients with metastatic RCC. The purpose of this study is salient considering their rapid adoption in the targeted therapy era, and that clinical practice guidelines now recommend many of these agents as first-line therapy.

Our study also has several key findings regarding the economic implications of targeted agents at a time when patients are likely to be increasingly responsible in sharing healthcare costs. First, to the best of our knowledge, our study is the first to demonstrate that patients likely face a significant economic burden regarding out-of-pocket costs for oral TKIs in the clinical setting of metastatic RCC.

We found that metastatic RCC patients pay, on average, $1100 out-of-pocket for a total of 3 cycles of either sunitinib or sorafenib. With previous studies reporting high and variable lifetime costs and cost-effectiveness associated with the use of TKI agents (ranging from $8537-$72,254 and $49,595-$272,419 per quality-adjusted life-year [QALY] gained), our study suggests, in combination with previous evidence, that these high out-of-pocket costs may affect the oncologic care patients receive.

 For instance, Dusetzina et al reviewed the costs of imatinib in treatment of chronic myeloid leukemia (CML) from 2002 to 2011, and demonstrated increasing monthly out-of-pocket costs per patient from $2798 to $4892 during this time frame.

Significantly, patients in the top quintile for co-payments for imatinib were associated with higher adjusted relative risk (RR) for nonadherence (RR, 1.42; 95% CI, 1.19-1.69), which was defined as a CML patient who was in possession of less than 80% of the oral TKI they should have had over a 180-day period. In addition, a recent survey of cancer patients demonstrated that more than two-thirds would like to discuss the out-of-pocket costs of medical care before starting treatment, and almost 40% reported paying for cancer care as difficult or that they faced financial hardship.24 Although the IMS Life-Link data do not capture the variables regarding adherence to oral TKI agents or patient attitudes about the associated costs, our results suggest that these high out-of-pocket costs for oral TKI agents among patients with metastatic RCC certainly raise the possibility of marked financial constraints or of harm by limiting access or adherence.

Second, our results illuminate the total costs and amount reimbursed attributable to commonly used oral TKI agents, and possible changes in these outcomes in relation to shifts in out-of-pocket costs over time from 2005 to 2010. In addition, a recent survey of cancer patients demonstrated that more than two-thirds would like to discuss the out-of-pocket costs of medical care before starting treatment, and almost 40% reported that paying for cancer care was difficult or induced financial hardship.

 The results suggest that while the total costs of 6 months for either oral TKI agent is substantial at approximately $19,000, private health insurance plans cover more than 90% of these costs. Another key finding is that there were minimal changes to the total and out-of-pocket costs and private health insurance reimbursement over time, which is contrary to the findings of Dusetzina et al on a near doubling of co-pays from 2002 to 2011.

In our study, the total costs remained stable over time, which likely contributed to minimal changes to the reimbursement and the out-of-pocket costs for both oral TKI agents. Third, we found minimal differences in the cost outcomes and private health insurance reimbursement for sunitinib versus sorafenib. Several studies have embarked on evaluating the cost-effectiveness of different targeted agents for metastatic RCC.

 Remak et al conducted a cost-effectiveness analysis using a Markov model and demonstrated that, compared with interferon-alpha, sunitinib correlated with significantly better progression-free year gained at $18,611 and life-year gained at $67,214.

However, Benedict and colleagues more recently reported that sunitinib conferred a 0.17 of QALY increase and savings of $13,576 per patient relative to sorafenib in the United States.

 Further study is needed also to elucidate and comprehensively evaluate the cost-effectiveness of different targeted agents by also focusing on out-of-pocket and other potential economic implications for these drugs.

Our study has several limitations. Since this national private insurance database did not include information regarding stage or other clinical variables, we could not reliably investigate the possible relationship of disease burden or other clinical aspects, such as performance status or physician/patient preference, on costs and reimbursement. It is also plausible that different private health insurance plans may vary in the amount they reimburse, thereby affecting the out-of-pocket costs for which patients are responsible. The IMS LifeLink Health Plan Claims Database, however, is limited by not supplying information about individual health insurance plans.

It is certainly possible that the economic burden of targeted agents may have varied substantially across different private health insurance plans. We also acknowledge that determining the out-of-pocket costs relied on estimating this outcome from calculating the difference between the total costs and private health insurance reimbursement of each agent, rather than directly inquiring about the direct and indirect healthcare costs from patients. Lastly, we recognize that the differing dosing schedules for each oral TKI may have impacted the out-of-pocket costs.

Our study on the economic burden of sunitinib and sorafenib among patients diagnosed with metastatic RCC has important policy implications in this targeted therapy era. For both oral TKI agents, patients appear to be responsible for approximately $1100 in out-of-pocket costs over a 6-month duration, or 3 cycles of treatment. Such high out-of-pocket costs may represent formidable barriers in equitable access to sunitinib or sorafenib, or pose economic hardship to metastatic RCC patients. Moreover, the economic costs to patients (even those who, like those studied, are covered by insurance) for these first-line agents for metastatic RCC may become increasingly problematic due to greater cost sharing on the part of patients as a result of the ACA.

 Patients and physicians should ensure that discussions about treatment alternatives for metastatic RCC include mention of potential out-of-pocket costs. Further research is needed to establish whether out-of-pocket costs may be a barrier to access to targeted agents and/or can adversely impact compliance regarding these beneficial drugs for metastatic RCC patients.

: University Hospitals Case Medical Center, Case Western Reserve University School of Medicine (SPK), Cleveland, OH; Cancer Outcomes and Public Policy Effectiveness Research Center (COPPER) (SPK, CPG) and Department of Internal Medicine, Yale School of Medicine (CPG), Yale University, New Haven, CT; Division of Urology, Brigham and Women’s Hospital, Harvard Medical School (Q-DT), Boston, MA; Department of Surgery, Fox Chase Cancer Center-Temple University Health System (MCS), Philadelphia, PA; Department of Urology (RHT, SPP), and Knowledge and Evaluation Research Unit (NDS), and Division of Health Care Policy and Research (HVH, LCH, NDS), Mayo Clinic, Rochester, MN; Department of Urology, Case Western University School of Medicine (RA), Cleveland, OH; Henry Ford Hospital’s Vattikuti Urology Institute (JDS), Detroit, MI; Cancer Prognostics and Health Outcomes Unit, University of Montreal Health Centre (MS), QC, Canada.

: Dr Kim was supported by the career development award from the Conquer Cancer Foundation of the American Society of Clinical Oncology.

: Dr Gross is a member of the FAIR Health, Inc, Scientific Advisory Board, and has received grants from Medtronic, Inc (as principal investigator [PI]), Johnson & Johnson (co-PI), and 21st Century Oncology (co-PI); he has also received honoraria as a speaker at the American Society for Radiation Oncology annual meeting in September 2014. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

: Concept and design (SPK, CPG, Q-DT, SPP, JDS, MS); acquisition of data (SPK, HVH); analysis and interpretation of data (SPK, Q-DT, SPP, HVH, LCH, JDS); drafting of the manuscript (SPK, HVH, RA, JDS); critical revision of the manuscript for important intellectual content (SPK, CPG, Q-DT, MS, RHT, SPP, HVH, LCH, RA, JDS); statistical analysis (SPK, HVH); obtaining funding (SPK); administrative, technical, or logistic support (SPK, LCH); and supervision (SPK, RA).

: Simon P. Kim, MD, MPH, University Hospitals Case Medical Center, Case Western Reserve University School of Medicine, 11100 Euclid Avenue, Mailstop LKS5046, Cleveland, OH 44106. E-mail: simon.kim@UHhospitals.org.

1. Hartman M, Martin AB, Benson J, Catlin A; National Health Expenditure Accounts Team. National health spending in 2011: overall growth remains low, but some payers and services show signs of acceleration. 

2. Wharam JF, Ross-Degnan D, Rosenthal MB. The ACA and high-deductible insurance--strategies for sharpening a blunt instrument. 

3. Siegel R, Ma J, Zou Z, Jemal A. Cancer statistics, 2014. 

4. Leibovich BC, Blute ML, Cheville JC, et al. Prediction of progression after radical nephrectomy for patients with clear cell renal cell carcinoma: a stratification tool for prospective clinical trials. 

5. Frank I, Blute ML, Cheville JC, Lohse CM, Weaver AL, Zincke H. An outcome prediction model for patients with clear cell renal cell carcinoma treated with radical nephrectomy based on tumor stage, size, grade and necrosis: the SSIGN score. 

6. Shih YC, Chien CR, Xu Y, Pan IW, Smith GL, Buchholz TA. Economic burden of renal cell carcinoma in the US: part II—an updated analysis. 

7. Shih YC, Chien CR, Xu Y, Pan IW, Smith GL, Buchholz TA. Economic burden of renal cell carcinoma: part I--an updated review. 

8. Motzer RJ, Agarwal N, Beard C, et al. NCCN clinical practice guidelines in oncology: kidney cancer. J 

9. Ljungberg B, Hanbury DC, Kuczyk MA, et al; European Association of Urology Guideline Group for renal cell carcinoma. Renal cell carcinoma guideline. Eur Urol. 2007;51(6):1502-1510.

10. Escudier B, Eisen T, Stadler WM, et al. Sorafenib for treatment of renal cell carcinoma: final efficacy and safety results of the phase III treatment approaches in renal cancer global evaluation trial. 

11. Motzer RJ, Hutson TE, Tomczak P, et al. Overall survival and updated results for sunitinib compared with interferon alfa in patients with metastatic renal cell carcinoma. 

12. Choueiri TK, McDermott D, Sheng Duh M, Sarda SP, Neary MP, Oh WK. Costs associated with angiogenesis inhibitor therapies for metastatic renal cell carcinoma in clinical practice: results from a medical chart review study. 

13. Miller DC, Saigal CS, Warren JL, et al. External validation of a claims-based algorithm for classifying kidney-cancer surgeries. 

14. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. 

15. Prices—table B-60: consumer price indexes for major expenditure classes, 1965-2009. Government Printing Office website. http://www.gpo.gov/fdsys/pkg/ERP-2010/pdf/ERP-2010-table60.pdf. Published 2012. Accessed December 1, 2012.

16. Manning WG, Mullahy J. Estimating log models: to transform or not transform? 

17. Efron B. Bootstrap methods: another look at the jackknife. 

18. Filson CP, Redman BG, Dunn RL, Miller DC. Initial patterns of care with oral targeted therapies for patients with renal cell carcinoma. 

19. Conti SL, Thomas IC, Hagedorn JC, et al. Utilization of cytoreductive nephrectomy and patient survival in the targeted therapy era. 

20. Gupta K, Miller JD, Li JZ, Russell MW, Charbonneau C. Epidemiologic and socioeconomic burden of metastatic renal cell carcinoma (mRCC): a literature review. 

21. Benedict A, Figlin RA, Sandstrom P, et al. Economic evaluation of new targeted therapies for the first-line treatment of patients with metastatic renal cell carcinoma. 

Study investigate the economic burden from the total costs, private insurance reimbursement, and out-of-pocket costs for oral TKI agents among metastatic RCC patients.