Introduction: The Oncology Care Model (OCM) is an innovative payment model introduced by the Centers for Medicare & Medicaid Services in 2016, which aims to improve quality and reduce the cost of cancer care. Under this framework, practices are incentivized to reduce spending in chemotherapy-centered episodes. Previous studies using data from pre-OCM periods (i.e. before July 1, 2016) suggested that reducing OCM episode costs, particularly disease-specific drug costs, may adversely affect overall survival (OS) in patients with multiple myeloma (MM). Using more recent data that covers 1.5 years after OCM implementation, the current study aimed to evaluate trends in OCM-defined episode costs and OS over time. Additionally, the association between OCM-defined MM episode costs and OS in MM, as well as changes in the association between the pre- and post-OCM periods, were evaluated. Methods: Patients with newly diagnosed MM (NDMM) and ≥ 1 qualifying OCM-defined MM episode between 2012 and 2017 were selected from the 100% Medicare data. OCM episodes were defined as the 6-month period following a triggering MM treatment claim. Each episode was linked to a practice and classified based on participation in the OCM and occurrence of pre- versus post-OCM implementation. Regression models were developed, based on the OCM algorithm, to adjust for case severity mix at the practice level. The models evaluated the impact of patient and episode characteristics on total episode costs, and episode subcomponents (e.g. MM-specific drugs, other medical treatment). Based on the regression outputs, standardized costs were calculated for each practice, adjusting for differences in patient and episode characteristics. All costs were inflated to 2017 US dollars (USD). From initial MM diagnosis, mean unadjusted episode costs, mean standardized episode costs, and 1-year OS rate were described over time by the year of first episode initiation. Two Cox proportional hazards models were constructed for OS with adjustment for key patient covariates (i.e. age, gender, comorbidity index, race, number of OCM episodes, and disability entitlement). Model 1 evaluated the association between OS and standardized total episode costs and the proportion of episode costs attributed to MM-specific drugs. Model 2 evaluated the effect on OS of the interaction between the time period (i.e. pre-OCM vs post-OCM) and total standardized episode costs. The analyses were repeated for key patient subgroups stratified by comorbidity status (i.e. low Charlson Comorbidity Index [CCI] vs high CCI) and practice type (i.e. OCM vs non-OCM). Results: A total of 17,363 patients with NDMM (51.0% male) were included in the analysis. Mean age at diagnosis was 74.8 years (30.0-102.0). Patient characteristics were comparable between pre-OCM and post-OCM periods. There were a total of 41,972 OCM episodes during the mean 2.2 years (standard deviation [SD] 1.4) of follow-up. Average (SD) MM-drug costs, other medical and drug costs, and total costs per OCM episode were USD 51,482 (USD 31,752), USD 22,625 (USD 28,452), and USD 74,107 (USD 38,606), respectively. From 2012 to 2017, average total episode costs, MM-drug costs, and survival all increased (Table). Model 1 indicated that a USD 10,000 increase in standardized total costs was associated with a 27.5% lower risk of death (hazard ratio [HR] 0.725; P < 0.05) and that MM-drug costs were the primary driver for the improved OS. The hazard of death decreased by 21.3% (HR 0.787; P < 0.05) for every 10% increase in the proportion of costs attributed to MM-drug costs versus other medical costs. Model 2 showed the association was similar during pre-OCM and post-OCM periods. Consistent results were observed between subgroups of high versus low CCI and OCM versus non-OCM practices. Conclusions: The analysis identified a positive correlation between average spending within OCM-defined episodes and OS among patients with NDMM, demonstrating observable clinical value for patients. This correlation remained consistent across pre-OCM and post-OCM periods. A closer evaluation of the cost subcomponents suggested that MM-specific drug costs are a primary driver for the observed OS benefit. Patient benefit from the innovative, albeit higher cost, therapies is noteworthy given the improved survival of MM. Careful evaluation is warranted for healthcare providers when attempting to reduce spending in response to new payment models. Disclosures Niesvizky: BMS: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; GSK: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Clancy:BMS: Current Employment, Current equity holder in publicly-traded company. Copher:Bristol Myers Squibb: Current Employment. Thomas:BMS: Current Employment. Qi:BMS: Other: Employee of Analysis Group Inc., which received consulting fees; Astellas Pharma, Inc.: Research Funding. Zhou:BMS: Other: Employee of Analysis Group Inc., which received consulting fees. Zichlin:BMS: Other: Employee of Analysis Group Inc., which received consulting fees. Koenigsberg:BMS: Other: Employee of Analysis Group Inc., which received consulting fees. Signorovitch:BMS: Other: Employee of Analysis Group Inc., which received consulting fees.

e19337 Background: In clinical trials, POM has demonstrated favorable clinical outcomes in patients with multiple myeloma (MM) who received prior LEN. Few studies, however, have examined POM treatment for MM in the community oncology setting. This retrospective cohort study compared treatment patterns and outcomes between patients who received a post-LEN treatment, either POM or another antimyeloma regimen. Methods: Adult patients with MM in the US Oncology Network (USON) who initiated a post-LEN treatment within 60 days of LEN discontinuation between Jan 1, 2016 and May 1, 2018, were not clinical trial participants, and had ≥ 2 subsequent clinic visits, were eligible. Data were sourced from USON’s iKnowMed electronic health records. Among patients observed to have discontinued treatment, time to treatment discontinuation (TTTD) was estimated from date of initiation of post-LEN treatment (index treatment) to date of discontinuation. Among patients who started a new treatment after the index treatment, time to next treatment (TTNT) was estimated from date of initiation of index treatment until date of initiation of the next treatment. TTTD and TTNT were analyzed using the Kaplan–Meier (KM) method across the whole study sample; patients who did not discontinue or start a next treatment were censored. Results: Of 547 eligible patients, 155 (28.3%) initiated POM and 392 (71.7%) initiated another antimyeloma regimen. Demographic characteristics were similar between the groups (for all patients, median age was 68 years, 54.5% patients were male and 71.7% were white). In total, 74.2% and 83.7% of patients discontinued the index treatment in the POM and other-treatment groups, respectively. Among the entire study population, KM estimates of median TTTD were 3.5 months (95% CI 2.8–4.6) and 1.9 months (95% CI 1.6–2.4) in the POM and other-treatment group, respectively (log-rank P < 0.001). In total, 65.2% and 71.2% of patients initiated subsequent treatment in the POM and other-treatment groups, respectively. KM estimates of median TTNT were 6.2 months (95% CI 4.5–7.8) and 4.5 months (95% CI 3.9–5.3) in the POM and other-treatment groups, respectively (log-rank P = 0.38). Conclusions: For patients with MM the use of POM following LEN treatment resulted in longer TTNT and TTTD compared with those who received other antimyeloma therapy. These findings support the use of POM treatment after LEN as an option for patients with relapsed/refractory MM.

Healthcare resource utilization (HCRU) and patient opinion play a role in treatment decisions for multiple myeloma (MM). This study aimed to characterize the impact of pomalidomide (POM) or carfilzomib (CARF) MM treatments on patient-reported outcomes (PROs).

The objective of the study was to assess racial disparities in the treatment and outcomes among white, African American, and Hispanic patients with multiple myeloma (MM). Patients with an MM diagnosis from the Surveillance Epidemiology and End Results (SEER)-Medicare (2007-2013) database were included. Continuous Medicare enrollment for 6 months before (baseline) and after MM diagnosis was required unless death occurred. Time from MM diagnosis to novel therapy initiation and autologous stem cell transplant (ASCT), overall survival (OS), and MM-specific survival (MSS) was evaluated. Unadjusted and multivariable regressions compared African Americans and Hispanics vs whites. Trends of novel therapy and ASCT use across MM diagnosis years were assessed using linear regression models. The study included 3504 whites, 858 African Americans, and 468 Hispanics. African Americans and Hispanics had a longer time from MM diagnosis to novel therapy initiation vs whites (median: 5.2 and 4.6 vs 2.7 months, respectively). All cohorts had an increasing trend of novel therapy initiation within 6 months of MM diagnosis, particularly whites (all P < .05). Median MSS was significantly longer for African Americans (5.4 years) than whites (4.5 years; P < .05), and was comparable for Hispanics and whites. Median OS was similar overall (2.6-2.8 years). ASCT rate within 1 year of MM diagnosis rose among whites and African Americans (P < .05), but not Hispanics, who were less likely to receive ASCT vs whites. Significant variations in novel therapy and ASCT use were observed among different racial/ethnic groups with MM. Although OS was similar, both African Americans and Hispanics may not be fully benefitting from the introduction of novel therapies, as they receive them later than whites.

The OCM was introduced in 2016 with the goal of improving quality and reducing costs associated with cancer care. Under this framework, practices are incentivized to reduce spending on chemotherapy-centered episodes. A previous study using SEER-Medicare data (2007–2013) suggested that reducing OCM episode costs in multiple myeloma (MM) may adversely affect patient survival. The present study aimed to evaluate the association between OCM-defined episode costs as well as MM-specific drug costs and overall survival (OS) in MM patients, using more recent data (2010–2015) and included 100% Medicare patients.

Despite an array of therapeutic options, multiple myeloma (MM) remains an incurable malignancy for most patients. A substantial unmet need exists in the treatment of MM, particularly for patients requiring multiple lines of therapy (LoT). This analysis describes clinical outcomes and treatment patterns among patients with MM initiating their first therapy after treatment with lenalidomide (LEN).

e20022Background: Patients with multiple myeloma (MM) are living longer, compared to other cancers. This analysis characterized the drivers of MM cost by phases of care along the patient journey. M...

e20030 Background: MM is the most common blood cancer among African Americans (AA). The study aimed to compare clinical and economic outcomes for AA with newly diagnosed MM who received novel vs non-novel agents as first-line (1L) Tx for MM. Methods: Pts with MM in SEER-Medicare (2007-2014) were included. Continuous Medicare enrollment for 6 mos before (baseline) and after 1L initiation date (index date) was required, unless pts died. Novel agent (eg, bortezomib [V], lenalidomide [R]) and non-novel agent users were identified based on the 1L Tx pts received. Overall survival (OS), myeloma specific survival (MSS), and healthcare costs were compared between AA novel and non-novel users. Among AA novel users, outcomes were further compared between 1L R + steroid (Rd) vs V + steroid (Vd) users. Results: Among 657 included AA pts, 398 (61%) used 1L novel agents. This proportion was significantly lower compared with caucasian (66%, p = 0.02). AA novel users were significantly younger (mean: 70 vs 73 yrs, p < 0.001). Both median OS (3.1 vs 1.8 yrs, p < 0.001) and MSS (not reached vs 57.7 mos, p < 0.01) were significantly longer for AA novel users than non-novel users; after adjusting for baseline characteristics, the risk of all-cause death remained lower for AA novel users (adj hazard ratio [HR] = 0.77, p = 0.01). AA non-novel users had numerically higher monthly medical costs than AA novel users (adj mean difference: $951), which were driven by higher inpatient ($2,571) and emergency room ($38) costs (both p < 0.05). Total costs (medical + pharmacy) were comparable between the two AA cohorts. Among AA novel users, 102 (26%) and 147 (37%) used 1L Rd and Vd, respectively. Time on Tx (DOT) was longer for Rd vs Vd (median 4.6 vs 4.0 mos, adj HR = 0.72, p = 0.04). A trend of longer time to next line Tx (TTNT) initiation or death (a proxy of PFS) was observed among Rd users (median 17.6 vs. 12.1 mos, p = 0.06). Conclusions: AA pts with MM who received 1L novel agents had longer OS and MSS compared with those who did not. Among novel users, pts receiving Rd had longer DOT and trended toward longer TTNT, compared with Vd. Results suggest that increasing the novel agents use, particularly Rd, may improve outcomes among AA pts with MM.

Abstract Background: For decades, CRAB criteria (hypercalcemia, renal impairment, anemia, and bone disease) have been used to diagnose multiple myeloma (MM), but little is known about the economic burden of CRAB in patients with MM who have relapsed. The only category 1 doublet regimens recommended by NCCN Guidelines for non-transplant candidates are lenalidomide-based (NCCN Myeloma v4.2018). This study aims to evaluate CRAB event rates and the associated economic burden among newly diagnosed MM (NDMM) patients on doublet therapy who were not eligible for stem cell transplant (SCT) and were followed to next line of therapy. Methods: This retrospective study identified patients with ≥ 2 claims of MM (ICD-9-CM code: 203.0x; ICD-10-CM code: C90.0x) ≥ 30 days apart and ≥ 1 treatment (first claim date was used as index date) between January 1, 2011 and December 31, 2015 from the Medicare database. Eligible patients were required to have continuous enrollment from 6 months pre-index or first MM diagnosis date until ≥ 12 months post-index date unless patients died < 12 months post-index date (the follow-up period); ≥ 1 full cycle of therapy; no evidence of prior MM diagnosis or treatment (including autologous SCT [ASCT]); and no evidence of ASCT in the follow-up period. First line of therapy (LOT1) included all treatments prescribed within 60 days of the index date, and patients were included in the doublet therapy cohort based on the index treatment regimen. Progression to a subsequent LOT (LOT2) was defined by the earliest occurrence of an addition or switch to new non-maintenance treatment > 60 days post-index date, restart of any non-maintenance MM treatment after a > 180-day gap, or a dose increase from maintenance to relapse therapy. CRAB event rate per 1,000 person-years (PYs), CRAB event-related healthcare resource utilization (HRU) per patient per month (PPPM), and costs PPPM were evaluated during the LOT1 (the time from index date to end of LOT1) and LOT2 (the time from initiation of LOT2 to end of LOT2) among patients on doublet therapy with LOT1 and those who progressed to LOT2, respectively. Mean was calculated for the continuous variables, while categorical variables were presented as percentage values. Since the LOT2 population was a subset of the LOT1 population and they were not mutually exclusive cohorts, no statistical comparisons were made. Results: The study included 4,970 patients with MM not eligible for ASCT, of which 3,065 (61.7%) patients were prescribed doublet therapy in LOT1. Among these patients on doublet therapy, 1,122 (36.6%) initiated LOT2. The mean age was approximately 77 years, and most patients were white (LOT1, 77.0%; LOT2, 79.7%). The majority of the patients had hypertension (LOT1, 88.3%; LOT2, 86.1%), followed by anemia (LOT1, 79.0%; LOT2, 75.6%) and osteoarthritis (LOT1, 75.7%; LOT2, 76.7%), as comorbidities during the 6 months prior to the index date. CRAB event rates declined from LOT1 to LOT2, with the highest event rate per 1,000 PYs observed for anemia (LOT1, 1,965.1; LOT2, 1,808.2), followed by bone disease (LOT1, 258.1; LOT2, 244.1), renal impairment (LOT1, 167.9; LOT2, 159.9), and hypercalcemia (LOT1, 106.9; LOT2, 99.6); a decline in event rate was observed from LOT1 to LOT2 (Figure 1). CRAB event-related HRU PPPM increased from LOT1 to LOT2, including number of inpatient visits (LOT1, 0.1; LOT2, 0.2), length of inpatient stay (LOT1, 0.8 days; LOT2, 1.0 days), and number of outpatient hospital visits (LOT1, 2.0; LOT2, 2.2). However, the number of outpatient office visits PPPM decreased from LOT1 to LOT2 (LOT1, 0.9; LOT2, 0.8). Similarly, there was a trend toward increased CRAB event-related healthcare costs PPPM from LOT1 to LOT2 (Figure 2) including inpatient (LOT1, USD 1,548; LOT2, USD 2,031), outpatient hospital (LOT1, USD 214; LOT2, USD 301), outpatient office (LOT1, USD 77; LOT2, USD 89), and total medical costs (LOT1, USD 2,120; LOT2, USD 2,593). Conclusions: This study shows that CRAB events were more expensive to treat as patients relapse. Delaying disease progression may be associated with lower HRU and potential cost savings, thereby reducing the burden of MM. A potential limitation of this study is that claims data do not include clinical parameters. Future studies should be considered to evaluate the impact of age and comorbidities on the economic burden associated with CRAB events. This study also highlights the value of delaying progression and the time to next treatment. Disclosures Pandya: Celgene Corporation: Consultancy; STATinMED Research: Employment. Clancy:Celgene Corporation: Employment, Equity Ownership, Research Funding. Shrestha:STATinMED Research: Employment, Equity Ownership. Wang:STATinMED Research: Employment, Equity Ownership.

98 Background: Survival probabilities for patients with multiple myeloma have increased considerably over the past several decades, and a conservative estimate of 5-year survival today is approximately 50%, perhaps higher with optimal treatment. Treatment options for multiple myeloma have grown significantly beginning in 2003 with the approval of bortezomib, followed by approvals for lenalidomide and thalidomide in 2006. The second wave of novel agent approvals began in 2012 with carfilzomib, followed by pomalidomide in 2013. The aim of this study was to estimate the survival gains associated with multiple myeloma therapies after the introduction of novel therapies beginning in 2003 in the United States. Methods: We estimated survival gains for multiple myeloma patients diagnosed in the 5-year period from 2010-2014—who had access to newer therapies like lenalidomide, bortezomib, pomalidomide, and carfilzomib—compared with patients diagnosed in the 5 years prior to the approval of bortezomib (1998–2002). We used data from the Surveillance, Epidemiology, and End Results (SEER) Program cancer registry and a generalized gamma regression survival model. The sample from SEER included patients aged > 18 years who had a diagnosis of multiple myeloma between 1983 and 2014. Results: Of 88,462 patients identified in the full sample, 14,446 patients were diagnosed in 1998–2002 and 25,948 patients were diagnosed in 2010–2014. Overall survival was 51% longer ( P< 0.001) in multiple myeloma patients diagnosed in 2010–2014 than in patients diagnosed in 1998–2002. Patients diagnosed in 2010–2014 had median and mean survival of 1.32 and 2.27 years longer, respectively, than patients diagnosed in 1998–2002. Conclusions: Patients diagnosed with multiple myeloma during 2010–2014 had significant improvement in survival relative to patients diagnosed in 1998–2002. This study found continued improvement in survival in multiple myeloma patients in the most recent 5-years of survival data available, demonstrating the considerable progress made since the wave of multiple myeloma innovation began in 2003.

194 Background: Despite improved survival with advanced multiple myeloma (MM) treatments (Tx), 15–20% of pts experience early relapse. We assessed the economic impact of early progression among non-stem-cell transplant NDMM pts. Methods: NDMM pts with ≥ 1 Tx (1st claim date: index date) from 01Jan2011–31Dec2015 were identified from the 100% Medicare database. Eligible pts had ≥ 1 full cycle of therapy and continuous health plan enrollment from 6 months pre-index or first MM diagnosis date until ≥ 12 month post-index date unless pts died < 12 months post-index date. First line of therapy (LOT1) included all Txs prescribed ≤ 60 days post-index date. Among pts who progressed to LOT2, median time to next Tx (TTNT) was estimated from a Kaplan–Meier curve as duration from LOT1 start until the earliest of an addition/switch/restart of non-maintenance MM Tx, or dose increase from maintenance to relapse therapy. Pts who progressed to LOT2 prior to and after median TTNT were included in the early and delayed progression cohorts, respectively. Annual all-cause and MM-related healthcare costs estimated from a generalized linear model were compared between doublet therapy pts in LOT1 in the early and delayed progression cohorts. Results: Of 3,768 MM pts with LOT1, 36.1% progressed to LOT2 with median TTNT of 302 days; 81.3% pts initiated doublet therapy in LOT1, of which 19.2% (n = 589) and 17.3% (n = 533) were included in the early and delayed progression cohorts, respectively. Pts in the early progression cohort were younger and incurred higher all-cause inpatient ($17,332 vs $10,455), outpatient hospital ($18,183 vs $15,097), emergency department ($462 vs $395), office ($37,728 vs $29,174), and total costs ($130,948 vs $108,003) compared with the delayed progression cohort. Similarly, the early progression cohort had higher MM-related total costs ($87,284 vs $72,150) including inpatient and outpatient costs (all P< 0.001). All-cause and MM-related pharmacy costs were similar between cohorts. Conclusions: Early progression after LOT1 is associated with substantially higher economic burden indicating the need for future studies of therapies that delay progression and potentially result in cost savings.

At this point, the rising cost of oncology care in the United States barely needs introduction. Cancer drugs account for approximately 10% of the total prescription drug market.1QuintilesIMS Institute Medicines Use and Spending in the U.S.: A Review of 2016 and Outlook to 2021. QuintilesIMS Institute, Parsippany, NJ2017Google Scholar Although the share of overall prescription spending in total health care spending has increased slightly in recent years,2Kamal R. Cox C. What are the recent and forecasted trends in prescription drug spending? Peterson-Kaiser Health System Tracker website.https://www.healthsystemtracker.org/chart-collection/recent-forecasted-trends-prescription-drug-spending/#item-startGoogle Scholar, 3Fonseca R. Abouzaid S. Bonafede M. et al.Trends in overall survival and costs of multiple myeloma, 2000-2014.Leukemia. 2017; 31: 1915-1921Crossref PubMed Scopus (209) Google Scholar new cancer treatments are now routinely priced at more than $100,000 per year of treatment,4Light D.W. Kantarjian H. Market spiral pricing of cancer drugs.Cancer. 2013; 119: 3900-3902Crossref PubMed Scopus (65) Google Scholar raising pressing questions of both affordability and value to patients. Despite the attention that cancer drug pricing has received, relatively little input into the debate has focused on 2 underlying economic principles that are critical to understanding key elements of the debate: whether new drugs offer value to patients and society and whether cancer drug prices can be reduced without adverse effects on innovation. In this article, we discuss 2 potential misconceptions about cancer care in the United States, emphasizing that a more robust understanding of existing evidence outside of medicine is needed to address core questions about cancer drugs' value. Economic evidence suggests that the societal benefits from cancer care have historically far exceeded the cost. For example, from 1988 to 2000, investments in cancer research resulted in survival improvements generating 23 million additional life-years and $1.9 trillion of social value for Americans.5Lakdawalla D.N. Sun E.C. Jena A.B. Reyes C.M. Goldman D.P. Philipson T.J. An economic evaluation of the war on cancer.J Health Econ. 2010; 29: 333-346Crossref PubMed Scopus (55) Google Scholar During the same period, health care providers and drug manufacturers appropriated approximately 5% to 19% of this societal value, with the rest of the benefit accruing to patients.5Lakdawalla D.N. Sun E.C. Jena A.B. Reyes C.M. Goldman D.P. Philipson T.J. An economic evaluation of the war on cancer.J Health Econ. 2010; 29: 333-346Crossref PubMed Scopus (55) Google Scholar Yet, with rising drug prices, there is increasing concern that new cancer drugs offer little value to patients, with the typical calculation comparing the incremental cost and benefit of a new cancer therapy relative to a standard of care. Drugs for which the incremental costs per quality-adjusted life-year (QALY) gained exceed a certain threshold, often $100,000 per QALY, are deemed to be of low value. This calculation holds intuitive appeal but remains problematic. Cost-effectiveness analyses rarely factor in the importance of drug rebates, which lower the effective cost of therapies beyond “list prices”—that are frequently used in analyses—and cost-effectiveness analyses infrequently include other important components of economic value such as patient preferences. Furthermore, many ethical problems remain when using QALYs to allocate which care should be provided to patients.6Harris J. QALYfying the value of life.J Med Ethics. 1987; 13: 117-123Crossref PubMed Scopus (361) Google Scholar The QALY is also woefully inadequate because it places a lower value for life after diagnosis, an assertion patients rightfully dispute. Consider, for instance, how using list prices for drugs affects cost-effectiveness estimates. Using the list price of cancer drugs invariably makes newer drugs appear less valuable, even though health insurers frequently pay prices well below the list price of oncology drugs.7Polite B.N. Ward J.C. Cox J.V. et al.Payment for oncolytics in the United States: a history of buy and bill and proposals for reform.J Oncol Pract. 2014; 10: 357-362Crossref PubMed Scopus (22) Google Scholar This scenario would be analogous to using the Manufacturer Suggested Retail Price in assessing the value of a consumer good, as opposed to the price that most consumers, in this case health plans, pay. This is not to say that using actual transaction prices would make new cancer drugs cost-effective—they may still not be—but proper accounting is the exception, not the norm. The “value of hope” is also not routinely included in value assessments of cancer therapies. Both new and old drugs offer important sources of value beyond improvements in traditionally measured and valued QALYs, none of which are routinely incorporated into value assessments for oncology drugs. For example, some evidence suggests that oncology patients place high value on therapies with outcomes that offer the potential for longer survival to some patients. In the parlance of typical cancer studies, this means that patients may not value median or “typical” improvements in survival as much as they value the probability of getting a better than average response to treatment. Indeed, in one survey of patients with cancer, 77% of those surveyed preferred to gamble on “hopeful” therapies rather on therapies with a certain median survival but less chance of a large survival gain.8Lakdawalla D.N. Romley J.A. Sanchez Y. Maclean J.R. Penrod J.R. Philipson T. How cancer patients value hope and the implications for cost-effectiveness assessments of high-cost cancer therapies.Health Aff (Millwood). 2012; 31: 676-682Crossref PubMed Scopus (83) Google Scholar Other sources of value exist as well. For instance, the effects of cancer frequently extend to individuals beyond just those with disease. These “spillover” effects include emotional suffering of loved ones, caregiver burden including work and productivity loss,9Grant M. Sun V. Fujinami R. et al.Family caregiver burden, skills preparedness, and quality of life in non-small cell lung cancer.Oncol Nurs Forum. 2013; 40: 337-346Crossref PubMed Scopus (113) Google Scholar, 10Gridelli C. Ferrara C. Guerriero C. et al.Informal caregiving burden in advanced non-small cell lung cancer: the HABIT study.J Thorac Oncol. 2007; 2 ([published correction appears in J Thorac Oncol. 2007;2(7):676]): 475-480Abstract Full Text Full Text PDF PubMed Scopus (18) Google Scholar, 11Fujinami R. Sun V. Zachariah F. Uman G. Grant M. Ferrell B. Family caregivers' distress levels related to quality of life, burden, and preparedness.Psychooncology. 2015; 24: 54-62Crossref PubMed Scopus (71) Google Scholar, 12Grunfeld E. Coyle D. Whelan T. et al.Family caregiver burden: results of a longitudinal study of breast cancer patients and their principal caregivers.CMAJ. 2004; 170: 1795-1801Crossref PubMed Scopus (678) Google Scholar, 13Girgis A. Lambert S. Johnson C. Waller A. Currow D. Physical, psychosocial, relationship, and economic burden of caring for people with cancer: a review.J Oncol Pract. 2013; 9: 197-202Crossref PubMed Scopus (277) Google Scholar and even adverse health effects. Improving cancer outcomes creates value for both patients whose outcomes are improved and their loved ones. The economic value of these spillover effects is rarely incorporated into cancer drug value assessments. It is quite possible that given the high price of new cancer drugs, incorporating broader notions of value may still not make these drugs cost-effective by conventional standards. But formal assessments of cancer drug value should take a broader and more economically rigorous view of value, as has been recently proposed.14Sanders G.D. Neumann P.J. Basu A. et al.Recommendations for conduct, methodological practices, and reporting of cost-effectiveness analyses: Second Panel on Cost-Effectiveness in Health and Medicine.JAMA. 2016; 316: 1093-1103Crossref PubMed Scopus (1619) Google Scholar To be sure, the issue at odds is not whether QALYs themselves should be used to measure health benefits but that broader concepts of value—such as the value of hope and the spillover benefits of new therapies—are not conceptually ignored in most health care value frameworks. Pharmaceutical companies frequently justify prices on the basis that innovation is risky and that high prices are needed to incentivize company investors to undertake investments that are highly uncertain. Some investigators estimate that 9 in every 10 drugs fail at some point in the research and development (R&D) process,15DiMasi J.A. Reichert J.M. Feldman L. Malins A. Clinical approval success rates for investigational cancer drugs.Clin Pharmacol Ther. 2013; 94: 329-335Crossref PubMed Scopus (135) Google Scholar, 16Hay M. Thomas D.W. Craighead J.L. Economides C. Rosenthal J. Clinical development success rates for investigational drugs.Nat Biotechnol. 2014; 32: 40-51Crossref PubMed Scopus (1488) Google Scholar with estimated costs of drug development (including failed therapies) of over $2 billion per marketed drug.17DiMasi J.A. Hansen R.W. Grabowski H.G. The price of innovation: new estimates of drug development costs.J Health Econ. 2003; 22: 151-185Crossref PubMed Scopus (3182) Google Scholar Critics of this view point to the fact that many marketed therapies received large, initial support from federal sources such as the National Institutes of Health.18Kesselheim A.S. Tan Y.T. Avorn J. The roles of academia, rare diseases, and repurposing in the development of the most transformative drugs.Health Aff (Millwood). 2015; 34: 286-293Crossref PubMed Scopus (64) Google Scholar Moreover, critics point to estimates that the pharmaceutical industry spends more on marketing as opposed to R&D and earns profits in excess of other industries.19Anderson R. Pharmaceutical industry gets high on fat profits. BBC News website.http://www.bbc.com/news/business-28212223Google Scholar These arguments suggest that drug prices could be substantially reduced without any adverse impact on innovation. In addition, these arguments fail to recognize that marketing of products and R&D are complements, not substitutes: economists have recognized the point that innovators spend more on R&D when they anticipate larger demand for their products, which is influenced by marketing. Moreover, many highly successful therapies, such as β-blockers, are routinely underutilized by patients and underprescribed by physicians, which suggests that marketing is an important component of ensuring that successful R&D efforts ultimately reach patients. The best available economic evidence tells a different story of the relationship between prices, innovation, and patient outcomes. Like many other innovative industries, the returns from a few successful products cover the R&D costs of many failures. One study found that two-thirds of drugs brought to market have a net present value of returns (lifetime sales) below the cost of development.17DiMasi J.A. Hansen R.W. Grabowski H.G. The price of innovation: new estimates of drug development costs.J Health Econ. 2003; 22: 151-185Crossref PubMed Scopus (3182) Google Scholar Evidence also suggests that the pharmaceutical industry does not earn excessive profits relative to other industries once one properly accounts for the costs and risks of uncertain R&D.20US Congress, Office of Technology Assessment Pharmaceutical R&D: Costs, Risks and Rewards. US Government Printing Office, Washington, DC1993Google Scholar To this point, uncertainty in the R&D process originates not only at the basic science level but also into clinical testing in humans. For example, only one-fifth of the new chemical entities that are subjected to human testing emerge at the end of the process with marketing approval; the rest fail at various stages.20US Congress, Office of Technology Assessment Pharmaceutical R&D: Costs, Risks and Rewards. US Government Printing Office, Washington, DC1993Google Scholar, 21Scherer F.M. The pharmaceutical industry—prices and progress.N Engl J Med. 2004; 351: 927-932Crossref PubMed Scopus (98) Google Scholar Ultimately, if pharmaceutical financial returns were disproportionately higher than in other industries after appropriate accounting for risk, large institutional investors would disproportionately weight investment portfolios toward pharmaceutical companies, a prediction that has not borne out. The question of whether reduced profits will lead to reductions in innovation also has an answer, although the economic studies that support this finding are unfamiliar to most clinicians.22Acemoglu D. Linn J. Market size in innovation: theory and evidence from the pharmaceutical industry.Q J Econ. 2004; 119: 1049-1090Crossref Scopus (377) Google Scholar, 23Finkelstein A. Static and dynamic effects of health policy: evidence from the vaccine industry.Q J Econ. 2004; 119: 527-564Crossref Scopus (134) Google Scholar, 24Blume-Kohout M.E. Sood N. Market size and innovation: effects of Medicare Part D on pharmaceutical research and development.J Public Econ. 2013; 97: 327-336Crossref PubMed Scopus (73) Google Scholar Nearly all of these studies evaluate how changes in drug profitability (driven by either changes in the market size for a product, changes in reimbursement, or changes in the speed of approval) influence rates of innovation, with all finding that reductions in profitability reduce rates of innovation. The critical question then becomes a normative one. What trade-offs would society make if drug prices were to be artificially manipulated? The evidence is fairly clear that reductions in drug prices will lead to reductions in innovation,22Acemoglu D. Linn J. Market size in innovation: theory and evidence from the pharmaceutical industry.Q J Econ. 2004; 119: 1049-1090Crossref Scopus (377) Google Scholar, 23Finkelstein A. Static and dynamic effects of health policy: evidence from the vaccine industry.Q J Econ. 2004; 119: 527-564Crossref Scopus (134) Google Scholar, 24Blume-Kohout M.E. Sood N. Market size and innovation: effects of Medicare Part D on pharmaceutical research and development.J Public Econ. 2013; 97: 327-336Crossref PubMed Scopus (73) Google Scholar but that does not mean change should not be welcome. If the counterfactual therapies that never make it to market (because prices are reduced) provide relatively little value to society, then reductions in drug prices would arguably be worth the trade-off of decreased innovation. This may very well be a trade-off society is willing to make, but the notion that no trade-offs exist between drug pricing and innovation is likely false. Moreover, this is an issue in which empirical evidence can guide policy. Although economic evidence is limited, the evidence that exists suggests that the returns to society of greater spending on cancer therapies and cancer R&D have been largely positive.5Lakdawalla D.N. Sun E.C. Jena A.B. Reyes C.M. Goldman D.P. Philipson T.J. An economic evaluation of the war on cancer.J Health Econ. 2010; 29: 333-346Crossref PubMed Scopus (55) Google Scholar The rising cost of cancer drugs has placed a growing emphasis on better understanding the affordability and value of cancer care. Existing evidence outside of medicine suggests that a failure to broadly and accurately assess value raises the risk of underinvesting in therapies that may create large benefits for society and perhaps overinvesting in therapies whose societal returns are relatively low. Moreover, a failure to understand empirical evidence that demonstrates economic trade-offs exist between cancer drug pricing and innovation risks promoting policy actions that may have unintended, but economically predictable, consequences for patients: lower prices may increase access to cancer therapies, improve cancer outcomes, and possibly lower cancer spending today but may reduce access to innovative therapies and the possibility of better cancer outcomes moving forward.

Studies evaluating the association between statins and colorectal cancer (CRC) have used various methods to address bias and have reported mixed findings. We sought to assess the association in a large cohort of residents in Emilia-Romagna, Italy, using multiple methods to address different sources of confounding. We also sought to explore potential effect measure modification by sex.

OBJECTIVES:The purpose of this study was to better quantify how urine drug monitoring (UDM) is used in clinical practice. Little is known about which patients are monitored, how often patients are monitored, which substances are important to detect, and under what circumstances clinicians modify the frequency of monitoring.DESIGN:An online survey was developed based on qualitative phone interviews with eight clinicians who use UDM as a routine component of clinical practice.PARTICIPANTS:One thousand fourteen randomly selected clinicians known to order urine toxicology screenings were invited by mail in June 2011 to respond to the online survey assessing their clinical needs and preferences regarding UDM.RESULTS:Of the 93 respondents, 76 percent (n = 72) require all new patients to have UDM performed when they enter their clinic. The majority administer UDM to patients four times a year. The most common reasons cited by clinicians for a change in the frequency of monitoring are patient history of substance abuse and aberrant behaviors. Overall, the respondents showed broad support to test patients consistently for the most common illicit drugs, the majority of opioids, and a handful of prescription medications associated with abuse.CONCLUSION:Despite a lack of agreement between guidelines informing the use of UDM, there appears to be a general consensus among practitioners that use UDM on: which patients to monitor, how often to monitor, and which substances are most important to detect.

To compare and contrast the Medication Possession Ratio (MPR) and Proportion of Days Covered (PDC) measures of adherence and explore the implications of measure choice and specific definition on study results. Two adherence measures, MPR and PDC, were selected for comparison because of their prominence in the claims database research literature. To highlight the effect of measure selection, examples demonstrating contrasting results for MPR and PDC are presented. Furthermore, the impact of numerator and denominator specification within each of those measures is examined and illustrated with examples. Implications for assessing and interpreting published research studies are presented. Although MPR and PDC have been operationally defined in similar ways in the literature, there are differences that could yield distinct results. The basic structure of these measures is a ratio with a proxy for the number of compliant days in the numerator and the number of days in a measurement period in the denominator. MPR is based on the sum of dispensed ‘days supply’ over a period, whereas PDC is based on evaluation of available supply for each individual day in the period. A demonstration is provided on how research design choices of MPR or PDC and specification of numerator and denominator can result in different findings for a given research question. Despite the similar structure of MPR and PDC metrics, study design choices can affect study results considerably. Selection of an adherence measure must be tailored to the therapeutic area, relevant medications, and research objectives. Researchers should be transparent in the specification of measures used and readers need to understand the implication of these research design decisions.