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The American Journal of Managed Care January 2017
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An Expanded Portfolio of Survival Metrics for Assessing Anticancer Agents
Jennifer Karweit, MS; Srividya Kotapati, PharmD; Samuel Wagner, PhD; James W. Shaw, PhD, PharmD, MPH; Steffan W. Wolfe, BA; and Amy P. Abernethy, MD, PhD
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Justin Blackburn, PhD; David J. Becker, PhD; Michael A. Morrisey, PhD; Meredith L. Kilgore, PhD; Bisakha Sen, PhD; Cathy Caldwell, MPH; and Nir Menachemi, PhD, MPH

An Expanded Portfolio of Survival Metrics for Assessing Anticancer Agents

Jennifer Karweit, MS; Srividya Kotapati, PharmD; Samuel Wagner, PhD; James W. Shaw, PhD, PharmD, MPH; Steffan W. Wolfe, BA; and Amy P. Abernethy, MD, PhD
A novel, simplified cost-value analysis tool was created to better differentiate the value of anticancer agents and further characterize the expected survival benefit of all patients.
ABSTRACT

Objectives: 
With the introduction of more effective anticancer agents that prolong survival, there is a need for new methods to define the clinical value of treatments. The objective of this preliminary qualitative and quantitative analysis was to assess the utility of an expanded portfolio of survival metrics to differentiate the value of anticancer agents.

Study Design: A literature review was conducted of phase 3 trial data, reported in regulatory submissions within the last 10 years of agents for 6 metastatic cancers (breast cancer, colorectal cancer [CRC], melanoma, non–small cell lung cancer [NSCLC], prostate cancer [PC], and renal cell cancer [RCC]).

Methods: A new, simplified cost-value analysis tool was applied using survival outcomes and total drug costs. Metrics included median overall survival (OS), mean OS, 1-year survival rate, and number needed to treat (NNT) to avoid 1 death at 1 year. Survival results were compiled and compared both within and across trials by tumor type. Total drug costs were calculated by multiplying each agent’s cost per month (from October/November 2013, based on the database Price Rx/Medi-Span) by duration of therapy.

Results: Relative clinical value for each agent was not consistent across survival outcomes. In 3 tumor types, both the highest improvement in median OS and the highest improvement in mean OS occurred with the same anticancer agent (ipilimumab with melanoma, pemetrexed with NSCLC, and sunitinib with RCC); the highest improvement in the 1-year survival rate and the lowest NNT occurred together with the same anticancer agent in 5 tumor types (bevacizumab with CRC, ipilimumab with melanoma, erlotinib with NSCLC, abiraterone with PC, and temsirolimus with RCC). In the cost-value analysis, agents were inconsistent and achieved a high relative value with some survival outcomes, but not others.

Conclusions: This analysis suggests that any 1 metric may not completely characterize the expected survival benefit of all patients. The cost-value analysis tool may be applied to trial data and may be useful in helping to make treatment decisions, regardless of the agent’s effectiveness. A combined metric will be needed, as well as further research that includes more mature data, other tumor types, and emerging treatments.

Am J Manag Care. 2017;23(1):54-61
Takeaway Points

We present a novel cost-value analysis tool that integrates previously validated measurements of the value of anticancer agents. 
  • Individual value metrics cannot completely characterize the expected survival benefit of all patients. 
  • A full cost-value analysis should also take total drug cost into consideration. 
  • Metrics currently used in oncology trials, such as median overall survival, may not capture the full clinical value of newly introduced immuno-oncology agents, which result in prolonged survival in a proportion of patients. 
  • Further research is warranted that includes the incorporation of quality-of-life measurements, potential impact of toxicities on the cost of delivery, and insurance discounting of drug costs.
Cancer is an ever-growing public health problem,1 with an estimated 14.1 million new cases and 8.2 million associated deaths per year worldwide.2 Although advances in cancer therapy have led to dramatic gains in survival and quality of life (QOL), the cost of care has risen sharply.1,3 These costs, which are outpacing those of other areas of healthcare,4,5 are attributed to multiple factors, including the adoption of new and more expensive therapies,3,6 a shift toward personalized care,1 increased cancer survivorship,7 and the expanding number of cancer cases with a growing and aging population.1,7 Several key initiatives are therefore underway to facilitate a dialogue around improving care while containing costs.1,8,9

Much attention has been directed at defining the value of cancer treatments,1,10,11 but there is no consensus on what constitutes value as it has different meanings to various stakeholders (eg, patients, caregivers, physicians, payers).5,12 Recent analyses suggest that cost may not be associated with large survival gains13,14; and metrics currently used in oncology trials, such as response rate or even median overall survival (OS), may not characterize the full clinical value of new agents,13,15 which result in prolonged survival in a proportion of patients.16-18 Metrics are available that characterize changes in Kaplan-Meier curves with new therapies (eg, median and mean OS show a shift to the right and 1-year survival rate shows an upward shift in the tail), but when used alone, may not present the complete value picture. Therefore, a wide range of outcome measures are needed.10,11,15

The objective of this preliminary qualitative and quantitative analysis was to assess the utility of an expanded portfolio of survival metrics in differentiating the value among anticancer agents. This analysis used survival outcomes from several randomized trials with metastatic solid tumors. In addition, we developed a new cost-value analysis tool that can be easily applied to clinical trial data and may be useful to payers and providers in managed care in determining treatment choice.

METHODS

Survival Metrics

This analysis used a survival metric portfolio consisting of median OS, mean OS, 1-year survival rate, and number needed to treat (NNT) to avoid 1 death at 1 year. Survival information for each agent was derived from the product’s regulatory submission documents to the FDA (prescribing information) or the European Medicines Agency (EMA) (summary of product characteristics), or the respective trial publications presented with the regulatory submission documents, depending on the availability of presented Kaplan-Meier curves.

Median OS—defined as the time from treatment randomization at which half of the patients remain alive, with deaths attributed to any cause19,20—was included because it is the most frequently used efficacy endpoint for anticancer agents.21,22 Mean OS—defined as the average length of time that patients are alive during the trial study period23,24—was estimated by calculating the area under the curve (AUC).25,26 Mean OS was truncated because AUCs had cut-offs based on the study duration, which was assumed to have been deemed appropriate by the regulatory bodies approving each treatment. The GetData software package version 12 (GetData Pty Ltd, Kogarah NSW, Australia) was used to digitize Kaplan-Meier curves for the investigational and comparator arms, and these curves were digitally replotted to estimate x and y coordinates.

Survival rate is defined as the percent of patients alive at a specific key time milestone (eg, 1 year) that reflects a meaningful period of a patient’s life, especially in tumors with a short survival prognosis.25 One-year survival rate data were acquired from each agent’s Kaplan-Meier curves. Survival rates for subsequent years were not included, because trial durations varied across agents, and some trials were designed to last less than 2 years.25,26

NNT is the average number of patients who need to be treated to prevent 1 event (eg, death) based on a time period (eg, 1 year).27,28 NNT measures the investment in number of treated patients required to receive a return benefit of treatment in the population. Although there are no set limits for NNT to be considered clinically effective, a lower NNT (closer to 1) is considered favorable across different disease interventions.28 In our analysis, NNT is the number of patients needed to treat to avoid 1 death at 1 year. As the inverse of the absolute risk reduction, NNT is calculated as follows:

NNT = 1 / (investigational arm event rate − comparator arm event rate)

Comparison of Survival Results

Survival results were compiled and compared both within and across trials by tumor type. Absolute improvement in mean or median OS was calculated (in months) by subtracting the median or mean OS of the comparator arm from that of the investigational arm. Absolute improvement in the 1-year survival rate was calculated (in percentages) by subtracting the survival rate of the comparator arm from that of the investigational arm.

Cost-Value Analysis

A cost-value analysis facilitated comparisons across tumor types. Total drug cost was calculated by multiplying the agent’s cost per month by duration of therapy. Drug costs were in US dollars for October through November 2013 based on Price Rx/Medi-Span, a public database of Wolters Kluwer. Duration of therapy was determined based on the agent’s median duration of administration, median progression-free survival (PFS), or median time to progression, as listed in the product labels, with these data elements chosen based on data availability. Total drug cost also factored in the loading dose, as indicated by the product label, for applicable agents. Because both ipilimumab and sipuleucel-T have limited fixed-dose durations relative to the other agents, their total drug costs were calculated by dividing drug cost by median OS; median OS was felt to present a more comparable and conservative measure of duration than mean OS due to the differential impact of censoring on the mean across trials.

To determine the cost-value relationship, each agent was plotted with the x-axis reflecting total drug cost and the y-axis reflecting absolute improvement in median OS, absolute improvement in mean OS, or 1-year NNT to avoid 1 death. A fitted regression line (with the intercept set at 0 to standardize the progression across metrics) indicating an average cost-to-outcome ratio was plotted for each graph to distinguish agents that were above and below the average for the specific metric. Agents above the regression line had a lower-than-average cost relative to outcome benefit, whereas those below the line had a higher-than-average cost relative to outcome benefit.

Disease and Agent Selection

The following metastatic solid cancers, which have been the focus of clinical investigation, were selected: breast cancer (BC), colorectal cancer (CRC), melanoma, non–small cell lung cancer (NSCLC), prostate cancer (PC), and renal cell cancer (RCC). The analysis was limited to metastatic disease—the most advanced form of cancer—as it accounts for more than 90% of cancer-related deaths,29 and because drug development is occurring in this setting.

Anticancer agents in this analysis were those that met all of the following criteria: used for the treatment of stage 3 or 4 metastatic or refractory disease; included as part of a comparative, multiple-arm, phase 3 trial, reported in regulatory submissions with any comparator and OS as the primary or secondary endpoint; having documented median OS benefit reached at the time of regulatory approval from the FDA or EMA; having follow-up data available for at least 1 year; having an available Kaplan-Meier curve; and having been launched in the last 10 years in both the United States and Europe (eAppendix [available at www.ajmc.com]). Other agents were excluded as a result of not meeting the criteria (eg, crizotinib). This analysis considered all lines of therapy and was performed in the context of limited head-to-head comparison trials.

RESULTS

Survival Metric Portfolio

In a few cases, survival metrics were consistent for a particular anticancer agent within tumor types. In 3 of the 6 tumor types, both the highest median OS improvement and the highest mean OS improvement occurred with the same anticancer agent (ipilimumab with melanoma, pemetrexed with NSCLC, and sunitinib with RCC) (Table 1, see bolded numbers). Also, the highest improvement in the 1-year survival rate and the lowest NNT occurred together with the same anticancer agent in 5 tumor types (bevacizumab with CRC, ipilimumab with melanoma, erlotinib with NSCLC, abiraterone with PC, and temsirolimus with RCC). In other cases, the survival metrics were not consistent within tumor types. In no tumor types did a given agent have the highest improvement in 1-year survival rate or the lowest NNT along with the highest improvement in mean OS. Also, in melanoma, NSCLC, PC, and RCC, improvements in 1-year survival rates appeared to outpace improvements in median OS, specifically for biologics and immunotherapy; this pattern was mixed in BC and less apparent in CRC.

In many cases, survival metric results varied by agent within each tumor type (Table 1, see bolded numbers). For BC, ado-trastuzumab emtansine demonstrated the greatest improvement in median OS, pertuzumab in mean OS, eribulin mesylate in 1-year survival rate; trastuzumab demonstrated the lowest NNT. For CRC, bevacizumab demonstrated the greatest improvements in median OS and 1-year survival rate, as well as the lowest NNT, whereas cetuximab had the greatest improvement in mean OS. Capecitabine, the only agent assessed in CRC with a nonplacebo comparator, showed the least improvement across all 3 outcomes. For melanoma, ipilimumab showed the greatest benefit with all 3 outcomes. For NSCLC, pemetrexed exhibited the greatest improvements of median and mean OS, whereas erlotinib showed the greatest improvement in 1-year survival rate and the lowest NNT. For PC, enzalutamide demonstrated the greatest improvement in median OS, sipuleucel-T showed the greatest improvement in mean OS, and abiraterone had the greatest improvement in 1-year survival rate and the lowest NNT. For RCC, sunitinib demonstrated the greatest improvements in both median and mean OS, whereas temsirolimus had the greatest improvement in 1-year survival rate and the lowest NNT.

Cost-Value Analysis

 
Copyright AJMC 2006-2017 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
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