Association Between Clinical Value and Financial Cost of Cancer Treatments: A Cross-Sectional Analysis

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  • 1 Health Outcomes Research Group, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York; and
  • 2 Yale University School of Medicine, New Haven, Connecticut.

Background: The cost of cancer treatment has increased significantly in recent decades, but it is unclear whether these costs have been associated with commensurate improvement in clinical value. This study aimed to assess the association between the cost of cancer treatment and 4 of the 5 NCCN Evidence Blocks (EB) measures of clinical value: efficacy of regimen/agent, safety of regimen/agent, quality of evidence, and consistency of evidence. Methods: This is a cross-sectional, observational study. We obtained NCCN EB ratings for all recommended, first-line, and/or maintenance treatments for the 30 most prevalent cancers in the United States and calculated direct pharmacologic treatment costs (drug acquisition, administration fees, guideline-concordant supportive care medications) using Medicare reimbursement rates in January 2019. We used generalized estimating equations to estimate the association between NCCN EB measures and treatment cost with clustering at the level of the treatment indication. Results: A total of 1,386 treatments were included. Among time-unlimited treatments (those administered on an ongoing basis without a predetermined stopping point), monthly cost was positively associated with efficacy ($3,036; 95% CI, $1,782 to $4,289) and quality of evidence ($1,509; 95% CI, $171 to $2,847) but negatively associated with safety (–$1,470; 95% CI, –$2,790 to –$151) and consistency of evidence (–$2,003; 95% CI, –$3,420 to –$586). Among time-limited treatments (those administered for a predetermined interval or number of cycles), no NCCN EB measure was significantly associated with treatment cost. Conclusions: An association between NCCN EB measures and treatment cost was inconsistent, and the magnitude of the association was small compared with the degree of cost variation among treatments with the same EB scores. The clinical value of cancer treatments does not seem to be a primary determinant of treatment cost.

Background

Drug prices have become a significant problem in oncology. Initial list prices have increased at an exponential rate,1 and most new cancer drugs experience additional postmarketing price increases.2,3 These trends have resulted in sufficient financial toxicity.4

However, it is not clear whether newer, more expensive drugs have produced a clinical benefit commensurate with their financial burden. As prices have increased, the financial cost necessary to achieve the same survival improvement using newer drugs has also increased.5 In addition, a number of studies evaluating the prices of newly approved cancer drugs with respect to their clinical trial data have failed to find a positive relationship between the magnitude of improvements in patient outcomes and drug prices.613

Despite these observations, pharmaceutical industry representatives have long asserted that drug prices reflect underlying clinical value and are therefore justified.1416 Whether increasing cancer drug prices reflect significant improvements in effectiveness is therefore germane to ongoing legislative attempts at drug price reforms.

Whether treatment price may reflect clinical benefit across all cancer treatment settings, not only among newly approved treatments for on-label indications as previously studied, has not been evaluated. In addition, the influence of recently approved immunotherapy drugs on the price–benefit association has not been evaluated. Therefore, we aimed to assess the correlation between the clinical value of cancer treatments and the cost of treatment, inclusive of all treatment regimens across all recommended uses, both approved and off-label.

Methods

NCCN publishes Evidence Blocks (EB), which assess cancer treatments on the following 5 measures using a simple 1 to 5 scoring system: efficacy of regimen/agent (extent to which treatment improves survival and/or symptoms), safety of regimen/agent (likelihood of adverse effects, with fewer adverse effects receiving higher scores), quality of evidence (number and rigor of the supporting clinical trials), consistency of evidence (degree to which clinical trials agree on the degree of benefit), and affordability of regimen/agent (overall cost of treatment, with less expensive treatments receiving higher scores).17 EB scores reflect a synthesis of clinical evidence and expert opinion. The NCCN EB are the most comprehensive of the value frameworks developed in oncology, including all recommended treatments regardless of approval date or level of clinical evidence, and is the most widely recognized framework by oncologists.18

We extracted EB scores for all treatments used in the adjuvant, neoadjuvant, definitive treatment, first-line, or maintenance settings for the 30 highest-incidence cancers in the United States, current as of December 31, 2018. For each treatment, we calculated Medicare treatment costs, using the average sales price (ASP) file and Medicare Plan Finder prices, inclusive of administration and supportive care costs, as described in earlier research (for full methodologic details, see supplemental eAppendix 1, available with this article at JNCCN.org).19 Ancillary and nonmedical costs were not included. To avoid making misleading cost comparisons, we categorized each treatment as either “time-limited” (often, adjuvant or neoadjuvant treatments) or “time-unlimited” (often, treatments for advanced/metastatic disease). For time-limited treatments, we calculated costs across the full course of therapy; for time-unlimited treatments, we calculated the monthly cost of treatment.

To enable comparisons between treatments that were similar (eg, those that might be weighed against each other in a clinical treatment decision), we categorized all treatments into treatment groups defined by (1) treatment indication, as defined by the NCCN EB; (2) time-limited versus time-unlimited; and (3) inclusion of radiation therapy and/or hematopoietic stem cell transfer as part of the treatment.

We used descriptive statistics to assess the distribution of treatment costs across NCCN EB scores separately for time-limited and time-unlimited treatments. We assessed the distribution for each measure separately and for the overall sum of efficacy, safety, quality of evidence, and consistency of evidence; affordability was omitted because it is a measure of treatment cost, which we measured separately.

We used generalized estimating equations, clustered at the level of the treatment group, to assess the independent association between treatment costs and each NCCN EB measure. Models were multivariable, using treatment cost as the dependent variable and the 4 included NCCN EB measures as independent variables. We conducted planned sensitivity analyses that included (1) general linear models without clustering, (2) omission of treatment groups containing n=1 treatment, (3) no supportive care costs, (4) more aggressive supportive care utilization, (5) modeling of logged treatment costs, (6) omission of treatment groups using radiation therapy or stem cell transfer, (7) rank-order testing (Kruskal-Wallis test), and (8) treatment group–level fixed effects.

Results

The analysis included 1,386 treatments: 541 were time-unlimited and 845 were time-limited. For each NCCN EB measure, there was wide variation in treatment cost across each possible score. For example, for time-limited regimens that received an efficacy score of 5 (highest efficacy), the mean cost was $35,796, with a range of $2,292 to $217,998 (Table 1).

Table 1.

Cost at Each Level of NCCN EB Measures

Table 1.

Wide variation was also seen when comparing the cost of each treatment with its summary NCCN EB score (the sum of efficacy, safety, quality of evidence, and consistency of evidence) (Figure 1).

Figure 1.
Figure 1.

Distribution of treatment costs and NCCN EB scores. The x axis represents the numeric sum of the efficacy, safety, quality of evidence, and consistency of evidence scores for each treatment. Because each measure ranges from 1 to 5, the x axis range is from 4 (lowest-clinical-value treatments) to 20 (highest-clinical-value treatments); NCCN EB measures are scaled such that higher scores are always preferable. (A) Time-unlimited treatments; cost represents cost per month of therapy (N=541; linear R2=0.03). (B) Time-limited treatments; cost represents cost for full course of treatment (N=845; linear R2=0.004). For the 2 treatments with drug costs of $0, we assigned a cost of $1 to allow inclusion on a logarithmic scale.

Abbreviation: EB, Evidence Blocks.

Citation: Journal of the National Comprehensive Cancer Network J Natl Compr Canc Netw 18, 10; 10.6004/jnccn.2020.7574

When we assessed the independent association between each NCCN EB measure and the cost for time-unlimited treatments, we found that efficacy was positively associated with treatment cost (estimated change in cost associated with 1-unit increase on efficacy measure; $3,036; 95% CI, $1,782 to $4,289) as was quality of evidence ($1,509; 95% CI, $171 to $2,847), whereas safety (–$1,470; 95% CI, –$2,790 to –$151) and consistency of evidence (–$2,003; 95% CI, –$3,420 to –$586) were negatively associated with cost (Table 2). Among time-limited treatments, no NCCN EB measure was significantly associated with cost.

Table 2.

Association of NCCN EB Measures With Treatment Costs

Table 2.

Model estimates were largely consistent across sensitivity analyses (supplemental eTables 18). Among time-unlimited treatments, efficacy and quality of evidence were positively associated with cost and safety and consistency of evidence were negatively associated with cost; among time-limited treatments, NCCN EB measures were not associated with cost. A notable exception was fixed-effect modeling (supplemental eTable 8), which suggested a positive association between efficacy and cost and a negative association between quality of evidence and cost among time-limited treatments.

Discussion

This analysis suggests an association between the NCCN EB measures and treatment cost. However, in our primary analysis this association was present among only 1 group of treatments (those intended to be administered until disease progression or unacceptable toxicity, designated herein as “time-unlimited”) and explained little of the significant variation in treatment cost. Monthly treatment costs varied by tens of thousands of dollars within each level of the NCCN EB efficacy score; the estimated $3,036 greater cost per month associated with a 1-unit increase in efficacy suggests that this measure accounts for little of the difference in cost between treatments. Implicitly, treatment costs are determined primarily by factors other than their clinical “value,” as assessed by the NCCN EB measures.

The finding that some NCCN EB measures were—among the time-unlimited treatments—inversely associated with cost is unexpected and intriguing. Because NCCN EB measures are scaled so that higher scores are always “better,” this finding would imply that treatments tend to have higher prices when scoring worse on these measures. Because these associations were present on multivariable analysis, they would not be explained by a correlation between measures (eg, treatments with greater efficacy tended to have lower safety). Rather, all other things being equal, more-toxic treatments (for example) seem to be more costly. Because the inverse association between safety and cost was unaffected in the sensitivity analysis excluding supportive care costs, a greater need for supportive medications for more-toxic treatments also fails to explain this observation. The inverse associations between treatment cost and both Safety and consistency of evidence may support the hypothesis that treatment costs poorly reflect clinical value.

Sensitivity analyses were largely consistent with the primary analysis. One notable exception was the fixed-effect model, which found a positive association between efficacy and cost among both time-limited and time-unlimited treatments. This result suggests that such an association may be present, after accounting for the substantial intertreatment group variation in cost. However, the same model still failed to find an association with safety or consistency of evidence, and quality of evidence was negatively associated with cost.

This study has limitations related to the NCCN EB data source. Because the NCCN EB are determined through expert consensus there is some degree of subjectivity, which makes misclassification possible. Note also that in many cases in which we did not identify a statistically significant association between NCCN EB measures and costs, the wide confidence interval of our estimates indicated that our results would be consistent with potentially large associations.

Conclusions

To the extent that the associations identified in this study were inconsistent (evident in only 1 of the 2 treatment categories) and that the correlation between cost and clinical value was minimal (explaining little of the variation in cost), these findings suggest that the clinical value of treatments is not an important determinant of costs. However, our findings did suggest statistically significant associations between cost and some measures of clinical value; unexpectedly, some of these were “negative” associations, suggesting that less-valuable drugs were priced higher. Therefore, to the extent that these associations do reflect a role of clinical value in determining treatment costs, our functional definition of “value” may prioritize some components of value (effectiveness of treatments, or efficacy) while failing to account for others (toxicity of treatment, or safety).

References

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    Bach P. Monthly and median costs of cancer drugs at the time of FDA approval 1965-2015. Memorial Sloan Kettering Cancer Center. Accessed December 9, 2015. Available at: https://www.mskcc.org/research-areas/programs-centers/health-policy-outcomes/cost-drugs

  • 2.

    Dusetzina SB, Huskamp HA, Keating NL. Specialty drug pricing and out-of-pocket spending on orally administered anticancer drugs in Medicare Part D, 2010 to 2019. JAMA 2019;321:20252028.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3.

    Alevizakos M, Gaitanidis A, Appleman L. Quantification of the financial burden of antineoplastic agent price increases [abstract]. J Clin Oncol 2019;37(Suppl):Abstract 6519.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 4.

    Zafar SY. Financial toxicity of cancer care: it’s time to intervene. J Natl Cancer Inst 2015;108:djv370.

  • 5.

    Howard DH, Bach PB, Berndt ER, . Pricing in the market for anticancer drugs. J Econ Perspect 2015;29:139162.

  • 6.

    Barnes TA, Amir E, Templeton AJ, . Efficacy, safety, tolerability and price of newly approved drugs in solid tumors. Cancer Treat Rev 2017;56:17.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 7.

    Amir E, Seruga B, Martinez-Lopez J, . Oncogenic targets, magnitude of benefit, and market pricing of antineoplastic drugs. J Clin Oncol 2011;29:25432549.

  • 8.

    Mailankody S, Prasad V. Implications of proposed Medicare reforms to counteract high cancer drug prices. JAMA 2016;316:271272.

  • 9.

    Saluja R, Arciero VS, Cheng S, . Examining trends in cost and clinical benefit of novel anticancer drugs over time. J Oncol Pract 2018;14:e280294.

  • 10.

    Becker DJ, Lin D, Lee S, . Exploration of the ASCO and ESMO value frameworks for antineoplastic drugs. J Oncol Pract 2017;13:e653665.

  • 11.

    Vivot A, Jacot J, Zeitoun JD, . Clinical benefit, price and approval characteristics of FDA-approved new drugs for treating advanced solid cancer, 2000-2015. Ann Oncol 2017;28:11111116.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12.

    Del Paggio JC, Sullivan R, Schrag D, . Delivery of meaningful cancer care: a retrospective cohort study assessing cost and benefit with the ASCO and ESMO frameworks. Lancet Oncol 2017;18:887894.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 13.

    Mailankody S, Prasad V. Five years of cancer drug approvals: innovation, efficacy, and costs. JAMA Oncol 2015;1:539540.

  • 14.

    Soriot P. Statement of Pascal Soriot, chief executive officer, AstraZeneca, before the Committee on Finance, United States Senate, concerning drug pricing in America: a prescription for change, part II. Accessed September 27, 2019. Available at: https://www.finance.senate.gov/imo/media/doc/26FEB2019SORIOT-ASTRAZENECA.pdf

  • 15.

    Harris G. Cost of developing new medicine swelled to $802 million, research study reports. The Wall Street Journal. December 3, 2001. Accessed September 27, 2019. Available at: https://www.wsj.com/articles/SB1007336440403996240

  • 16.

    Burkholder R. Pricing and value of cancer drugs. JAMA Oncol 2015;1:841842.

  • 17.

    Carlson RW, Jonasch E. NCCN Evidence Blocks. J Natl Compr Canc Netw 2016;14(5 Suppl):616619.

  • 18.

    Shah-Manek B, Wong W, Ravelo A, . Oncologists’ perceptions of drug affordability using NCCN Evidence Blocks: results from a national survey. J Manag Care Spec Pharm 2018;24:565571.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19.

    Bach PB. Limits on Medicare’s ability to control rising spending on cancer drugs. N Engl J Med 2009;360:626633.

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Submitted December 21, 2019; accepted for publication April 6, 2020.

Author contributions: Study concept and design: All authors. Dataset creation: Mitchell, Tabatabai, Dey, Ohn. Data analysis: Mitchell, Curry. Results presentation: Mitchell, Tabatabai, Dey, Bach. Approval of final manuscript: All authors.

Disclosures: Dr. Mitchell has disclosed that he received a research award from Conquer Cancer Foundation, partially funded by Merck. Dr. Bach has disclosed that he has received personal fees from WebMD, Defined Health, JMP Securities, Mercer, Foundation Medicine, Grail, Morgan Stanley, Oppenheimer & Co, Cello Health, Oncology Analytics, Anthem, Magellan Health, America’s Health Insurance Plans, and EQRx; grants from Kaiser Permanente and Arnold Ventures; nonfinancial support from Oppenheimer & Co, America’s Health Insurance Plans, and Oncology Analytics; and other from Oncology Analytics. The remaining authors have disclosed that they have not received any financial consideration from any person or organization to support the preparation, analysis, results, or discussion of this article.

Correspondence: Aaron P. Mitchell, MD, MPH, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 485 Lexington Avenue, New York, NY 10017. Email: mitchea2@mskcc.org

Supplementary Materials

  • View in gallery

    Distribution of treatment costs and NCCN EB scores. The x axis represents the numeric sum of the efficacy, safety, quality of evidence, and consistency of evidence scores for each treatment. Because each measure ranges from 1 to 5, the x axis range is from 4 (lowest-clinical-value treatments) to 20 (highest-clinical-value treatments); NCCN EB measures are scaled such that higher scores are always preferable. (A) Time-unlimited treatments; cost represents cost per month of therapy (N=541; linear R2=0.03). (B) Time-limited treatments; cost represents cost for full course of treatment (N=845; linear R2=0.004). For the 2 treatments with drug costs of $0, we assigned a cost of $1 to allow inclusion on a logarithmic scale.

    Abbreviation: EB, Evidence Blocks.

  • 1.

    Bach P. Monthly and median costs of cancer drugs at the time of FDA approval 1965-2015. Memorial Sloan Kettering Cancer Center. Accessed December 9, 2015. Available at: https://www.mskcc.org/research-areas/programs-centers/health-policy-outcomes/cost-drugs

  • 2.

    Dusetzina SB, Huskamp HA, Keating NL. Specialty drug pricing and out-of-pocket spending on orally administered anticancer drugs in Medicare Part D, 2010 to 2019. JAMA 2019;321:20252028.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3.

    Alevizakos M, Gaitanidis A, Appleman L. Quantification of the financial burden of antineoplastic agent price increases [abstract]. J Clin Oncol 2019;37(Suppl):Abstract 6519.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 4.

    Zafar SY. Financial toxicity of cancer care: it’s time to intervene. J Natl Cancer Inst 2015;108:djv370.

  • 5.

    Howard DH, Bach PB, Berndt ER, . Pricing in the market for anticancer drugs. J Econ Perspect 2015;29:139162.

  • 6.

    Barnes TA, Amir E, Templeton AJ, . Efficacy, safety, tolerability and price of newly approved drugs in solid tumors. Cancer Treat Rev 2017;56:17.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 7.

    Amir E, Seruga B, Martinez-Lopez J, . Oncogenic targets, magnitude of benefit, and market pricing of antineoplastic drugs. J Clin Oncol 2011;29:25432549.

  • 8.

    Mailankody S, Prasad V. Implications of proposed Medicare reforms to counteract high cancer drug prices. JAMA 2016;316:271272.

  • 9.

    Saluja R, Arciero VS, Cheng S, . Examining trends in cost and clinical benefit of novel anticancer drugs over time. J Oncol Pract 2018;14:e280294.

  • 10.

    Becker DJ, Lin D, Lee S, . Exploration of the ASCO and ESMO value frameworks for antineoplastic drugs. J Oncol Pract 2017;13:e653665.

  • 11.

    Vivot A, Jacot J, Zeitoun JD, . Clinical benefit, price and approval characteristics of FDA-approved new drugs for treating advanced solid cancer, 2000-2015. Ann Oncol 2017;28:11111116.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12.

    Del Paggio JC, Sullivan R, Schrag D, . Delivery of meaningful cancer care: a retrospective cohort study assessing cost and benefit with the ASCO and ESMO frameworks. Lancet Oncol 2017;18:887894.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 13.

    Mailankody S, Prasad V. Five years of cancer drug approvals: innovation, efficacy, and costs. JAMA Oncol 2015;1:539540.

  • 14.

    Soriot P. Statement of Pascal Soriot, chief executive officer, AstraZeneca, before the Committee on Finance, United States Senate, concerning drug pricing in America: a prescription for change, part II. Accessed September 27, 2019. Available at: https://www.finance.senate.gov/imo/media/doc/26FEB2019SORIOT-ASTRAZENECA.pdf

  • 15.

    Harris G. Cost of developing new medicine swelled to $802 million, research study reports. The Wall Street Journal. December 3, 2001. Accessed September 27, 2019. Available at: https://www.wsj.com/articles/SB1007336440403996240

  • 16.

    Burkholder R. Pricing and value of cancer drugs. JAMA Oncol 2015;1:841842.

  • 17.

    Carlson RW, Jonasch E. NCCN Evidence Blocks. J Natl Compr Canc Netw 2016;14(5 Suppl):616619.

  • 18.

    Shah-Manek B, Wong W, Ravelo A, . Oncologists’ perceptions of drug affordability using NCCN Evidence Blocks: results from a national survey. J Manag Care Spec Pharm 2018;24:565571.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19.

    Bach PB. Limits on Medicare’s ability to control rising spending on cancer drugs. N Engl J Med 2009;360:626633.

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