Although currently underutilized, granulocyte colony-stimulating factor prophylaxis as supportive cancer care provides substantial value to society. Aligning utilization with clinical guidelines would increase this value considerably.
Objectives: Febrile neutropenia (FN) is a life-threatening complication of chemotherapy that can lead to hospitalizations, chemotherapy dose reductions or delays, and mortality. Granulocyte colony-stimulating factor (G-CSF) prophylaxis reduces the incidence of FN, enabling patients to undergo and remain on myelosuppressive chemotherapy. We estimate the benefits of continuing current G-CSF use patterns and an alternative that aligns prophylactic G-CSF use with guideline recommendations.
Study Design: Using The Health Economics Medical Innovation Simulation microsimulation, we estimated lifetime social value (SV) of prophylactic G-CSF for a nationally representative US population with breast, lung, and gynecological cancers and non-Hodgkin lymphoma.
Methods: SV estimates included the cost of G-CSF, FN, chemotherapy relative dose intensity (RDI) less than 85% (RDI<85%), medical spending, and deaths for 3 scenarios: current use (current G-CSF use), targeted use (100% G-CSF use among patients with high FN risk), and reduced use (current G-CSF use reduced by 20% across all FN risk categories).
Results: Over 10 years, current use, compared with no G-CSF use, would decrease cases of FN by 3.3 million, prevent 354,000 cases of RDI<85%, and generate $96 billion in SV. Compared with current use, targeted use would decrease cases of FN by an additional 3.3 million, prevent 355,000 more cases of RDI<85%, and generate another $119 billion in SV. Reduced use would increase FN and RDI<85%, lowering SV by $18 billion compared with current use.
Conclusions: Current use of G-CSF prophylaxis would provide $96 billion in SV over the next 10 years. Targeting G-CSF prophylaxis to align with guidelines would more than double SV, highlighting the substantial value of appropriate FN risk assessment and targeted G-CSF prophylaxis.
Am J Manag Care. 2019;25(10):486-493Takeaway Points
We modeled the benefits of prophylactic granulocyte colony-stimulating factor (G-CSF) use among patients with breast, lung, and gynecological cancers and non-Hodgkin lymphoma.
Over the past 3 decades, medical and pharmaceutical innovations have made substantial strides in many disease areas that have increased both longevity and quality of life (QOL). However, these technological advancements are often met with criticism over high prices, and, despite research showing that the benefits can outweigh the costs of new therapies, these treatments may often be underutilized.1-3 For example, statins represent a huge technological breakthrough for the treatment of cardiovascular disease, and a large literature of clinical trial data and cost-effectiveness modeling demonstrates their wide-ranging cost-effectiveness, yet data still suggest that this breakthrough therapy is greatly underused.3
Similarly, prophylactic administration of granulocyte colony-stimulating factor (G-CSF) is underutilized despite guideline recommendations and literature on the cost-effectiveness of varying options.4-6 G-CSF use is well established as the standard of supportive care among patients receiving myelosuppressive chemotherapy to prevent the onset of febrile neutropenia (FN; fever and infection),7-9 which subsequently can lead to hospitalizations, dose reductions, and increased risk of mortality.10,11 National Comprehensive Cancer Network (NCCN) and American Society of Clinical Oncology guidelines recommend primary prophylactic G-CSF in patients receiving a chemotherapy regimen associated with more than a 20% risk of FN. The guidelines also suggest that G-CSF therapy should be considered for patients receiving a chemotherapy regimen with a 10% to 20% risk of FN and who have additional risk factors (eg, age ≥65 years, poor performance status, history of FN, comorbid conditions).12 Despite these clear and well-respected guidelines and evidence from meta-analyses supporting the favorable benefit—risk profile of G-CSFs,13 real-world use is lower than recommended. For example, only 51% of patients with breast cancer and 28% of patients with lung cancer with high risk of FN have been documented as receiving G-CSF prophylaxis.14
G-CSF prophylaxis continues to be the subject of much debate due to the rising costs of cancer care. Although previous research has noted the clinical benefits of these therapies and has demonstrated their cost-effectiveness, it has failed to completely account for their potential indirect benefits in the form of improved productivity and QOL.7,15,16 FN can be very debilitating and often leads to inpatient hospital stays and potentially death. Although some previous studies have accounted for reductions in QOL, they have not included lost work or reductions in productivity. There has been much discussion recently about the importance of including these indirect benefits in any value calculation, and both the Second Panel on Cost-Effectiveness17 and the International Society for Pharmacoeconomics and Outcomes Research’s Special Task Force on Value Assessment Frameworks18 recommended doing so where possible. Our study builds upon the current literature by including these indirect sources of value in our model and estimating the accumulation of value over patients’ lifetimes.
Additionally, our model builds on the current literature by estimating the social value (SV) both at the US population level of current G-CSF prophylaxis use and at a level of use better aligned with NCCN guidelines. SV quantifies the resources that society would be willing to give up in order to achieve the health, QOL, and productivity benefits associated with G-CSF prophylaxis. This study represents the first comprehensive estimate of the benefit to society from G-CSF prophylaxis use that better aligns with guidelines.
We used The Health Economics Medical Innovation Simulation (THEMIS), an established microsimulation model based on the Future Elderly Model,19,20 to predict the lifetime value associated with use of G-CSF consistent with NCCN guidelines.12 A patient-level microsimulation like THEMIS allows costs and benefits to be estimated over individuals’ lifetimes within a variety of subgroups while capturing the heterogeneity that would be lost in a cohort simulation. Our model focused on 4 patient populations with high rates of use of myelosuppressive chemotherapy regimens categorized as high and intermediate FN risk,12 and it documented use of G-CSF prophylaxis that is not consistent with NCCN guidelines—overuse in the population at low risk of FN and/or underuse in the population at high risk of FN. Specifically, we focused on patients with breast, gynecological, and lung cancers and non-Hodgkin lymphoma (NHL) who were 25 years or older, and we simulated their health status, health spending, and mortality experience over their lifetimes.
THEMIS is based on data from the Panel Study of Income Dynamics (PSID)21 and the Health and Retirement Study.22 Both data sets are nationally representative panel surveys that have been ongoing since 1968 and 1992, respectively. The breast, gynecological, and lung cancer cohorts were modeled using the PSID data; however, the survey does not differentiate patients with NHL from patients with other lymphomas. Thus, we built a synthetic cohort for the NHL population based on Surveillance and Epidemiology End Results (SEER) data. A raking procedure, which adjusts the weights of the PSID cancer population to match the distributions of the NHL population in SEER, was combined with SEER NHL prevalence and incidence data and the National Cancer Institute lymphoma incidence projections to compute a population of prevalent and incident NHL cases.23-27
To estimate the potential impact of G-CSF prophylaxis on financial outcomes, we supplemented the PSID data with medical spending data from the Medicare Current Beneficiary Survey and Medical Expenditure Panel Survey, depending on the individual’s Medicare eligibility. This allowed us to estimate the change in medical costs that occurs with the use of G-CSF as well as the long-term effects over the lifetime of patients. Further detail on the mechanics and implementation of the model are described in Kabiri et al.28
The risk of FN, and thus the appropriateness of G-CSF prophylaxis, depended on the toxicity of chemotherapy and other patient characteristics according to NCCN guidelines.12 We stratified patients with cancer into 3 groups based on risk of FN due to chemotherapy: high risk of FN, defined as greater than 20% risk, or 10% to 20% risk with additional risk factors (eg, being ≥65 years); intermediate risk of FN, defined as 10% to 20% risk without additional patient risk factors; and low risk of FN, defined as 0% to 10% risk. We incorporated the appropriate distribution of FN risk for each cancer type according to data presented in Table 1.
Simulated incident patients with cancer who received chemotherapy were at risk of developing FN and of decreased relative dose intensity (RDI) of treatment, depending on the individual’s cancer type, FN risk category, and use of G-CSF prophylaxis. We based the rates of FN and RDI less than 85% (RDI<85%) on the literature (Table 1).
We assumed that each FN event reduced patients’ QOL by 0.022 quality-adjusted life-years (QALYs). In addition, our model included FN mortality rates during treatment as the percentage of total deaths for each cancer type and included a calculated hazard ratio for death of 1.46 for RDI<85% across all cancer types. See the eAppendix (available at ajmc.com) for sources of model parameters and assumptions.
G-CSF Coverage Scenarios
To model the SV of G-CSF use, we simulated 1 baseline and 3 G-CSF coverage scenarios. The baseline scenario assumed no G-CSF use, representing care in a world without G-CSF coverage. The first scenario, current use, incorporated current rates of G-CSF prophylaxis in each FN risk category, by cancer type, from the recent literature. The second scenario, targeted use, measured the SV from G-CSF coverage that aligned with the NCCN guidelines for primary prophylaxis with G-CSF. We assumed that all high-risk patients received G-CSF prophylaxis and all intermediate-risk and low-risk patients did not. Under the third scenario, reduced use, we assumed that current G-CSF use fell by 20% in each of the 3 FN risk categories, representing the potential impact of lower rates of G-CSF prophylaxis in response to cost concerns. The rates of G-CSF use by risk group and cancer type are presented in Table 2. The scenarios were run within each cancer population and aggregated for the results presented here. See the eAppendix for sources of model parameters and assumptions.
Short- Versus Long-Acting G-CSF Scenario
Amid concerns over rising drug expenditures in the United States, a potential approach to minimize costs while still providing G-CSF prophylaxis could be to substitute the use of pegfilgrastim (long-acting G-CSF) with lower-cost, multidose filgrastim (short-acting G-CSF). Such a substitution would allow access to G-CSF therapies for a reduced cost. However, many studies have established the superior efficacy of pegfilgrastim over filgrastim. To test how such a strategy would affect patients, we simulated 1 additional scenario assuming a reduction in pegfilgrastim use by 20% and a corresponding increase in filgrastim use by 20%. We assumed that 6 doses of filgrastim were used per cycle, according to the median real-world utilization. We assumed that pegfilgrastim and filgrastim are associated with odds ratios of FN of 0.24 and 0.51, respectively, and that 4% and 5% of patients experience RDI<85%, respectively. See the eAppendix for sources of model parameters and assumptions.
We included estimates of the cost of FN-related inpatient and outpatient encounters by cancer type from the literature and adjusted to 2017 US$ using the Medical Consumer Price Index. We also estimated the costs of G-CSF prophylaxis assuming pegfilgrastim use in each cancer type. We assumed 6 cycles of chemotherapy and therefore 6 cycles of G-CSF prophylaxis for NHL and breast and gynecological cancers and 4 chemotherapy and G-CSF prophylaxis cycles for lung cancer. The model had a societal perspective, so the drug price represented the average price paid across all payers. We used the average wholesale acquisition cost for pegfilgrastim. To account for the introduction of biosimilar competition in the marketplace, we reduced the price of pegfilgrastim by 15% in 2019 and by an additional 15% in 2020.29 All cost estimates are shown in Table 1. See the eAppendix for sources of model parameters and assumptions.
We compared the incidence of FN, RDI<85%, and death across G-CSF scenarios. Productivity was measured through additional earnings from avoided deaths and reduced caregiver burden ($4413 per FN episode for patient and caregiver work loss).30 Additionally, we calculated QALYs, total prophylaxis cost, and medical spending under each modeling scenario. This allowed us to calculate the total SV generated under each scenario estimated as QALYs gained, which were each valued at $150,00031 plus increased total earnings minus increased medical spending (including treatment cost). We used the formula:
SV = ∑Tt=1 ∑Ni=1($QALYsit + ∆Earningit) − (Medical spendingit + G-CSF costit)
where i represents individual and t represents each year of the simulation period. For an individual, this calculation is done over a lifetime, starting at treatment initiation. The results for G-CSF scenarios were calculated in 2017 US$ and compared with the baseline (no G-CSF) scenario. Lifetime SV of G-CSF was estimated over a 10-year horizon, between 2017 and 2027.
FN, RDI<85%, and Death
Both current and targeted use of G-CSF prophylaxis resulted in fewer annual cases of FN compared with the scenario of no G-CSF use. As seen in Figure 1, the benefits of G-CSF prophylaxis can be observed immediately and continue to increase over time. By 2027 (after year 10), current use and targeted use of G-CSF prophylaxis were estimated to prevent about 340,000 and 700,000 FN cases per year, respectively. The cumulative cases of FN prevented over 2 and 10 years are displayed in Table 3.
Similarly, occurrences of RDI<85% were reduced under both current and targeted use of G-CSF prophylaxis compared with the scenario of no G-CSF use. As shown in Figure 2, current use and targeted use of G-CSF prophylaxis led to an estimated 35,000 and 70,000 fewer occurrences of RDI<85%, on average, at year 10 (2027), respectively. The cumulative occurrences of RDI<85% prevented over 2 and 10 years are displayed in Table 3.
Reductions in both FN and RDI<85% reduced annual patient mortality. These benefits are shown as the cumulative number of deaths prevented at 2 and 10 years displayed in Table 3. Compared with no G-CSF use, current G-CSF use prevented more than 27,000 deaths after 2 years and more than 117,000 deaths after 10 years. Similarly, targeted G-CSF use prevented more than 48,000 and 222,000 deaths over 2 and 10 years, respectively.
QALYs, Prophylaxis Cost, Medical Spending, and SV
The average life expectancy gains for each of the G-CSF prophylaxis scenarios are shown in Table 3. Under the current G-CSF use scenario, patients treated over the next 10 years gained about 570,000 QALYs over their baseline counterparts. Targeted G-CSF use generated even greater life expectancy benefits than witnessed with current G-CSF use. Compared with no G-CSF use, patients treated under the targeted G-CSF scenario over the next 10 years generated 970,000 QALYs.
Cumulative prophylaxis costs and medical spending for each scenario over 2 and 10 years are shown in Table 3. Compared with no G-CSF prophylaxis, both current and targeted use of G-CSF reduced medical spending (excluding prophylaxis cost) over 10 years, such that total spending (including prophylaxis cost) was less than in the scenario of no G-CSF use. These results suggested that the cost offsets from G-CSF prophylaxis are greater than the prophylaxis costs; thus, G-CSF prophylaxis is cost-effective at all positive QALY thresholds.
Finally, we measured SV as the estimated benefits that G-CSF prophylaxis generated for society net of its costs. The components of this calculation are shown in Table 3. SV was positive for all G-CSF use scenarios, but targeted use generated substantially more value than current and reduced G-CSF use across all periods.
After only 2 years, we estimated that the current use of G-CSF prophylaxis would generate more than $15 billion in value to society, or $9700 per patient. However, if G-CSF use were targeted to patients with a high risk of FN, the value to society would more than double to almost $40 billion, or $16,400 per patient. After 10 years, the SV increased to $96 billion ($14,700 per patient) and $215 billion ($21,000 per patient) for current and targeted G-CSF use scenarios, respectively, compared with baseline. The per-patient SV increases over time as a result of the decrease in mortality due to G-CSF prophylaxis use.
Reduced Pegfilgrastim Use Results
A 20% reduction in pegfilgrastim use in favor of filgrastim saved $2.0 billion and $3.5 billion in prophylaxis costs over 2 and 10 years, respectively, compared with current G-CSF use. However, the shift also led to an additional $4.0 billion and $17.0 billion in medical spending from FN events over 2 and 10 years, respectively. Ultimately, reduced use of pegfilgrastim resulted in a loss of $2.7 billion and $14.9 billion in SV over 2 and 10 years, respectively.
We used a microsimulation model to examine the SV of aligning primary prophylactic G-CSF use with guideline recommendations over a 10-year period. Our model estimated that current use of G-CSF prophylaxis would decrease the total number of FN cases by 3.3 million, prevent more than 350,000 cases of RDI<85%, and generate $96 billion in net SV over 10 years compared with a no G-CSF scenario. We estimated that the targeted use of G-CSF prophylaxis, defined according to the NCCN guidelines, would reduce the total number of FN cases and cases of RDI<85% by 6.6 million and 709,000, respectively, in addition to gaining $215 billion in SV. Finally, our model estimated that shifting from pegfilgrastim to filgrastim would result in increased rates of FN and RDI<85%, in addition to higher medical spending and lower net SV.
We note that the large reduction in cases of FN relative to the reduction in cases of RDI<85% is due to the fact that cases of FN were estimated per cycle of chemotherapy and cases of RDI<85% were estimated per course of chemotherapy treatment.
Previous cost-effectiveness analyses of G-CSF prophylaxis have been conducted across a variety of treatment conditions, including specific tumor types as well as solid tumors more broadly; different clinical contexts; primary versus secondary prophylaxis; and long-acting pegfilgrastim versus short-acting filgrastim. Across all of these options, primary prophylaxis with pegfilgrastim has been identified as being cost-effective at traditionally accepted thresholds for the prevention of FN in patients with cancer receiving myelosuppressive chemotherapy5,6,32-35; however, these studies have several limitations. First, they fail to fully capture the impact of FN and G-CSF prophylaxis on patient productivity. Second, they assume 100% compliance with the G-CSF prophylaxis options being evaluated, despite mounting evidence suggesting that compliance is far less.36-43 Our analysis modeled increased productivity and health-related QOL due to reduced incidence of FN, which provides a more comprehensive evaluation of G-CSF prophylaxis. We also incorporated estimates of suboptimal use of G-CSF prophylaxis (specifically underuse in high-risk patients and overuse in low-risk patients) to provide an estimate of inclusive SV.
Our study does have limitations worth noting. First, parameters for the model were taken from the current literature. When published estimates did not exist for parameters by tumor type, we relied on reasonable assumptions and input from clinical experts. Second, the PSID data do not include information on most of the patient risk factors for FN. Thus, our FN risk group categorization relied solely on chemotherapy risk and patient age. Additionally, we were unable to account for the differential rates of FN and RDI<85% by cancer stage and histology. Although we recognize that RDI<85% may not equally affect survival across cancer types, disease stages, and regimens, the literature supports that RDI≥85% has a favorable impact on survival.44 Third, the efficacy of G-CSF prophylaxis on the risk of FN is not as well defined for intermediate- and low-risk groups. Thus, we assumed the same impact of G-CSF on the incidence of FN for all risk groups. This could potentially over- or underestimate the impact of G-CSF prophylaxis in these groups. Fourth, we did not account for the impact of newer G-CSF administration options on guideline adherence from a prescribing perspective or medication adherence at the patient level. For example, the on-body injector allows patients and caregivers to remain at home and thus avoid the caregiver burden and lost work days associated with next-day clinic visits, which have been identified as both a patient- and practice-related rationale for suboptimal prophylaxis.45,46 Finally, we did not account for the evolving treatment landscape in cancer. As more targeted therapies and immunotherapies become available, chemotherapy treatment may decline, although these therapies also can be used with combinations of myelosuppressive chemotherapy.47,48 Thus, we show the value gained over 2 years and over 10 years. Treatment patterns are not likely to change drastically in the next 2 years, and the introduction of newer therapies over the next decade does not change our conclusions.
Our results suggest that G-CSF therapies provide tremendous value to society. Better targeting G-CSF to align with current guidelines would double this value. Rather than reducing G-CSF use or switching use to less-costly short-acting G-CSFs, efforts to support appropriate FN risk assessment in all patients receiving myelosuppressive chemotherapy, with consequent targeted G-CSF prophylaxis, could add considerable value.Author Affiliations: Precision Health Economics (ASW, MK, ARS, EvE, DG), Los Angeles, CA; Global Health Economics (AY, MB) and US Medical (CB), Amgen, Thousand Oaks, CA.
Source of Funding: Amgen Inc provided funding for this research.
Author Disclosures: Drs Ward and Kabiri, Ms Silverstein, and Ms van Eijndhoven are employees of Precision Health Economics, which received consulting fees for this study. Drs Yucel, Bowers, and Bensink are employees of and own stock in Amgen. Dr Goldman is a consultant to Precision Health Economics and a scientific advisor to ACADIA Pharmaceuticals; owns equity (<1%) in Precision Medicine Group, the parent company of Precision Health Economics; received honoraria from The Aspen Institute and lecture fees from Celgene; and is funded through the Leonard D. Schaeffer Center for Health Policy & Economics, which is supported by gifts and grants from individuals, corporations, and associations; by government grants and contracts; and by private foundations (specific information about funding sources is available at healthpolicy.usc.edu).
Authorship Information: Concept and design (ASW, MK, AY, ARS, CB, MB, DG); acquisition of data (ASW, MK, ARS); analysis and interpretation of data (ASW, MK, AY, EvE, CB, MB, DG); drafting of the manuscript (ASW, MK, AY, ARS, EvE, MB); critical revision of the manuscript for important intellectual content (ASW, MK, AY, ARS, EvE, CB, MB, DG); statistical analysis (EvE); administrative, technical, or logistic support (MK, ARS); and supervision (ASW, AY, MB, DG).
Address Correspondence to: Alison Sexton Ward, PhD, Precision Health Economics, 11100 Santa Monica Blvd, Ste 500, Los Angeles, CA 90025. Email: firstname.lastname@example.org.REFERENCES
1. Baigent C, Blackwell L, Emberson J, et al; Cholesterol Treatment Trialists’ (CTT) Collaboration. Efficacy and safety of more intensive lowering of LDL cholesterol: a meta-analysis of data from 170,000 participants in 26 randomised trials. Lancet. 2010;376(9753):1670-1681. doi: 10.1016/S0140-6736(10)61350-5.
2. Chen Z, Peto R, Collins R, MacMahon S, Lu J, Li W. Serum cholesterol concentration and coronary heart disease in population with low cholesterol concentrations. BMJ. 1991;303(6797):276-282. doi: 10.1136/bmj.303.6797.276.
3. Grabowski DC, Lakdawalla DN, Goldman DP, et al. The large social value resulting from use of statins warrants steps to improve adherence and broaden treatment. Health Aff (Millwood). 2012;31(10):2276-2285. doi: 10.1377/hlthaff.2011.1120.
4. Fust K, Li X, Maschio M, et al. Cost-effectiveness of prophylaxis treatment strategies for febrile neutropenia in patients with recurrent ovarian cancer. Gynecol Oncol. 2014;133(3):446-453. doi: 10.1016/j.ygyno.2014.03.014.
5. Hill G, Barron R, Fust K, et al. Primary vs secondary prophylaxis with pegfilgrastim for the reduction of febrile neutropenia risk in patients receiving chemotherapy for non-Hodgkin’s lymphoma: cost-effectiveness analyses. J Med Econ. 2014;17(1):32-42. doi: 10.3111/13696998.2013.844160.
6. Ramsey SD, Liu Z, Boer R, et al. Cost-effectiveness of primary versus secondary prophylaxis with pegfilgrastim in women with early-stage breast cancer receiving chemotherapy. Value Health. 2009;12(2):217-225. doi: 10.1111/j.1524-4733.2008.00434.x.
7. Aarts MJ, Grutters JP, Peters FP, et al. Cost effectiveness of primary pegfilgrastim prophylaxis in patients with breast cancer at risk of febrile neutropenia. J Clin Oncol. 2013;31(34):4283-4289. doi: 10.1200/JCO.2012.48.3644.
8. Caggiano V, Weiss RV, Rickert TS, Linde-Zwirble WT. Incidence, cost, and mortality of neutropenia hospitalization associated with chemotherapy. Cancer. 2005;103(9):1916-1924. doi: 10.1002/cncr.20983.
9. Wang XJ, Lopez SE, Chan A. Economic burden of chemotherapy-induced febrile neutropenia in patients with lymphoma: a systematic review. Crit Rev Oncol Hematol. 2015;94(2):201-212. doi: 10.1016/j.critrevonc.2014.12.011.
10. Schilling MB, Parks C, Deeter RG. Costs and outcomes associated with hospitalized cancer patients with neutropenic complications: a retrospective study. Exp Ther Med. 2011;2(5):859-866. doi: 10.3892/etm.2011.312.
11. Shayne M, Crawford J, Dale DC, Culakova E, Lyman GH; ANC Study Group. Predictors of reduced dose intensity in patients with early-stage breast cancer receiving adjuvant chemotherapy. Breast Cancer Res Treat. 2006;100(3):255-262. doi: 10.1007/s10549-006-9254-4.
12. Crawford J, Becker PS, Armitage JO, et al. Myeloid growth factors, version 2.2017, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2017;15(12):1520-1541. doi: 10.6004/jnccn.2017.0175.
13. Lyman GH, Yau L, Nakov R, Krendyukov A. Overall survival and risk of second malignancies with cancer chemotherapy and G-CSF support. Ann Oncol. 2018;29(9):1903-1910. doi: 10.1093/annonc/mdy311.
14. Baig H, Somlo B, Eisen M, Stryker S, Bensink M, Morrow PK. Appropriateness of granulocyte colony-stimulating factor use in patients receiving myelosuppressive chemotherapy by febrile neutropenia risk level [published online September 10, 2018]. J Oncol Pharm Pract. doi: 10.1177/1078155218799859.
15. Cooper KL, Madan J, Whyte S, Stevenson MD, Akehurst RL. Granulocyte colony-stimulating factors for febrile neutropenia prophylaxis following chemotherapy: systematic review and meta-analysis. BMC Cancer. 2011;11(1):404. doi: 10.1186/1471-2407-11-404.
16. Wildiers H, Reiser M. Relative dose intensity of chemotherapy and its impact on outcomes in patients with early breast cancer or aggressive lymphoma. Crit Rev Oncol Hematol. 2011;77(3):221-240. doi: 10.1016/j.critrevonc.2010.02.002.
17. Sanders GD, Neumann PJ, Basu A, et al. Recommendations for conduct, methodological practices, and reporting of cost-effectiveness analyses: Second Panel on Cost-Effectiveness in Health and Medicine [erratum in JAMA. 2016;316(18):1924. doi: 10.1001/jama.2016.15518]. JAMA. 2016;316(10):1093-1103. doi: 10.1001/jama.2016.12195.
18. Lakdawalla DN, Doshi JA, Garrison LP Jr, Phelps CE, Basu A, Danzon PM. Defining elements of value in health care—a health economics approach: an ISPOR Special Task Force report . Value Health. 2018;21(2):131-139. doi: 10.1016/j.jval.2017.12.007.
19. Bhattacharya J, Shang B, Su CK, Goldman DP. Technological advances in cancer and future spending by the elderly. Health Aff (Millwood). 2005;24(suppl 2):W5-R53-W5-R66. doi: 10.1377/hlthaff.w5.r53.
20. Chernew ME, Goldman DP, Pan F, Shang B. Disability and health care spending among Medicare beneficiaries. Health Aff (Millwood). 2005;24(suppl 2):W5-R42-W5-R52. doi: 10.1377/hlthaff.w5.r42.
21. Panel Study of Income Dynamics. University of Michigan Institute for Social Research website. simba.isr.umich.edu/data/data.aspx. Accessed March 3, 2017.
22. Data products of the Center for the Study of Aging. RAND website. rand.org/well-being/social-and-behavioral-policy/centers/aging/dataprod.html. Accessed February 3, 2017.
23. Kalton G, Flores-Cervantes I. Weighting methods. J Off Stat. 2003;19(2):81-97.
24. Table 19.25: non-Hodgkin lymphoma. National Cancer Institute website. seer.cancer.gov/archive/csr/1975_2009_pops09/results_single/sect_19_table.25.pdf. Accessed September 8, 2017.
25. Table 19.1: non-Hodgkin lymphoma. National Cancer Institute website. seer.cancer.gov/archive/csr/1975_2015/results_merged/sect_19_nhl.pdf. Accessed September 8, 2017.
26. Cancer prevalence. National Cancer Institute website. costprojections.cancer.gov/cancer.prevalance.html. Accessed September 8, 2017.
27. Trask PC, Mehta J, Abbe A, RuizSoto R. Epidemiology projection trends for non-Hodgkin lymphoma (NHL) and its subtypes in the United States (US) and Europe (EU). Blood. 2012;120(21):5074.
28. Kabiri M, Brauer M, Shafrin J, Sullivan J, Gill TM, Goldman DP. Long-term health and economic value of improved mobility among older adults in the United States. Value Health. 2018;21(7):792-798. doi: 10.1016/j.jval.2017.12.021.
29. Atteberry P, Bach PB, Ohn JA, Trusheim M. Biologics are natural monopolies (part 1): why biosimilars do not create effective competition. Health Affairs Blog website. healthaffairs.org/do/10.1377/hblog20190405.396631/full/. Published April 15, 2019. Accessed May 7, 2019.
30. Calhoun EA, Chang CH, Weishman EE, Fishman DA, Lurain JR, Bennett CL. Evaluating the total costs of chemotherapy-induced toxicity: results from a pilot study with ovarian cancer patients. Oncologist. 2001;6(5):441-445. doi: 10.1634/theoncologist.6-5-441.
31. Viscusi WK, Aldy JE. The value of a statistical life: a critical review of market estimates throughout the world. J Risk Uncertain. 2003;27(1):5-76. doi: 10.1023/A:1025598106257.
32. Eldar-Lissai A, Cosler LE, Culakova E, Lyman GH. Economic analysis of prophylactic pegfilgrastim in adult cancer patients receiving chemotherapy. Value Health. 2008;11(2):172-179. doi: 10.1111/j.1524-4733.2007.00242.x.
33. Fust K, Li X, Maschio M, et al. Cost-effectiveness analysis of prophylaxis treatment strategies to reduce the incidence of febrile neutropenia in patients with early-stage breast cancer or non-Hodgkin lymphoma. Pharmacoeconomics. 2017;35(4):425-438. doi: 10.1007/s40273-016-0474-0.
34. Lyman G, Lalla A, Barron R, Dubois RW. Cost-effectiveness of pegfilgrastim versus 6-day filgrastim primary prophylaxis in patients with non-Hodgkin’s lymphoma receiving CHOP-21 in United States. Curr Med Res Opin. 2009;25(2):401-411. doi: 10.1185/03007990802636817.
35. Lyman GH, Lalla A, Barron RL, Dubois RW. Cost-effectiveness of pegfilgrastim versus filgrastim primary prophylaxis in women with early-stage breast cancer receiving chemotherapy in the United States. Clin Ther. 2009;31(5):1092-1104. doi: 10.1016/j.clinthera.2009.05.003.
36. Henk HJ, Becker L, Tan H, et al. Comparative effectiveness of pegfilgrastim, filgrastim, and sargramostim prophylaxis for neutropenia-related hospitalization: two US retrospective claims analyses. J Med Econ. 2013;16(1):160-168. doi: 10.3111/13696998.2012.734885.
37. Naeim A, Henk HJ, Becker L, et al. Pegfilgrastim prophylaxis is associated with a lower risk of hospitalization of cancer patients than filgrastim prophylaxis: a retrospective United States claims analysis of granulocyte colony-stimulating factors (G-CSF). BMC Cancer. 2013;13(1):11. doi: 10.1186/1471-2407-13-11.
38. Weycker D, Li X, Figueredo J, Barron R, Tzivelekis S, Hagiwara M. Risk of chemotherapy-induced febrile neutropenia in cancer patients receiving pegfilgrastim prophylaxis: does timing of administration matter? Support Care Cancer. 2016;24(5):2309-2316. doi: 10.1007/s00520-015-3036-7.
39. Weycker D, Li X, Barron R, et al. Risk of chemotherapy-induced febrile neutropenia with early discontinuation of pegfilgrastim prophylaxis in US clinical practice. Support Care Cancer. 2016;24(6):2481-2490. doi: 10.1007/s00520-015-3039-4.
40. Weycker D, Hackett J, Edelsberg JS, Oster G, Glass AG. Are shorter courses of filgrastim prophylaxis associated with increased risk of hospitalization? Ann Pharmacother. 2006;40(3):402-407. doi: 10.1345/aph.1G516.
41. Weycker D, Bensink M, Wu H, Doroff R, Chandler D. Risk of chemotherapy-induced febrile neutropenia with early discontinuation of pegfilgrastim prophylaxis based on real-world data from 2010 to 2015. Curr Med Res Opin. 2017;33(12):2115-2120. doi: 10.1080/03007995.2017.1386638.
42. Weycker D, Bensink M, Lonshteyn A, Doroff R, Chandler D. Risk of chemotherapy-induced febrile neutropenia by day of pegfilgrastim prophylaxis in US clinical practice from 2010 to 2015. Curr Med Res Opin. 2017;33(12):2107-2113. doi: 10.1080/03007995.2017.1386858.
43. Weycker D, Hanau A, Lonshteyn A, et al. Risk of chemotherapy-induced febrile neutropenia with same-day versus next-day pegfilgrastim prophylaxis among patients aged ≥65 years: a retrospective evaluation using Medicare claims. Curr Med Res Opin. 2018;34(9):1705-1711. doi: 10.1080/03007995.2018.1495621.
44. Havrilesky LJ, Reiner M, Morrow PK, Watson H, Crawford J. A review of relative dose intensity and survival in patients with metastatic solid tumors. Crit Rev Oncol Hematol. 2015;93(3):203-210. doi: 10.1016/j.critrevonc.2014.10.006.
45. Arvedson T, O’Kelly J, Yang BB. Design rationale and development approach for pegfilgrastim as a long-acting granulocyte colony-stimulating factor. BioDrugs. 2015;29(3):185-198. doi: 10.1007/s40259-015-0127-4.
46. Marion S, Tzivelekis S, Darden C, et al. “Same-day” administration of pegfilgrastim following myelosuppressive chemotherapy: clinical practice and provider rationale. Support Care Cancer. 2016;24(9):3889-3896. doi: 10.1007/s00520-016-3193-3.
47. Ashraf N. Atezolizumab treatment of nonsquamous NSCLC. N Engl J Med. 2018;379(12):1187-1188. doi: 10.1056/NEJMc1809195.
48. Paz-Ares L, Luft A, Vicente D, et al; KEYNOTE-407 Investigators. Pembrolizumab plus chemotherapy for squamous non—small-cell lung cancer. N Engl J Med. 2018;379(21):2040-2051. doi: 10.1056/NEJMoa1810865.