Compared with single-marker genetic testing (SMGT), multigene panel sequencing (MGPS) has the potential to identify more patients with cancer who could benefit from targeted therapies, but the effects on outcome and total cost of care are uncertain. Our goal was to estimate the clinical and cost effectiveness of MGPS versus SMGT among patients with advanced non–small-cell lung cancer (aNSCLC).

Patients with aNSCLC—stage IIIB or metastatic—who were diagnosed between 2011 and 2016 were identified from the Flatiron Health database. After stratifying patients into MGPS or SMGT cohorts, we analyzed the percentage of patients who received targeted treatment, survival, and total costs of care. SMGT included epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase testing. MGPS also allowed for the detection of BRAF, RET, ROS1, HER2, and MET mutations. Cost data sources were the Centers for Medicare & Medicaid Services Fee Schedule and 2017 average sales price drug cost. We estimated the incremental cost-effectiveness ratio from a US payer perspective over a lifetime horizon using a decision model.

We identified 5,688 patients with aNSCLC who received MGPS (n = 875) or SMGT (n = 4,813), of which 22% tested positive for epidermal growth factor receptor (18.5% MGPS; 17.3% SMGT) or anaplastic lymphoma kinase (3.59% MGPS; 3.78% SMGT). Among MGPS-tested patients, an additional 8% were found to have BRAF, RET, ROS1, HER2, or MET mutations. Of MGPS-tested patients, 21% received treatments that were targeted to the specific mutations versus 19% with SMGT. Expected survival was 1.14 life years (LYs) in SMGT versus 1.20 LYs in MGPS. Lifetime total costs were $8,814 higher per patient for MGPS. The incremental cost-effectiveness ratio of MGPS versus SMGT was $148,478 per LY gained.

On the basis of data from a nationwide oncology patient database, MGPS is shown to have moderate cost effectiveness compared with SMGT in patients with aNSCLC.

After the discovery of specific somatic genetic alterations associated with malignant transformation, the development and clinical use of oral targeted therapies and companion genomic biomarker tests has fundamentally changed the treatment of patients with advanced non–small-cell lung cancer (aNSCLC). Although each specific mutation is relatively uncommon, previous research has demonstrated that approximately 25% of patients with aNSCLC are potential candidates for oral targeted treatments.1 Several guidelines now recommend the routine genetic evaluation of tumor specimens at the time of documentation of metastatic disease.2,3

Single-marker genetic testing (SMGT) companion assays are available for most actionable targets, but as the number of known genetic alterations and targeted therapies increases,1,4,5 high-throughput parallel DNA multigene panel sequencing (MGPS) offers the potential to streamline diagnostic processes to inform treatment selection.6

MGPS, however, is generally more expensive than SMGT, and experts have voiced concerns that the use of MGPS will encourage off-label prescribing of targeted agents that have not been sufficiently evaluated for safety and efficacy.7 Whereas MGPS holds promise to improve testing efficiency, the clinical utility of MGPS has not yet been established in community oncology settings. Moreover, no study has directly compared economic outcomes for patients with aNSCLC who are managed with MGPS versus SMGT.8 Therefore, using simulation modeling informed with data from a large longitudinal, demographically and geographically diverse database drawn from electronic health records (EHRs) that include detailed genomic test information, we sought to estimate the expected cost effectiveness of MGPS versus SGMT for patients with aNSCLC.


We developed a decision model in Excel (Microsoft, Redmond, WA) to compare health outcomes and costs related to testing with MGPS (defined as a ≥ 30-gene next-generation sequencing [NGS] panel) or SMGT (single-gene tests or < 30-gene NGS panel; SMGT tests include fluorescence in situ hybridization [53%], polymerase chain reaction [45%], and other in-house developed tests [2%]) in a population of patients who were diagnosed with aNSCLC. The model is populated with estimates derived from analysis of patient-level data from the Flatiron Health electronic health record (EHR) –derived database, a longitudinal, demographically and geographically diverse database containing curated clinical information from 191 community and academic oncology practices nationwide—at the time data were obtained for this study—plus previously published population-level data from peer-reviewed data sources.9 Model outcomes reported are expected life-years (LYs) and direct medical costs over a lifetime horizon from a US payer perspective. Costs are presented in 2017 US dollars. Future costs and LYs were discounted at 3% annually.10

Model Structure

For each testing strategy, the model tracked patients who received oral targeted therapies for genetic driving mutations that were identified by genetic testing (hereafter referred to as targeted treatment), immunotherapy, chemotherapy, receiving supportive care only, or were enrolled in a clinical trial (Fig 1). In both strategies, probabilities and costs of severe (grade 3 to 4) adverse events associated with each type of treatment were considered. Model end points were overall survival and total direct medical costs. Costs included MGPS or SMGT test costs; drug treatment and administration cost; and cost for supportive care, hospitalizations, and management of serious adverse events.

  • Key Objective

  • To estimate the clinical and cost effectiveness of multigene panel sequencing (MGPS) versus single-marker genomic testing (SMGT) among patients with advanced non–small-cell lung cancer (aNSCLC) using data from a nationwide oncology patient database.

  • Knowledge Generated

  • MGPS is expected to be moderately cost effective compared with SMGT in patients with aNSCLC. Efforts to increase the proportion of patients who receive targeted therapies would improve the cost effectiveness of MGPS, assuming that incremental costs and outcomes of targeted treatments remain unchanged.

  • Relevance

  • Compared with SMGT, MGPS has the potential to identify more patients who could benefit from targeted therapies, but the effects on outcome and total cost of care remain unclear. This analysis provides evidence for health insurers who must consider both value and budget impact when weighing coverage policies for MGPS.

Model Inputs
Database, identification, and stratification of patients with aNSCLC.

The Flatiron Health database contains patient-level data from EHRs, including structured data (eg, laboratory values and prescribed drugs) and unstructured data collected via technology-enabled chart abstraction from physicians’ notes and other unstructured documents (eg, NGS reports). Study data were drawn from a geographically diverse nationwide sample of 191 oncology practices. The data set includes patient demographics, age, and clinical data, such as mutation status and treatment type. Institutional review board approval of the study protocol was obtained before the study was conducted and included a waiver of informed consent. Data provided to investigators were deidentified and provisions were put in place to prevent reidentification and protect patients’ confidentiality.

Patients of any age were eligible if their records indicated that they were diagnosed with advanced—stage IIIB or IV—NSCLC between January 1, 2011, and July 31, 2016, or were diagnosed with early-stage NSCLC and subsequently developed recurrent or progressive disease between those same dates. Each patient had an International Classification of Diseases, Ninth or Tenth Revision, diagnosis of lung cancer (162.x or C34.x), at least two documented clinical visits on or after January 1, 2011, pathology consistent with nonsquamous histology (adenocarcinoma, large-cell carcinoma, non–small-cell carcinomas not otherwise specified), and confirmation of advanced NSCLC on or after January 1, 2011. All patients received at least one line of antineoplastic treatment for advanced NSCLC.

Patients were excluded if their records showed evidence of concurrent active cancers, other than nonmelanoma skin cancer, within 6 months before advanced NSCLC diagnosis; and if their EHR did not document any tumor genetic test results, or if a genetic test report indicated an inconclusive result because of technical difficulties (eg, insufficient DNA for testing).

Patients with genetic information available were stratified into the MGPS cohort when their charts confirmed that they underwent NGS testing for 30 or more genes even if possibly done in addition to a single-gene test. The SMGT cohort included all patients who only received single-gene testing or panel testing for fewer than 30 genetic variants. ALK (anaplastic lymphoma kinase) rearrangement testing was required for inclusion in the SGMT group when EGFR (epidermal growth factor receptor) mutation testing was negative.

Identification of treatment types and lines of treatment.

For each cohort, we identified patients who received a targeted treatment, immunotherapy, or chemotherapy. Lines of therapy were based on oncologist-defined rule-based algorithms that were concordant with standard practice patterns.

To achieve mutually exclusive probability estimates, we grouped patients as follows: if any treatment was a targeted therapy—either on or off label—defined as oral kinase inhibitors that inhibit the target detected by genetic testing, the patient was counted in the targeted treatment group. Of the remaining patients, if any of the treatments received was an immunotherapy, the patient was counted in the immunotherapy group. If the patient received chemotherapy or antiangiogenic therapy—for example, bevacizumab or ramucirumab—but no immunotherapy or targeted treatment, the patient was counted toward the chemotherapy group.

Probabilities for upfront trial or hospice enrollment were based on published literature11 as the Flatiron cohort was designed to include only patients who received at least one line of systemic antineoplastic therapy—clinical trial treatments were blinded—which may have biased these particular estimates.

Overall survival.

Overall survival for patients who received either targeted treatment or nontargeted treatment—the latter including chemotherapy and immunotherapy—was estimated from the date of diagnosis of advanced disease to death of any cause or censoring. We censored all patients who were alive at the time of the last encounter available in the Flatiron database but who had no subsequent follow-up records. Estimates for overall survival after clinical trial enrollment and best supportive care were based on published data.12,13

Direct medical costs.

We used 2017 ASP drug costs for targeted and nontargeted therapies.14 Supportive care costs, including hospice, and costs of intravenous drug administration were based on the Centers for Medicare & Medicaid Services 2017 fee schedule.15 Costs for managing adverse events associated with targeted and nontargeted therapies were based on published literature.16-21 For patients who were enrolled in a clinical trial, we assumed that only costs for supportive care and adverse events were reimbursed by the payer and drug costs were not.


We used summary statistics to describe basic characteristics of the study population and treatments received. Kaplan-Meier methods were used to determine overall survival stratified by targeted versus nontargeted treatment received. To address potential confounding, analysis was adjusted for age at diagnosis of metastatic disease, newly detected advanced cancer, race, sex, smoking history, and Eastern Cooperative Oncology Group status. Adjusted mean survival estimates and corresponding 95% CIs were used to parameterize the survival inputs for the cost-effectiveness analysis.

The incremental cost-effectiveness ratio was calculated as:

(Cost MGPSCost SMGT)(Effects MGPSEffects SMGT).

Key factors that influenced cost effectiveness were identified using one-way sensitivity analyses. Decision uncertainty was quantified using probabilistic analysis—that is, jointly varying all model parameters over 10,000 Monte Carlo simulations. We also conducted a scenario analysis to characterize clinical contexts in which MGPS testing would be most cost effective.

Key Model Input Data

We identified 5,688 patients with aNSCLC who received MGPS (n = 875) or SMGT (n = 4,813) at any point during their care. Patient characteristics in the MGPS and SMGT cohorts were similar (Table 1). Approximately 22% of patients tested positive for driver alterations in EGFR or ALK in the MGPS cohort and 21% in the SMGT cohort. Among MGPS-tested patients, 8.0% were found to have other actionable mutations, with BRAFV600E, MET, and ERBB2 as the most common. In the MGPS and SMGT cohorts, 2.2% of patients tested positive for ROS1 mutations.


TABLE 1. Cohort Characteristics

Of MGPS-tested patients, 30.1% had evidence of an actionable mutation and 21.4% received a targeted treatment. Of SMGT-tested patients, 23.3% had evidence of an actionable mutation and 18.7% received a targeted treatment (Fig 2). Among patients younger than age 65 years (n = 1,858), 25.8% received a targeted treatment in the MGPS cohort versus 21.7% in the SMGT cohort. Among patients age 65 years or older (n = 3,415) tested with MGPS, 18.4% received a targeted treatment versus 17.5% in patients tested with SMGT.

Mean adjusted overall survival from the date of advanced NSCLC diagnosis was 2.31 years (95% CI, 0.31 to 4.12 years) for patients who received a targeted treatment versus 1.73 years (95% CI, 0.28 to 3.59 years) for patients who did not receive a targeted treatment.

Testing costs for the MGPS cohort were substantially higher than those for the SMGT cohort ($1,948 v $467). Costs of targeted treatments were roughly six times those of nontargeted treatments yet substantially lower than estimated costs of immunotherapy (Table 2).


TABLE 2. Main Model Inputs for Treatment and Testing Costs

Cost Effectiveness

After propagating the key model inputs reported above through the decision tree model, we found that three patients in the MGPS group were expected to gain 0.06 additional LYs and had $8,814 higher total direct health care costs compared with patients in the SMGT group. This results in an incremental cost-effectiveness ratio of $148,478 per LY gained for MGPS versus SMGT (Table 3).


TABLE 3. Cost-Effectiveness Results

The majority of model simulations generated by the probabilistic analysis of the decision tree model indicate that MGPS is more effective and more costly than SMGT, with a 52% probability of being cost effective at a willingness-to-pay threshold of $150,000 per LY gained. Given the current evidence, the probability that MGPS is considered to be cost effective versus SMGT approaches 60% when the willingness-to-pay threshold for a life year is more than $200,000 per LY gained and decreases to less than 40% when the willingness-to-pay threshold is less than $100,000 per LY gained (Appendix Fig A1).

Sensitivity and Scenario Analyses

One-way sensitivity analyses demonstrated that results were most sensitive to costs of targeted treatments and immunotherapies, survival estimates, probability of receiving a targeted treatment when a genetic driver mutation is detected, and receipt of immunotherapy (Appendix Fig A2).

In a scenario analysis in which all patients with an actionable mutation receive a targeted treatment, the expected incremental cost effectiveness of MGPS versus SMGT improves to $110,000 per LY gained (Appendix Fig A3).

Targeted therapies are now a therapeutic cornerstone for patients with aNSCLC, but the best diagnostic approach for identifying those who are eligible for treatment remains uncertain. Drawing from a large nationwide database containing detailed clinical data and mutation test results of nearly 6,000 patients with NSCLC, we estimated the cost effectiveness of managing therapeutic selection using MGPS versus SMGT. The MGPS cohort demonstrated an 8% larger proportion of patients with actionable mutations than the SMGT cohort, but the difference in those who received targeted therapies between testing strategies was only approximately 3%. As a result, the cost effectiveness of MGPS versus SMGT was moderate ($147,000 per LY gained) compared with commonly cited threshold values in the United States,22 ranging from $50,000 to $200,000 per LY gained. Of note, the incremental cost of MGPS over SMGT testing was not a major factor driving the cost effectiveness of MGPS-driven management. Thus, with regard to the current dilemma that community oncologists face as to whether to order either SMGT or MGPS testing or both—considering the difference in turnaround time, yield, and cost—results indicate that choosing MGPS over SMGT is expected to be moderately cost effective from a payer perspective, yet substantial decision uncertainty remains.

The richness of the genomic testing records allowed us to conduct a broad range of sensitivity and scenario analyses to better understand the factors that drive value for MGPS testing. Of importance, we found that in both SMGT and MGPS groups, a proportion of patients had a relevant driving mutation identified but did not receive oral targeted therapy (4.6% and 8.7%, respectively). Although the precise reasons for this are unclear, in MGPS this may be a result, in part, of the unavailability of effective treatments and lack of access to treatments for off-label use during the study’s timeframe for mutations other than EGFR, ALK, and ROS1 or incomplete data as documented in the EHR. Our scenario analysis shows the extent to which the cost effectiveness of MGPS would potentially improve if the use of targeted treatments in community oncology practices could be increased. In addition, as the number of available targeted therapies increases over time, and if the incremental benefit afforded by those therapies also improves compared with current treatments, MGPS will likely become more cost effective in the future.

Our study used the same database and mirrors the recent findings by Presley et al.23 Their study demonstrated that overall survival is not significantly higher for MGPS versus SMGT strategies, in part because a relatively small fraction of patients are candidates for targeted therapies and the survival benefit conferred by targeted treatments is diluted across a broad cohort of patients with NSCLC. Our analysis, however, expands on these findings by considering the economic implications of MGPS, showing that the additional total health care costs associated with MGPS testing are not disproportionate to its expected average effectiveness. Presley et al23 further note the reasons that the relatively modest observed survival benefit from MGPS is likely multifactorial: there is a relatively low incremental yield of actionable mutations compared with EGFR and ALK testing alone; a lack of use of targeted agents among those with identified mutations, possibly because of barriers to access; and relatively modest survival benefit among those who do receive targeted therapies in community settings. From an economic perspective, we add that the costs of MGPS testing are not a key cost driver. All else being equal, our results suggest that modification of practice patterns that encourage the optimal use of MGPS information and increasing access to (cost-)effective targeted therapies for patients with actionable mutations will improve the value of MGPS testing for patients with aNSCLC.

The higher cost of novel therapies in cancer, however, has been a topic of recent public debate.14,24-27 Our analyses demonstrate that the differential cost of targeted therapies and immunotherapies compared with traditional chemotherapeutic agents is an important factor that limits the cost effectiveness of MGPS. Our findings suggest that either a reduction in the cost differential of these therapies or an increase in their relative effectiveness will potentially do more to improve the cost effectiveness of MGPS than factors related to the tests themselves.

Our study has potential limitations. First, our estimates of overall survival are based on real-world observational data and are therefore susceptible to confounding biases, particularly from unobserved factors that may have influenced both the choice of testing strategy and the selection of treatments. We note that the comparatively rich EHR-based data elements available in the Flatiron database allowed us to adjust for some factors that are typically unavailable in registry data—for example, performance status—yet we recommend additional statistical analysis that controls for other factors—for example, EGFR/ALK status or receipt of immunotherapy—to obtain more precise inputs for this model parameter. In addition, as the data are derived largely from community oncology clinics, results are more generalizable to patients who are treated in community settings compared with academic health centers.

Another limitation of our data is that unstructured oral prescription documentation was not abstracted and EHR capture of oral treatments in structured data may be incomplete. The amount of missing oral prescriptions was estimated on the basis of an exploratory analysis of approximately 7,500 patients (data not shown). We estimate that the actual number of patients who could have received oral targeted therapies in the SMGT and MGPS groups might be a greater by a factor of 1.1 to 1.2. Missing oral drug prescription data could have biased our analysis to the extent that imbalances have occurred in the distribution of nonobserved oral targeted therapies between MGPS and SMGT groups. If a higher proportion of patients in the MGPS group received targeted therapies than we observed, the cost effectiveness of MGPS would be more favorable. For other variables, missing data was less than 10% and no imputation has been applied.

In summary, our cost-effectiveness analysis of MGPS versus SMGT provides evidence for health insurers who must consider both value and budget impact when weighing coverage policies for MGPS. Economic evaluations are enhanced when they are informed with high-quality head-to-head comparisons from clinical trials.28 In general, because of a lack of evidence from prospective, controlled comparisons, estimating the cost effectiveness of MGPS in cancer care requires modeling with retrospective data. A previous cost-effectiveness analysis of genomic sequencing procedures for treatment of NSCLC in the US health system predicted potential reduced health care costs associated with a net increase in average progression-free survival, reduced adverse events, and identification of appropriate care pathways,29 whereas a European study estimated higher upfront costs associated with MGPS testing that would be offset, in part, by long-term cost savings.30 To reduce decision uncertainty regarding insurance coverage of MGPS, our study highlights the need for prospective studies directly comparing the management of aNSCLC with MGPS versus SMGT that include both clinical and economic end points. As this analysis represents a snapshot in time, the model developed should be updated as new clinical or cost information becomes available.

© 2019 by American Society of Clinical Oncology

Presented in part at the 2018 American Society of Clinical Oncology Annual Meeting, Chicago, IL, June 1-5, 2018.

Funded by the Personalized Medicine Coalition.

Conception and design: Lotte Steuten, Bernardo Goulart, Daryl Pritchard, Scott D. Ramsey

Provision of study materials or patients: Neal J. Meropol, Scott D. Ramsey

Collection and assembly of data: Lotte Steuten, Neal J. Meropol

Data analysis and interpretation: All authors

Manuscript writing: All authors

Final approval of manuscript: All authors

Accountable for all aspects of the work: All authors

The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to or

Lotte Steuten

Consulting or Advisory Role: Agendia, Roche (I)

Research Funding: Thermo Fisher Scientific, EMD Serono (Inst), Nohla Therapeutics (Inst), Personalized Medicine Coalition (Inst)

Bernardo Goulart

Travel, Accommodations, Expenses: Flatiron Health

Neal J. Meropol

Employment: Flatiron Health

Stock and Other Ownership Interests: Flatiron Health, Roche

Patents, Royalties, Other Intellectual Property: US Patent 20020031515: Methods of therapy for cancers characterized by overexpression of the HER2 receptor protein

Daryl Pritchard

Honoraria: Xcenda, Genentech

Travel, Accommodations, Expenses: Genentech

Scott D. Ramsey

Consulting or Advisory Role: Kite Pharma, Bayer, Seattle Genetics, Genentech, Bristol-Myers Squibb, AstraZeneca, Merck, Cascadian Therapeutics, Epigenomics

Research Funding: Bayer (Inst), Bristol-Myers Squibb (Inst), Microsoft Corporation (Inst)

Travel, Accommodations, Expenses: Bayer Schering Pharma, Bristol-Myers Squibb, Flatiron Health

No other potential conflicts of interest were reported.


The authors thank the members of the Project Steering Committee (Amy Abernathy, MD, PhD; Edward Abrahams, PhD; Vineeta Agarwala, MD, PhD; Lori Anderson; Rachel Anhorn, PharmD; Peter Bach, MD, MAPP; Tara Burke, PhD; Lena Chaihorsky; Chris Cournoyer, MA; Katie Darius, PhD; Robert Dumanois; Marcia Eisenberg, PhD; Sheryl Elkin, PhD; Kristen Fessele, PhD; Yuri Fesko, MD; Pat James, MD; Michael Korn, MD; Brian Krueger, PhD; Razelle Kurzrock, MD; John Leite, PhD; Amy Miller, EVP; Mike Pellini, MD, MBA; Ammar Qadan; Hakan Sakul, PhD, MSc; Pooja Shaw, MBA; David Spetzler, PhD, MBA, MS; Kai te Kaat, PhD; Apostolia-Maria Tsimberidou, MD, PhD; Katherine Tynan, PhD; Ed Wang, PhD; Christopher Wells; and Jay Wohlgemuth, MD) and the Payer Advisory Committee (Suzanne Belinson, PhD, MPH; Jennifer Malin, MD, PhD; Girish Putcha, MD, PhD; Kenneth Schaecher, MD, FACP, CPC; Michael Sherman, MD, MBA; and Sean Tunis, MD, MSc) for providing important insight and expertise throughout this study. Views expressed in this manuscript are solely the authors’ views.

1. Sholl LM, Aisner DL, Varella-Garcia M, et al: Multi-institutional oncogenic driver mutation analysis in lung adenocarcinoma: The Lung Cancer Mutation Consortium experience. J Thorac Oncol 10:768-777, 2015 Crossref, MedlineGoogle Scholar
2. Ettinger DS, Wood DE, Akerley W, et al: NCCN guidelines insights: Non-small cell lung cancer, version 4.2016. J Natl Compr Canc Netw 14:255-264, 2016 Crossref, MedlineGoogle Scholar
3. Kalemkerian GP, Narula N, Kennedy EB, et al: Molecular testing guideline for the selection of patients with lung cancer for treatment with targeted tyrosine kinase inhibitors: American Society of Clinical Oncology endorsement of the College of American Pathologists/International Association for the Study of Lung Cancer/Association for Molecular Pathology clinical practice guideline update. J Clin Oncol 36:911-919, 2018 LinkGoogle Scholar
4. Morgensztern D, Campo MJ, Dahlberg SE, et al: Molecularly targeted therapies in non-small-cell lung cancer annual update 2014. J Thorac Oncol 10:S1-S63, 2015 (suppl 1) Crossref, MedlineGoogle Scholar
5. VanderLaan PA, Rangachari D, Majid A, et al: Tumor biomarker testing in non-small-cell lung cancer: A decade of change. Lung Cancer 116:90-95, 2018 Crossref, MedlineGoogle Scholar
6. Hiley CT, Le Quesne J, Santis G, et al: Challenges in molecular testing in non-small-cell lung cancer patients with advanced disease. Lancet 388:1002-1011, 2016 Crossref, MedlineGoogle Scholar
7. UnitedHealth Group: Personalized medicine: Trends and prospects for the new science of genetic testing and molecular diagnostics. Google Scholar
8. Deverka PA, Dreyfus JC: Clinical integration of next generation sequencing: Coverage and reimbursement challenges. J Law Med Ethics 42:22-41, 2014 (suppl 1) Crossref, MedlineGoogle Scholar
9. Agarwala V, Khozin S, Singal G, et al: Real-world evidence in support of precision medicine: Clinico-genomic cancer data as a case study. Health Aff (Millwood) 37:765-772, 2018 Crossref, MedlineGoogle Scholar
10. 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. JAMA 316:1093-1103, 2016 Crossref, MedlineGoogle Scholar
11. National Cancer Institute: NCI Cancer Bulletin: Clinical trials enrollment. Google Scholar
12. Duggan KT, Hildebrand Duffus S, D’Agostino RB Jr, et al: The impact of hospice services in the care of patients with advanced stage nonsmall cell lung cancer. J Palliat Med 20:29-34, 2017 Crossref, MedlineGoogle Scholar
13. Kris MG, Johnson BE, Berry LD, et al: Using multiplexed assays of oncogenic drivers in lung cancers to select targeted drugs. JAMA 311:1998-2006, 2014 Crossref, MedlineGoogle Scholar
14. Bach PB: Limits on Medicare’s ability to control rising spending on cancer drugs. N Engl J Med 360:626-633, 2009 Crossref, MedlineGoogle Scholar
15. Centers for Medicare & Medicaid Services: 2017 fee schedule. Google Scholar
16. Stokes ME, Muehlenbein CE, Marciniak MD, et al: Neutropenia-related costs in patients treated with first-line chemotherapy for advanced non-small cell lung cancer. J Manag Care Pharm 15:669-682, 2009 Crossref, MedlineGoogle Scholar
17. Shepherd FA, Rodrigues Pereira J, Ciuleanu T, et al: Erlotinib in previously treated non-small-cell lung cancer. N Engl J Med 353:123-132, 2005 Crossref, MedlineGoogle Scholar
18. Hanna N, Shepherd FA, Fossella FV, et al: Randomized phase III trial of pemetrexed versus docetaxel in patients with non-small-cell lung cancer previously treated with chemotherapy. J Clin Oncol 22:1589-1597, 2004 LinkGoogle Scholar
19. Solomon BJ, Mok T, Kim DW, et al: First-line crizotinib versus chemotherapy in ALK-positive lung cancer. N Engl J Med 371:2167-2177, 2014 Crossref, MedlineGoogle Scholar
20. Kuderer NM, Dale DC, Crawford J, et al: Mortality, morbidity, and cost associated with febrile neutropenia in adult cancer patients. Cancer 106:2258-2266, 2006 Crossref, MedlineGoogle Scholar
21. Burke TA, Wisniewski T, Ernst FR: Resource utilization and costs associated with chemotherapy-induced nausea and vomiting (CINV) following highly or moderately emetogenic chemotherapy administered in the US outpatient hospital setting. Support Care Cancer 19:131-140, 2011 Crossref, MedlineGoogle Scholar
22. Neumann PJ, Sanders GD, Russell LB, et al (eds): Cost-Effectiveness in Health and Medicine (ed 2). New York, NY, Oxford University Press, 2016, pp 201 CrossrefGoogle Scholar
23. Presley CJ, Tang D, Soulos PR, et al: Association of broad-based genomic sequencing with survival among patients with advanced non-small cell lung cancer in the community oncology setting. JAMA 320:469-477, 2018 Crossref, MedlineGoogle Scholar
24. Bennette CS, Richards C, Sullivan SD, et al: Steady increase in prices for oral anticancer drugs after market launch suggests a lack of competitive pressure. Health Aff (Millwood) 35:805-812, 2016 Crossref, MedlineGoogle Scholar
25. Shih YC, Smieliauskas F, Geynisman DM, et al: Trends in the cost and use of targeted cancer therapies for the privately insured nonelderly: 2001 to 2011. J Clin Oncol 33:2190-2196, 2015 LinkGoogle Scholar
26. Shih YT, Xu Y, Liu L, et al: Rising prices of targeted oral anticancer medications and associated financial burden on Medicare beneficiaries. J Clin Oncol 35:2482-2489, 2017 LinkGoogle Scholar
27. American Society of Clinical Oncology: American Society of Clinical Oncology position statement on addressing the affordability of cancer drugs. J Oncol Pract 14:187-192, 2018 LinkGoogle Scholar
28. Ramsey SD, Willke RJ, Glick H, et al: Cost-effectiveness analysis alongside clinical trials II: An ISPOR Good Research Practices Task Force report. Value Health 18:161-172, 2015 Crossref, MedlineGoogle Scholar
29. Sabatini LM, Mathews C, Ptak D, et al: Genomic sequencing procedure microcosting analysis and health economic cost-impact analysis: A report of the Association for Molecular Pathology. J Mol Diagn 18:319-328, 2016 Crossref, MedlineGoogle Scholar
30. van Amerongen RA, Retèl VP, Coupé VM, et al: Next-generation sequencing in NSCLC and melanoma patients: A cost and budget impact analysis. Ecancermedicalscience 10:684, 2016 Crossref, MedlineGoogle Scholar
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DOI: 10.1200/CCI.19.00002 JCO Clinical Cancer Informatics - published online June 26, 2019

PMID: 31242043

ASCO Career Center