The cost-utility of the prognostic test was compared with the current standard of care (SoC) in patients with early-stage non-small cell lung cancer (NSCLC). Findings suggest that using a prognostic test to guide adjuvant chemotherapy decisions in early-stage NSCLC is potentially cost-effective compared with using the SoC based on globally accepted willingness-to-pay thresholds.
Keywords: Cost-utility analysis, Cost-effectiveness analysis, Non-small cell lung cancer, Prognostic test, Economic analysis
Abstract
Background.
A prognostic test was developed to guide adjuvant chemotherapy (ACT) decisions in early-stage non-small cell lung cancer (NSCLC) adenocarcinomas. The objective of this study was to compare the cost-utility of the prognostic test to the current standard of care (SoC) in patients with early-stage NSCLC.
Materials and Methods.
Lifetime costs (2014 U.S. dollars) and effectiveness (quality-adjusted life-years [QALYs]) of ACT treatment decisions were examined using a Markov microsimulation model from a U.S. third-party payer perspective. Cancer stage distribution and probability of receiving ACT with the SoC were based on data from an academic cancer center. The probability of receiving ACT with the prognostic test was estimated from a physician survey. Risk classification was based on the 5-year predicted NSCLC-related mortality. Treatment benefit with ACT was based on the prognostic score. Discounting at a 3% annual rate was applied to costs and QALYs. Deterministic one-way and probabilistic sensitivity analyses examined parameter uncertainty.
Results.
Lifetime costs and effectiveness were $137,403 and 5.45 QALYs with the prognostic test and $127,359 and 5.17 QALYs with the SoC. The resulting incremental cost-effectiveness ratio for the prognostic test versus the SoC was $35,867/QALY gained. One-way sensitivity analyses indicated the model was most sensitive to the utility of patients without recurrence after ACT and the ACT treatment benefit. Probabilistic sensitivity analysis indicated the prognostic test was cost-effective in 65.5% of simulations at a willingness to pay of $50,000/QALY.
Conclusion.
The study suggests using a prognostic test to guide ACT decisions in early-stage NSCLC is potentially cost-effective compared with using the SoC based on globally accepted willingness-to-pay thresholds.
Implications for Practice:
Providing prognostic information to decision makers may help some patients with high-risk early stage non-small cell lung cancer receive appropriate adjuvant chemotherapy while avoiding the associated toxicities and costs in patients with low-risk disease. This study used an economic model to assess the effectiveness and costs associated with using a prognostic test to guide adjuvant chemotherapy decisions compared with the current standard of care in patients with non-small cell lung cancer. When compared with current standard care, the prognostic test was potentially cost effective at commonly accepted thresholds in the U.S. This study can be used to help inform decision makers who are considering using prognostic tests.
Introduction
Lung cancer is the third most common type of cancer in the U.S. among men and women, with non-small cell lung cancer (NSCLC) accounting for 85%–90% of all lung cancers [1]. The minority of patients are diagnosed with early-stage disease, approximately 15% [2]; however, these patients still have a significant risk of recurrence and lung cancer death [3]. Surgical resection is recommended as the initial therapy for early-stage (stages I and II) disease [4]. While cisplatin-based adjuvant chemotherapy (ACT) is the standard of care for patients with stage II to IIIA NSCLC [5–7], controversy surrounds its use in stage I disease [8]. Although ACT is recommended for patients with tumors that have high-risk features [4, 9], identifying high-risk patients still remains challenging [9, 10]. Debate also exists surrounding the benefits of ACT in early-stage NSCLC and what type of ACT should be given to these patients to balance the benefits along with toxicity and cost of chemotherapy [4, 9–13]. Personalized medicine seeks to identify and treat patients who are most likely to achieve optimal outcomes. In NSCLC, this means that patients with a high risk of recurrence should receive ACT, and patients with a low risk should not undergo ACT to avoid the unnecessary toxicity and cost of therapy.
Recently, the expression levels of cell cycle progression (CCP) genes, reflecting the inherent aggressiveness of the tumor, have shown to be prognostic in patients with early-stage adenocarcinoma of the lung [14]. The CCP score has been shown to predict patients at high risk of cancer recurrence and cancer-related death [15–20]. A proprietary prognostic test, myPlan Lung Cancer (myPlan), which is based on CCP score and pathologic tumor stage, has been developed by Myriad Genetics, Inc. (Salt Lake City, UT; http://www.myriad.com/products-services/lung-cancer/myplan-lung-cancer/) and validated as a prognostic marker of cancer-related death in patients with NSCLC [21, 22]. Specifically, in an independent validation cohort of 650 patients with stage I or II lung adenocarcinoma, the prognostic score was a more significant indicator of lung cancer mortality risk than pathologic stage [hazard ratio (HR), 2.01; p < .001] and efficiently stratified patients into low- and high-risk groups with a significant difference (p < .001) in 5-year lung cancer-specific survival [22].
Given the controversy of using ACT in early-stage NSCLC, identifying patients who are at the highest risk of death from NSCLC is vital. However, the added benefit of identifying and treating high-risk patients is likely to be associated with increased monetary costs and adverse treatment effects. Considering the rapid proliferation of these technologies, evidence-based decision-analytical models are needed to support effective decision making related to testing technologies as they are used in clinical practice [23]. The purpose of this study was to examine the cost-utility of using myPlan to guide ACT treatment decisions compared with the current standard of care (SoC) in patients with early-stage adenocarcinoma of the lung who have undergone surgical resection.
Materials and Methods
Decision Analytic Model
The cost effectiveness of using myPlan prognostic test was compared with the SoC in patients with early-stage NSCLC using a health state transition model. The analysis was performed using a microsimulation of 10,000 patients with early-stage NSCLC who had previously undergone surgical tumor resection. Microsimulation, which runs patients through the model one at a time, was used to calculate individual disease-specific mortality risk. The analysis was performed using a lifetime horizon from the perspective of third-party payers in the U.S. All analyses were performed using TreeAge Pro 2013 (TreeAge Software, Williamstown, MA).
Quality-adjusted life-years (QALYs), costs (2014 U.S. dollars), and the incremental cost-effectiveness ratio (ICER) were the primary model outcomes. Life-years were also calculated, without adjustment for quality of life, which payers in the U.S. may also value. Both costs and effectiveness outcomes were discounted at a 3% annual rate and a half-cycle correction was applied. Additionally, cost estimates were inflated where necessary to 2014 U.S. dollars using the personal consumption expenditures price index [24].
Patients with early-stage NSCLC who underwent resection were classified as having stage IA, IB, IIA, or IIB disease and then as high or low risk according to myPlan (Fig. 1). After being classified as either high or low risk, patients entered the Markov node, which consisted of the following four health states: ACT, no cancer, any cancer recurrence, and death (Fig. 2). In the Markov node, patients could start by either receiving ACT or entering the no-cancer state. In the model, patients transitioned between health states once per cycle, which was defined as 1 month to approximate ACT treatment cycles, during their lifetime. Transitions between health states were determined by probabilities within each health state.
Figure 1.
Schematic diagram of the decision analytic model. The point at which a decision is made is the decision node, which is represented by the blue square node. At each chance node, represented by the green circular nodes, each branch has a probability associated to the event. The Markov node is the point at which the health state transitions occur and is represented by the purple circular node with an “M” in it. This schematic is simplified for illustration purposes; for example, the chance node off the SoC branch mimics the chance node off of the myPlan node.
Abbreviations: myPlan, myPlan Lung Cancer; NSCLC, non-small cell lung cancer; SoC, standard of care.
Figure 2.
Health state transition diagram. The diagram indicates the four potential health states considered in the model. Possible transitions are indicated by the direction of the arrows. Health states are mutually exclusive, and patients can only transition between health states once per cycle.
Abbreviation: ACT, adjuvant chemotherapy.
In the ACT state, patients could experience a noncancer death, die because of chemotherapy, or survive the cycle. If they survived, patients could either continue receiving ACT or discontinue treatment early because of adverse events, refusing to continue, or other reasons. If patients discontinued treatment early, they were also at risk of disease progression, which transitioned them to the cancer recurrence state. If patients continued ACT, they could experience adverse events (AEs) or not. Once in the no-cancer state, patients could die of a noncancer cause, die because of NSCLC, experience a recurrence of NSCLC, or continue without cancer. Because of the high mortality rate after NSCLC recurrence, it was assumed that once a patient had a recurrence, the patient could not be cured and remained in that state until death. Patients with a cancer recurrence could experience a noncancer death, die of NSCLC, or remain in the cancer recurrence state.
Model Inputs
Literature-Derived Inputs
Most of the probabilities and costs as well as all of the utility parameters for the model were derived from the published literature (Table 1). MEDLINE and the Tufts Medical Center Cost-Effectiveness Analysis Registry were searched using Medical Subject Headings and keyword search terms. Studies were included if they were randomized controlled trials, meta-analyses, health technology assessments, quality-of-life analyses, performed in North America or Europe, performed in adult patients with early-stage lung cancer, and available in English. References of identified articles were also searched.
Table 1.
Model parameters
The Lung Adjuvant Cisplatin Evaluation (LACE) study and the studies it comprised were used to collect information on the following: effect of ACT on disease-specific mortality; noncancer-related mortality; death due to ACT; effect of ACT on risk of recurrence; serious adverse events (SAEs) with ACT; discontinuing ACT early [5, 6]; and discontinuing ACT early because of toxicity, patient refusal, or disease progression [8]. The risk of recurrence without ACT was derived from a study by Pepek et al., which reported the 5-year risk of cancer recurrence by stage (i.e., IA, IB, IIA, and IIB) [25]. Other studies were excluded because they did not report the results by stage at the level of granularity needed [26–29]. The risk of death after experiencing a cancer recurrence was pooled from two studies [30, 31].
The cost of ACT, being in the no-cancer state, having a cancer recurrence, and the cost of the final month of treatment before death were derived from the literature [32–34]. Fox et al. reported the 3-month cost of ACT, which was assumed to be equally divided between each month and used as the 1-month cost [34]. Cipriano et al. reported the final month of costs for patients experiencing an NSCLC-related death by age [33]. Finally, Buck et al. reported the median Medicare reimbursement for imaging [32]. It was assumed that patients without a recurrence would have monitoring with imaging approximately every quarter, but this cost was divided equally across each month in the quarter.
Utility values were derived from the literature for patients with and without ACT, SAEs with ACT, and for no cancer as well cancer recurrence [35–38].
Probability of Receiving ACT With myPlan
Integrated Insights Consulting conducted a phone- and Internet-based survey of physicians located throughout the U.S. in March 2013. The purpose of the survey was to understand physician preferences and interest in myPlan. Physicians were paid to participate in the survey and were screened to ensure they had a sufficient patient volume in NSCLC, were full-time practitioners, and were familiar with genetic tests. A total of 101 physicians practicing in academic (approximately 40% of respondents) and community settings completed the survey, including 61 medical oncologists, 25 cardiothoracic surgeons, and 15 general surgeons. The proportion of patients by cancer stage whom physicians would typically treat with ACT, and how myPlan information would affect treatment decisions, was used in the model (Table 1).
Disease Stage and Probability of Receiving ACT With the SoC
Patients diagnosed with early-stage NSCLC and treated at the Huntsman Cancer Institute (HCI) were identified between January 1, 2004, and December 31, 2012. The HCI Tumor Registry was used to collect information on the site and histologic findings of the NSCLC diagnosis as well as establish cancer stage at diagnosis. A total of 220 patients with early-stage NSCLC were identified, and approximately 74% had stage I disease (Table 1). This distribution of disease stages was similar to those reported by the National Cancer Data Base [39]. Chart review was performed to confirm NSCLC diagnosis and to extract the proportion of patients who received ACT.
NSCLC-Related Mortality and myPlan Cost
Myriad Genetics, Inc. provided patient-level data, which were pooled to determine the distribution of CCP scores by stage from published studies on myPlan [21, 22]. Each patient in the model was assigned a CCP score based on these distributions. The CCP score and cancer stage were used to calculate individual prognostic scores, which were in turn used to calculate an individual’s NSCLC-related, 5-year mortality risk [22]. If a patient’s 5-year mortality risk was ≤22%, the patient was classified as low risk by myPlan [22]. If a patient’s 5-year mortality risk was >22%, the patient was classified as high risk by myPlan [22]. The 22% threshold corresponds to the predefined 85th percentile of the prognostic score used to classify patients with stage IA cancer as low risk in a myPlan validation study [22]. In other words, the 22% threshold places the 15% of patients with stage IA disease who died of lung cancer within 5 years into the high-risk category. In microsimulation, each patient is run through all arms of the decision model and the difference in costs and effectiveness values are determined. Because of this, each patient in the model had the same mortality risk and received the same benefit from ACT in the myPlan or SoC arms. The estimated treatment benefit of ACT on NSCLC-related mortality by CCP score was determined from the subset of patients who received ACT [21]. Higher CCP scores indicate cancer cells may be dividing more rapidly, and chemotherapy traditionally is more effective in rapidly dividing cells, so it may be that ACT would be more effective in patients with higher CCP scores. This was seen in the small subset of patients who received ACT [21]. However, because of a limited number of patients receiving ACT in that study, this assumption was explored in sensitivity analyses by using data from other studies [8, 40]. Myriad Genetics, Inc. also provided the estimated cost of myPlan.
Sensitivity Analyses
One-Way Sensitivity Analyses
One-way sensitivity analyses were used to examine the influence individual parameters had on the model. In the model, each parameter was varied over a range of values based on 95% confidence intervals (95% CI), maximum and minimums, or a plausible range as determined by the authors.
Probabilistic Sensitivity Analysis
Probabilistic sensitivity analysis (PSA) was used to examine joint uncertainty among model parameters. In the model, all parameters were assigned distributions based on the underlying data and which met the criteria of the parameter. Whereas cost data are typically skewed and fit a γ distribution at the individual-patient level, PSA is used to assess the uncertainty in a parameter estimate. It is unlikely that the estimate of mean costs for a population is skewed to the extent of individual costs. Therefore, in the PSA, a normal distribution was used for costs, but γ distributions were used in a stochastic uncertainty analysis, which examined random variability between patients.
Other Sensitivity Analyses
Because of member turnover, health care payers are often interested in the cost-effectiveness of interventions over time horizons other than lifetime. Therefore, the model was also run over 5- and 10-year time horizons. Additionally, as ACT treatment benefit was derived from a small subgroup of patients with an overall treatment benefit somewhat different than other estimates (approximately 14.6% absolute treatment benefit on disease-free survival [DFS]), other sources of ACT treatment benefit were explored and used in the model. This included the LACE study [8], which assumed that ACT benefit was not associated with CCP score and showed an absolute treatment benefit on DFS of 5.8%. It also included another prognostic test, which used different gene expressions than myPlan to assess risk and had an approximate absolute treatment benefit of 45% on overall survival in high-risk patients [40].
Results
Base-Case Analysis
A total of 63.4% of patients with early-stage NSCLC were classified as high risk using myPlan, and myPlan resulted in a higher overall percentage of patients receiving ACT when compared with the SoC (myPlan, 42.6% vs. the SoC, 27.3%). Use of myPlan resulted in a mean cost of $137,403 and 5.45 QALYs compared with $127,359 and 5.17 QALYs with the use of SoC (Table 2). The resulting overall ICER for myPlan versus the SoC was $35,867/QALY gained. The ICER varied by stage, with the lowest ICER occurring in patients with stage IB disease ($27,715/QALY gained) and the highest in patients with stage IIA disease ($61,473/QALY gained). When not considering quality of life, myPlan resulted in 7.40 life-years compared with 7.02 life-years with the SoC and an ICER of $26,518/life-year gained (LYG). Additionally, to further examine the effect of ACT in the model, a post hoc analysis compared the survival of patients who received ACT with those who did not while controlling for risk classification using a Cox proportional hazards model. Use of ACT was associated with an 8% reduction in the risk of death (HR, 0.92; 95% CI, 0.88–0.96).
Table 2.
Base-case costs, effectiveness, and incremental cost-effectiveness
Sensitivity Analyses
One-Way Sensitivity Analyses
Results from the one-way sensitivity analyses showed the model was most sensitive to the utility value associated with the “no cancer” health state after receiving ACT, the treatment benefit seen with ACT, the 5-year mortality risk cut-off used to classify patients as high or low risk, the probability of receiving ACT in patients with stage IA disease, and the hazard ratio regarding the risk of recurrence associated with ACT (Fig. 3). When other parameters were varied, the ICER changed by less than $10,000/QALY gained.
Figure 3.
Tornado diagram of 15 most influential variables on the model. The x-axis represents the ICER of myPlan versus SoC. The dotted vertical line represents the base-case ICER of $35,867/quality-adjusted life-year gained. The horizontal bars represent the relative impact varying one parameter over a range of plausible values has on the ICER. Larger bars indicate the model results are more sensitive to changes in that parameter.
Abbreviations: ACT, adjuvant chemotherapy; HR, hazard ratio; ICER, incremental cost-effectiveness ratio; LACE, Lung Adjuvant Cisplatin Evaluation study; myPlan, myPlan Lung Cancer; NSCLC, non-small cell lung cancer; prob, probability; SoC, standard of care.
Probabilistic Sensitivity Analysis
The PSA showed that use of myPlan resulted in a mean cost of $140,698 and 5.50 QALYs compared with $129,765 and 5.22 QALYs for the SoC. The corresponding ICER for myPlan versus the SoC was $38,869/QALY gained. When quality of life was not considered, the use of myPlan and the SoC resulted in 7.50 and 7.12 life-years, respectively, corresponding to an ICER of $29,092/LYG. Finally, myPlan was cost-effective in 65.5% of simulations at a willingness to pay (WTP) of $50,000/QALY, and in 92.4% of simulations at a WTP of $100,000/QALY (Fig. 4).
Figure 4.
Probabilistic sensitivity analysis cost-effectiveness acceptability curve (CEAC). The CEAC displays the probability of each option being cost-effective at various willingness-to-pay thresholds by determining the proportion of probabilistic sensitivity analysis simulations that result in an incremental cost-effectiveness ratio below the given threshold.
Abbreviations: myPlan, myPlan Lung Cancer; SoC, QALY, quality-adjusted life-year; standard of care.
Other Sensitivity Analyses
When a 5-year time horizon was examined, the cost of myPlan was $72,520 with 2.73 QALYs compared with the SoC that cost $65,914 with 2.68 QALYs, giving an ICER of $132,996/QALY gained. At 10-years, the resulting ICER was reduced to $50,301/QALY gained. When the ACT treatment benefit was taken from the LACE study, which assumed ACT benefit was not associated with CCP score, the ICER was $69,809/QALY gained. When the ACT treatment benefit was derived from a different prognostic test, the ICER ranged from $34,984 to $38,079 per QALY gained.
Discussion
Despite recent advances in the treatment of other cancers, patients with NSCLC continue to have a poor prognosis. The benefit of ACT varies by stage in NSCLC; a higher survival benefit is seen in patients with stage II disease, but no survival benefit is seen in patients with stage I disease [8]. However, the mortality rate in stage I disease is still relatively high, which may indicate there is a subset of patients that would benefit from ACT. The myPlan Lung Cancer prognostic test provides a measure of tumor aggressiveness and has been validated as a prognostic marker of cancer-related death in patients with NSCLC [21]. Identifying patients at the highest risk of cancer recurrence and death will help health care providers weigh the benefits of ACT with the significant costs and adverse events associated with treatment.
This study used a decision analytic model to compare the cost-effectiveness of myPlan to the SoC in guiding ACT treatment decisions. Microsimulation was used in this analysis, and each patient was assigned a prognostic score based on cancer stage and CCP scores. This allowed the identification of patients at a higher risk of dying from NSCLC who might benefit from ACT, as well as those at a lower risk in whom it might be prudent to avoid the costs and adverse events of ACT. The study found that myPlan is potentially cost-effective at widely accepted WTP thresholds ($35,867/QALY gained and $26,518/LYG). The model was sensitive to the utility value associated with not having a recurrence of cancer after having received ACT and the treatment benefit of ACT. PSA indicated that myPlan was cost-effective in 65.5% of simulations at a WTP of 50,000/QALY and 92.4% at a WTP of $100,000/QALY.
The results of the current study are similar to a recently published study that compared the cost-effectiveness of a different risk score assay (14 genes) with the SoC in patients with NSCLC [41]. In that study, the 14-gene risk score assay had ICERs of $23,200/QALY gained and $11,952/LYG, and it was cost-effective in 68% of simulations at a WTP of $50,000/QALY. The decision analytic model used in the 14-gene risk score assay study had several minor differences from the model in the current study; for example, differences in risk stratification (low, intermediate, and high risk), health state considered, and model inputs. Despite these differences, the results were similar and seem to indicate that using a prognostic test to guide ACT decisions in patients with NSCLC is a potentially cost-effective option.
The current study has several limitations worthy of discussion. First, there is limited evidence indicating that response to ACT is associated with CCP score, and it was assumed in the model that patients with higher CCP scores have greater response rates to ACT [21]. This is a logical assumption given that CCP is an indicator of tumor aggressiveness and current antineoplastic chemotherapy is most effective in rapidly dividing cells. However, because this has only been seen in a small number of patients, this assumption was tested in sensitivity analysis where it was assumed that ACT response was not associated with CCP score. This sensitivity analysis resulted in an ICER of $69,809/QALY gained. The relationship between CCP score and ACT treatment response warrants further study. Similarly, CCP scores have been validated in NSCLC-related death but not in cancer recurrence. Thus, in the model, it was assumed that risk of recurrence was only dependent upon disease stage and ACT use. However, for the aforementioned reasons, it is likely that CCP score also predicts the risk of cancer recurrence and that ACT may have an increased benefit in patients with higher CCP scores. Finally, the use of ACT in patients who received the myPlan test was an area of uncertainty in the current model. Although physicians were surveyed to ask how the risk stratification results would affect treatment decisions, future research should examine how using myPlan changes ACT treatment decisions.
Conclusion
Myriad myPlan appears to be a potentially cost-effective option in guiding ACT treatment decisions in patients with NSCLC. Further studies should be conducted to examine how the use of myPlan in clinical practice affects ACT treatment decisions.
Acknowledgment
This research was funded by a grant from Myriad Genetics Laboratories, Inc. (Salt Lake City, UT), of which Kraig M. Yager, Joshua Jones, and Rajesh Kaldate are employees.
Author Contributions
Conception/Design: David D. Stenehjem, Brandon K. Bellows, Kraig M. Yager, Joshua Jones, Rajesh Kaldate, Uwe Siebert, Diana I. Brixner
Provision of study material or patients: David D. Stenehjem, Brandon K. Bellows
Collection and/or assembly of data: David D. Stenehjem, Brandon K. Bellows, Kraig M. Yager, Joshua Jones, Rajesh Kaldate
Data analysis and interpretation: David D. Stenehjem, Brandon K. Bellows, Uwe Siebert, Diana I. Brixner
Manuscript writing: David D. Stenehjem, Brandon K. Bellows
Final approval of manuscript: David D. Stenehjem, Brandon K. Bellows, Kraig M. Yager, Joshua Jones, Rajesh Kaldate, Uwe Siebert, Diana I. Brixner
Disclosures
David D. Stenehjem: Myriad Genetics Laboratories, Inc. (RF); Brandon K. Bellows: Myriad Genetics, Inc. (RF); Kraig M. Yager: Myriad Genetics Laboratories, Inc. (E); Joshua Jones: Myriad Genetics Laboratories, Inc. (E); Rajesh Kaldate: Myriad Genetics Laboratories, Inc. (E, OI); Uwe Siebert: University of Utah (RF); Diana I. Brixner: Myriad Genetics, Inc. (RF).
(C/A) Consulting/advisory relationship; (RF) Research funding; (E) Employment; (ET) Expert testimony; (H) Honoraria received; (OI) Ownership interests; (IP) Intellectual property rights/inventor/patent holder; (SAB) Scientific advisory board
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