This article presents a cost-effectiveness analysis of nivolumab vs docetaxel from the US payer perspective in non–small cell lung cancer (NSCLC) based on randomized phase 3 studies with a minimum 5 years of follow-up.
Objectives: To determine the lifetime cost-effectiveness of nivolumab vs docetaxel in advanced squamous and nonsquamous non–small cell lung cancer (NSCLC) following platinum-based chemotherapy from a US payer perspective.
Study Design: Trial- and cohort-based cost-effectiveness analyses.
Methods: The analyses used partitioned survival models with 3 mutually exclusive health states: progression free, progressed disease, and death. The mean starting age was 61 years. Clinical parameters were derived from the 2 registrational, randomized, phase 3 trials with a minimum follow-up of 5 years. Costs were derived from published literature. The primary outcomes were quality-adjusted life-years (QALYs), life-years gained (LYG), and incremental cost-effectiveness ratios (ICERs). Costs and outcomes were discounted at 3% per annum. Uncertainty was examined using univariate and probabilistic sensitivity analyses.
Results: In patients with squamous NSCLC, the use of nivolumab improved life-years (LYs) and QALYs by 1.23 and 0.99, respectively, compared with docetaxel. Costs were increased by $99,677, resulting in ICERs of $100,776 per QALY and $81,294 per LYG. In patients with nonsquamous NSCLC, nivolumab increased LYs and QALYs by 0.99 and 0.80, respectively. Costs were increased by $94,174, resulting in ICERs of $117,739 per QALY and $94,849 per LYG. ICERs were most sensitive to the discount rates applied to costs and outcomes. At a willingness-to-pay threshold of $150,000, nivolumab had probabilities of 91% and 99% of being cost-effective in patients with squamous and nonsquamous NSCLC, respectively.
Conclusions: Nivolumab is likely to be cost-effective for the treatment of patients with advanced NSCLC following platinum-based chemotherapy in the United States.
Am J Manag Care. 2021;27(8):e254-e260. https://doi.org/10.37765/ajmc.2021.88726
Lung and bronchus cancer is the second most common cancer diagnosis in the United States, accounting for 12% of all new cancer cases, and is the leading cause of cancer-related death, representing 18% of all cancer deaths.1 The introduction of immune checkpoint blockade to restore antitumor immunity has substantially transformed the treatment landscape of non–small cell lung cancer (NSCLC) and other cancer types.2 Nivolumab, a fully human immunoglobulin G4 programmed death 1 (PD-1) immune checkpoint inhibitor antibody that disrupts PD-1–mediated signaling and restores antitumor immunity, was approved by the US FDA in March 2015 for patients with locally advanced or metastatic squamous NSCLC with progression after platinum-based chemotherapy and an epidermal growth factor receptor or anaplastic lymphoma kinase treatment, if appropriate. The label was expanded to include patients with nonsquamous NSCLC in October 2015.3
The efficacy of nivolumab was demonstrated in 2 pivotal, randomized, phase 3 trials in patients with squamous and nonsquamous NSCLC: CheckMate 017 (NCT 01642004) and CheckMate 057 (NCT 01673867), respectively.4,5 These were the first randomized phase 3 trials to report outcomes for a PD-1 inhibitor in patients with previously treated advanced NSCLC. The reported analyses are based on data from a minimum of 5 years of follow-up.6 These longer-term data demonstrated that the treatment effect of nivolumab observed in shorter-term data was maintained. In CheckMate 017, median overall survival (OS) was 9.23 (95% CI, 7.33-12.62) months for nivolumab vs 6.01 (95% CI, 5.13-7.33) months for docetaxel (HR, 0.62; 95% CI, 0.48-0.79).4 Survival rates at 5 years were 12.3% with nivolumab and 3.6% with docetaxel. In CheckMate 057, median OS was 12.21 (95% CI, 9.66-15.08) months for nivolumab vs 9.49 (95% CI, 8.11-10.74) months for docetaxel (HR, 0.70; 95% CI, 0.58-0.83).5 Survival rates at 5 years were 14.0% with nivolumab vs 2.1% with docetaxel. Nivolumab was associated with a significantly better adverse event (AE) profile in both trials. In CheckMate 017, grade 3/4 treatment-related AEs were reported in 8.4% of nivolumab-treated patients vs 55.8% of docetaxel-treated patients. In CheckMate 057, grade 3/4 treatment-related AEs were reported in 11.8% of nivolumab-treated patients vs 54.1% of docetaxel-treated patients.
At the time of seeking reimbursement for new oncology therapies, clinical data typically included limited follow-up for key end points such as OS and progression-free survival (PFS). For treatments that potentially prolong life, this necessitates extrapolation of survival data to capture the potential lifetime benefit of new treatments. Inherent uncertainty in approaches to extrapolation often translates into uncertainty over long-term survival—an important driver for the results of cost-effectiveness analyses. Following regulatory approval of nivolumab, cost-effectiveness analyses were developed using CheckMate 017 and CheckMate 057 with 12 and 18 months of minimum follow-up, respectively. Having 5 years or more of follow-up data available from these trials presents a unique opportunity to revisit the cost-effectiveness analysis of nivolumab in these patient populations.
Cohort-based, partitioned survival models were developed to evaluate the incremental cost-effectiveness of nivolumab vs docetaxel in patients with advanced (metastatic; stage IIIb/IV) squamous and nonsquamous NSCLC that had progressed during or after platinum-doublet chemotherapy. The models included 3 mutually exclusive health states: progression free (PF), progressed disease (PD), and death. In each cycle, patients were partitioned to each state based on cumulative PF and OS probabilities; those, in turn, were based on individual patient data from the registrational trials, which had at least 5 years of follow-up at the time of this analysis. Nivolumab dosage was 480 mg every 4 weeks. This flat-dose regimen was clinically equivalent to the weight-based dose of 3 mg/kg used in the clinical trials4,5 and was approved by the FDA in 2018.7 Docetaxel dosage was 75 mg/m2 every 3 weeks.8
The analysis perspective was that of the US third-party payer. Models were run over a patient’s lifetime in weekly cycles; this was the largest common denominator between nivolumab (4 weeks) and docetaxel (3 weeks) administration cycles, applying a half-cycle correction. The base-case patient was the average patient with advanced NSCLC in the 2 clinical trials, with a mean age of 61.6 years, mean weights of 72.7 kg (squamous) and 75.2 kg (nonsquamous), and a mean height of 173 cm.4,5
Primary outcomes were life-years (LYs) and quality-adjusted life-years (QALYs), incremental costs, and incremental costs per QALY gained and per LY gained (LYG) expressed in US$. Costs and outcomes were discounted at 3% per annum.9 No institutional review board approval was required.
Survival analyses. Parametric survival curves were fit to the OS and PFS 5-year follow-up data from CheckMate 017 and CheckMate 057 and extrapolated over a lifetime horizon. Several parametric functions and cubic spline models were tested (eAppendix Figure [eAppendix available at ajmc.com]). Final selection of parametric distributions for the base-case analysis were based on 3 criteria: (1) a visual comparison of the survival distributions with Kaplan-Meier curves of the clinical trials data, (2) statistical goodness-of-fit assessed using Akaike information criterion and Bayesian information criterion, and (3) external validation of model-predicted landmark survival against observed landmark survival from the 6-year follow-up data from CheckMate 003 (NCT 00730639)10; conditional survival estimates from the Surveillance, Epidemiology, and End Results Program cancer registry database (SEER*Stat [National Cancer Institute]); and clinical expert opinion.
Consistent with guidelines from the Decision Support Unit at the National Institute for Health and Care Excellence, the proportional effects assumption was tested across all survival analyses by examining log-cumulative hazards, log-cumulative odds, and Schoenfeld residual plots.11 If the assumption was satisfied, parametric survival models were fit with a covariate to capture the treatment effect of nivolumab vs docetaxel. Conversely, if the assumption was violated, parametric distributions were independently fit to nivolumab and docetaxel survival times.
In the squamous NSCLC survival analysis, the proportional effects assumption was satisfied for OS. A dependent 2-knot spline on the hazards scale was the best-fitting model for nivolumab and provided the best fit to 6-year OS from CheckMate 003 (Bristol Myers Squibb; CheckMate 003 data on file). This distribution was fit to 5-year data from CheckMate 017, with a parameter to capture treatment effect of nivolumab vs docetaxel. The proportional effects assumption was violated for PFS. One-knot spline models on the hazards scale were fit separately to nivolumab and docetaxel data. Final OS and PFS distributions used in the base-case analyses for nivolumab and docetaxel are shown in eAppendix Figure [A].
In the nonsquamous NSCLC survival analysis, the proportional effects assumption was violated for OS and PFS. Log normal distributions were fit separately to nivolumab and docetaxel OS times. Two-knot spline on the odds scale was the best-fitting distribution for PFS for nivolumab and docetaxel. Final OS and PFS distributions used in the base-case analysis for nivolumab and docetaxel are shown in eAppendix Figure [B].
Treatment patterns. The proportion of patients on treatment in each cycle was modeled based on Kaplan-Meier analyses of treatment duration from the clinical trial data. Although treatment with nivolumab beyond 2 years was allowed in the trials, in the squamous NSCLC patient population, only 6.1% and 4.6% of patients remained on treatment after 3 and 5 years, respectively, as did 7.7% and 4.2% of patients in the nonsquamous patient population. The base-case analysis therefore assumed a maximum treatment duration of 2 years for nivolumab. This is consistent with previous health technology assessment (HTA) submissions.12,13 In addition, analyses of CheckMate 003, a phase 1/2 trial in previously treated patients with NSCLC that applied a 96-week treatment-stopping rule, demonstrated a long-term survival profile similar to that of nivolumab patients in CheckMate 017 and 057.14 Furthermore, a 2-year treatment-stopping rule was applied in KEYNOTE-010 (NCT 019095657), a randomized phase 2/3 trial comparing pembrolizumab and docetaxel in patients with previously treated PD-L1–positive NSCLC.15 A scenario analysis was conducted in which patients received nivolumab up until disease progression.
The analysis accounted for patients receiving subsequent treatment following disease progression on nivolumab and docetaxel. For each comparator, the proportions of patients who received subsequent treatment were obtained from the clinical trials (eAppendix Table 1).4,5 The mean duration of subsequent treatment was estimated as 3.5 months based on data from the LENS clinical trial (Bristol Myers Squibb; Treatment patterns, outcomes, and resource use study for advanced stage non-small cell lung cancer [squamous and nonsquamous] in Europe [LENS] data on file).
Costs. Health resource use and direct medical expenditures included those associated with drug acquisition, administration, and monitoring; subsequent treatments; disease management; and treatment-related grade 3 or 4 AEs (eAppendix Table 2).4,5,16-20
Acquisition costs for nivolumab, docetaxel, and subsequent treatments were based on the wholesale acquisition cost, assuming no vial sharing.20 Drug administration costs were estimated based on 2019 CMS physician fee schedule reimbursement rates for a 1-hour intravenous infusion in an outpatient setting.16 Monitoring costs were based on health resources required for recommended clinical monitoring strategy and unit costs from the 2019 CMS laboratory fee schedule (eAppendix Table 3).16,21 The aggregate subsequent treatment cost was computed based on the mean duration of treatment and weighted by the proportion of patients receiving each subsequent treatment (eAppendix Table 3).16,21
Disease management costs were estimated based on monthly health resources used for supportive care derived from published literature (eAppendix Table 4).16-18,21
Health resource use estimates were multiplied by unit costs derived from the 2019 CMS physician fee schedule and adjusted to account for the weekly cycles used in the model.16 A one-off end-of-life/terminal-care cost of $11,467.44 based on published literature was applied to each patient who died.22
AE costs were based on a previously published trial-based analysis of nivolumab vs docetaxel (Bristol Myers Squibb; BMS adverse event cost analysis data on file).23,24 Unit costs for each AE, obtained from the 2014 Healthcare Cost and Utilization Project, were multiplied by the proportion of patients experiencing AEs from the 5-year update of the clinical trials to obtain the per-patient cost of managing AEs (eAppendix Table 2).4,5,16-20 All costs were inflated to 2019 US$25 and were applied as one-off costs in the first cycle.
Health-state utilities. Utility values for the PF and PD health states were derived from quality-of-life data collected using the EQ-5D questionnaire in the CheckMate 017 and 057 trials, applying a US-specific scoring algorithm.26 Among patients with squamous NSCLC, EQ-5D utility scores were 0.765 and 0.716 for the PF and PD health states, respectively. Among patients with nonsquamous NSCLC, utility scores were 0.772 and 0.716 for the PF and PD health states, respectively. Utility decrements (disutilities) associated with AEs were identified in previous HTA submissions.27-30 Where disutilities were not available for specific AEs, disutility was assumed to be zero. The associated overall QALY decrements associated with all AEs were applied to the first cycle of the models.
Sensitivity analyses were performed to examine the impact of parameter uncertainty on outcomes. In univariate sensitivity analyses, individual parameters were varied over plausible ranges based on either 95% CIs for HRs and health state utilities or ±20% for body weight, body surface area, and costs. Discount rate was varied from 0% to 6%. Probability distributions were assigned to model parameters, and 1000 Monte Carlo simulations were performed to explore the impact of joint uncertainty on outcomes. OS and PFS parameters were drawn from their corresponding parametric survival distributions accounting for correlation between shape and scale parameters. Costs and AE disutilities were drawn from gamma distributions and utility weights from a beta distribution.
In patients with squamous NSCLC, using nivolumab vs docetaxel resulted in improved LYs (2.22 vs 0.99; difference, 1.23) and QALYs (1.67 vs 0.68; difference, 0.99). Costs were higher with nivolumab vs docetaxel ($143,287 vs $43,610; difference, $99,677). Incremental cost-effectiveness ratios (ICERs) comparing nivolumab vs docetaxel were $81,294 per LYG and $100,776 per QALY gained (Table).
In nonsquamous NSCLC, using nivolumab vs docetaxel resulted in improved LYs (2.24 vs 1.25; difference, 0.99) and QALYs (1.68 vs 0.88; difference, 0.80). Costs were higher with nivolumab vs docetaxel ($147,275 vs $53,101; difference, $94,174). ICERs comparing nivolumab with docetaxel were $94,849 per LYG and $117,739 per QALY gained (Table).
In a scenario analysis where patients received nivolumab until disease progression, among patients with squamous NSCLC, costs were higher with nivolumab vs docetaxel ($180,557 vs $43,610; difference, $136,946), resulting in ICERs comparing nivolumab vs docetaxel of $111,690 per LYG and $138,457 per QALY gained. In nonsquamous NSCLC, costs were higher with nivolumab vs docetaxel ($257,828 vs $53,101; difference, $204,727). ICERs comparing nivolumab vs docetaxel were $206,195 per LYG and $255,955 per QALY gained.
Results of the univariate sensitivity analyses are summarized as tornado diagrams for patients with squamous and nonsquamous NSCLC in Figure 1 [A and B]. In both models, the ICER was most sensitive to discount rates on outcomes.
Probabilistic sensitivity analysis results are presented as cost-effectiveness acceptability curves over a range of willingness-to-pay (WTP) thresholds. In the model simulations of patients with squamous NSCLC, nivolumab had a 99% probability of being a cost-effective treatment at a WTP threshold of $150,000 per QALY gained (Figure 2 [A]). In the model simulations of patients with nonsquamous NSCLC, nivolumab had a 91% probability of being cost-effective at a WTP threshold of $150,000 per QALY gained (Figure 2 [B]).
This trial-based cost-effectiveness analysis demonstrated that incremental cost per QALY gained for nivolumab vs docetaxel was $100,766 in squamous NSCLC and $117,739 in nonsquamous NSCLC. Incremental cost per LYG for nivolumab vs docetaxel was $81,294 in squamous NSCLC and $94,849 in nonsquamous NSCLC. ICERs were sensitive to discount rates, and to cost and utilities in the PF state. In a scenario in which patients received nivolumab until progression, the incremental cost per QALY gained was $138,457 in those with squamous NSCLC and $255,955 in those with nonsquamous NSCLC. The value of nivolumab results from the additional LYG among these patients. Costs are accrued due to longer treatment duration and patients living longer with nivolumab. However, some of these costs may be offset by saving on AE and subsequent treatment costs.
In the absence of an explicit threshold, Ubel et al inferred the level of cost-effectiveness that was generally acceptable in the US context by reviewing the literature on stated and revealed preference in the United States.31 Based on this evidence, they concluded that the cost-effectiveness threshold that was acceptable at that time (2003) approached $200,000 or more per QALY; adjusting with the medical Consumer Price Index to 2020 dollars, this threshold exceeds $300,000.32 More recent theoretical and empirical evidence has suggested that acceptable thresholds in metastatic cancer in the end-of-life context may be higher.33,34 Compared with this body of evidence, the projected ICERs presented here are well within what is considered cost-effective in the United States. Further support of acceptable value for money in the United States was documented in a real-world setting that showed that within 2 years of nivolumab approval in the United States in second-line NSCLC, 66% of patients were treated with anti–PD-1/PD-L1 therapy.35
A prior cost-effectiveness model developed by the US Institute for Clinical and Economic Review (US ICER) that used a similar 3-state partitioned survival structure estimated a much higher incremental cost per QALY gained for nivolumab vs docetaxel of $415,950.36 HRs for nivolumab vs docetaxel used in the US ICER analysis did not distinguish between squamous and nonsquamous NSCLC. OS and PFS curves and HRs for nivolumab vs docetaxel differ widely in these populations. Further, PFS and OS extrapolations published by the US ICER at 24 months36 did not provide an adequate fit to the longer-term data reported in the nivolumab vs docetaxel trials (Bristol Myers Squibb; CheckMate 003 data on file)14,37 and were not validated against trials with longer follow-up such as CheckMate 003. Survival extrapolations were very conservative, with projected nivolumab survival rates of approximately 8% at 4 years and less than 5% at 5 years. In the CheckMate trials, the proportions of patients alive at 4 years were higher—13% for nivolumab in the squamous NSCLC population and 15% in the nonsquamous NSCLC population. The corresponding 5-year survival rates were 12% and 14% in the most current data set. The US ICER analysis did not consider the durable survival benefit of immunotherapies, which are shown in long-term follow-up data. This also highlights the limitations of extrapolations based on survival data sets with limited follow-up. Furthermore, utility values were derived from estimates available at that time30 and were, therefore, based on chemotherapy standard-of-care experience from approximately 20 years ago. This may have contributed to underestimation of QALYs gained as these utilities were substantially lower than the values derived from patient-generated EQ-5D data in CheckMate 017 and 057.4,5
Prior survival extrapolations from earlier data cuts from CheckMate 017 and 057 that informed HTA submissions in Canada, Sweden, and the United Kingdom12,13,38,39 varied in predictive accuracy by histology. For example, whereas extrapolations from our original 12-month model in patients with squamous NSCLC provided a more accurate prediction of a 5-year survival rate of 12.05% vs an observed 12.4%, the 18-month model in patients with nonsquamous NSCLC yielded a more conservative 5-year OS rate of 11.94% vs an observed 14.0%. The estimated mean OS for patients with squamous NSCLC who received nivolumab increased by 13% between the 12-month and the 5-year model-based extrapolations, from 27.0 to 30.5 months. Mean OS for patients with nonsquamous NSCLC who received nivolumab increased by 12% between the 18-month analysis and the 5-year analysis from 26.9 to 30.2 months (Bristol Myers Squibb; cost-effectiveness model of nivolumab versus docetaxel in advanced squamous and non-squamous NSCLC data on file).
Aguiar et al40 compared the cost-effectiveness of immune checkpoint inhibitors for second-line NSCLC from a US payer perspective. ICERs were $155,605 and $187,685 for nivolumab vs docetaxel in patients with squamous and nonsquamous NSCLC, respectively. However, this model used a 5-year time horizon for evaluation; calculations from our 5-year model with a lifetime horizon (20 years) show this would underestimate LYG for nivolumab by 50%.
Strengths and Limitations
The analyses described in this article overcome many of the limitations of prior analyses. These analyses draw on individual patient data from the pivotal nivolumab registrational trials, CheckMate 017 and 057, with a minimum follow-up of 5 years. External validation of survival predictions were performed based on individual patient data from CheckMate 003, the longest available follow-up in any immuno-oncology trial in the patient population with pretreated NSCLC.6 However, a limitation of this analysis is that even with the availability of long-term follow-up data, survival extrapolations remain necessary to project OS and PFS over a patient’s lifetime horizon.14 Therefore, inherent uncertainty remains in the long-term survival predictions from these models. Nonetheless, this analysis likely underestimates the long-term survival benefits of nivolumab because the parametric distributions did not account for the potential for a durable long-term survival, as evident in the trends toward flattening of tails of the nonparametric Kaplan-Meier curves in CheckMate 003, as well as CheckMate 017 and 057 (Bristol Myers Squibb; CheckMate 003 data on file).6,14 QALY decrements associated with AEs in the model were of duration equivalent to a single cycle, which may underestimate utility impact of AEs with long duration. Finally, this study modeled outcomes based on data from clinical trials as opposed to real-world data. However, multiple studies in patients receiving nivolumab in the second-line setting indicate that clinical outcomes in real-world patients are similar to those in clinical trials.41-44 Future work should elucidate whether these model-based and trial-based results could be replicated with long-term follow-up in a real-world setting.
Clinical data show that nivolumab is associated with longer OS and higher response rates in patients with squamous and nonsquamous NSCLC vs docetaxel.4,5 This trial-based analysis suggests that nivolumab may be considered cost-effective from a US payer perspective across a range of WTP thresholds that are considered acceptable for drugs that prolong life in an end-of-life setting.
Medical writing and editorial assistance were provided by Parexel, funded by Bristol Myers Squibb.
Author Affiliations: Bristol Myers Squibb, Princeton, NJ (MAC, SJL, JRP), and Singapore (NH); Parexel International (CS), London, UK.
Source of Funding: Bristol Myers Squibb.
Author Disclosures: Dr Chaudhary, Dr Lubinga, Ms Hertel, and Dr Penrod are employees of and own stock in Bristol Myers Squibb, the sponsor of this work. Ms Smare is employed by Parexel, which received a consultancy fee for development of this analysis.
Authorship Information: Concept and design (MAC, CS, NH, JRP); acquisition of data (MAC, NH); analysis and interpretation of data (MAC, SJL, CS, NH, JRP); drafting of the manuscript (MAC, SJL, CS, NH, JRP); critical revision of the manuscript for important intellectual content (MAC, SJL, CS, NH, JRP); statistical analysis (JRP); provision of patients or study materials (NH); obtaining funding (NH); and supervision (MAC, SJL).
Address Correspondence to: Solomon J. Lubinga, PhD, Bristol Myers Squibb, 3401 Princeton Pike, Lawrence Township, NJ 08648. Email: Solomon.Lubinga@bms.com.
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