A partitioned-survival model [6] was developed to project expected costs and outcomes for pembrolizumab compared with SoC. The main outcome measure was the incremental cost-effectiveness ratio (ICER), expressed as cost per quality-adjusted life-year (QALY) gained; the incremental cost per life-year (LY) gained was also determined. The model was developed in Microsoft® Excel 2010 (Redmond, WA, USA).

Efficacy, safety and utility data were derived from KEYNOTE-024, with a cut-off date of 9 May 2016. This phase III randomized controlled trial compared pembrolizumab with the investigator’s choice of five platinum-based regimens in first-line metastatic NSCLC.

Target Population

The target population in the model was based on the KEYNOTE-024 trial population: patients aged ≥18 years (mean age 64 years); diagnosis of stage IV NSCLC without EGFR-activating mutations or ALK translocations; no prior systemic chemotherapy treatment for metastatic NSCLC; PD-L1 TPS ≥50%.

Interventions

Patients in KEYNOTE-024 were randomized to pembrolizumab 200 mg once every 3 weeks for up to 35 cycles or platinum-based chemotherapy for 4–6 cycles. Non-squamous patients in the SoC arm without progressive disease after completion of chemotherapy were eligible to receive pemetrexed maintenance. The SoC regimens included pemetrexed + carboplatin, pemetrexed + cisplatin, gemcitabine + cisplatin, gemcitabine + carboplatin, and paclitaxel + carboplatin (Electronic Supplementary Material [ESM] 1) [7].

Treatment was continued for the specified number of cycles or until disease progression, intolerable toxicity, patient withdrawal, or investigator decision. PFS was assessed according to RECIST (Response Evaluation Criteria in Solid Tumors), version 1.1 [8] by means of blinded, independent, central radiologic review (BICR). Patients receiving SoC with verified progressive disease could switch to receive pembrolizumab [7]. Patients were eligible to switch if they had documented progression, did not stop chemotherapy for any other reason than progressive disease, had an Eastern Cooperative Oncology Group (ECOG) score of 0 or 1 at time of progression and had at least 30 days of survival after SOC treatment. Clinically stable patients considered to be deriving clinical benefit by the investigator were eligible to continue pembrolizumab beyond progression. Patients treated with pembrolizumab were assumed to be treated for a maximum of 2 years as per trial protocol and FDA-approved label [9].

Model Structure

The model evaluated the cost effectiveness of pembrolizumab compared with SoC as first-line treatment in the target population (ESM 2). A partitioned-survival model was constructed with three mutually exclusive health states, progression-free (starting state), progressive disease, and death (Fig. 1). Outcomes and costs of AEs, toxicity grade 3–5, were incorporated and weighted by their observed incidence. The time in each health state was used to estimate cumulative total costs and health outcomes over the time horizon for a cohort of patients receiving each intervention.

Fig. 1 Model states and transitions. AEs adverse events Full size image

Time Horizon, Discounting, and Perspective

A time horizon long enough to accommodate patients’ lifetime (20 years for base case) was selected to ensure important differences in costs and outcomes between the interventions were considered. Scenario analyses explored the effect of shorter time horizons (5 and 10 years), in view of the short life expectancy of patients with metastatic NSCLC. Costs and outcomes were discounted at 3% per year, with rates of 0 and 5% explored in scenario analysis [10].

The analysis was conducted from the perspective of a US third-party public healthcare payer.

Clinical Parameters

The model effectiveness parameters were estimated from KEYNOTE-024 patient-level data for time-on-treatment (ToT), PFS and OS.

Parametric functions were fitted to the Kaplan–Meier (KM) ToT, PFS and OS data, in line with published guidelines, to extrapolate from the trial duration (median follow-up 11.2 months; range 6.3–19.7 months) to the 20-year time horizon. The survival curve fitting was carried out in line with UK National Institute for Health and Care Excellence (NICE) Decision Support Unit (DSU) guidelines [11]. The parametric models fit were the Weibull, the exponential, the log-normal, the log-logistic and the Gompertz distributions. Statistical tests based on the Akaike information criterion (AIC) and the Bayesian information criterion (BIC), combined with visual inspection (comparing fitted distributions to study KM plots), were used to select the best-fitting parametric distributions for the base case. Finally, the clinical plausibility of the extrapolated results was considered in selecting the final distribution functions for the model. Alternative parametric functions were tested in scenario analyses.

Time-on-Treatment

ToT data from the KEYNOTE-024 trial were analysed to determine treatment duration and cost, since patients deriving clinical benefit could continue pembrolizumab beyond progression. Parametric functions were fit to the KM ToT data. The AIC and BIC combined with visual inspection were used to select the Weibull distribution for pembrolizumab and the generalized gamma distribution for SoC as the base case. The base-case ToT curves are presented in Fig. 2. Alternative parametric functions and the base-case curves with 20-year time horizon are presented in ESM 2.

Fig. 2 Modelled ToT from KEYNOTE-024 for pembrolizumab and standard-of-care arms. ToT time on treatment, SoC standard-of-care Full size image

Progression-Free Survival

For PFS, the KM probabilities from KEYNOTE-024 were used directly up to Week 9, with parametric functions fitted thereafter. This was because the first radiologic tumour response assessment was conducted at Week 9, resulting in a protocol-driven drop in PFS at this point. The Weibull distribution was the best fit for pembrolizumab, and the exponential parametric function was the best fit for SoC. The base-case PFS curves are presented in Fig. 3. Alternative parametric functions and the base-case curves with 20-year time horizon are presented in ESM 3.

Fig. 3 Modelled progression-free survival from KEYNOTE-024 for pembrolizumab and standard-of-care arms. BICR blinded independent central review, ITT intention-to-treat, PFS progression-free survival, SoC standard of care Full size image

Overall Survival

In KEYNOTE-024, 43.71% of patients from the SoC group crossed over to receive pembrolizumab after disease progression, which was anticipated to diminish the survival benefit of pembrolizumab. A simplified two-stage approach as described by Latimer and Abrams [12] and Latimer [13] was implemented to perform an OS analysis adjusting for switching in the SoC arm. Through this approach, OS treatment effect estimate was adjusted to correct for the bias induced by treatment switch.

Two datasets are available for use in the OS analysis: (1) the intention-to-treat (ITT) dataset included patients who switched from SoC to pembrolizumab following disease progression, and (2) a ‘switching-adjusted’ dataset used the two-stage adjustment method to adjust the survival benefit of patients who switched to pembrolizumab,

The ITT dataset was used in the base-case analysis, as it better represented the real-world situation given the existing FDA approval of pembrolizumab as second-line therapy for metastatic NSCLC expressing PD-L1 [9]. The OS impact of subsequent therapies was assumed to be reflected within the OS KM data from KEYNOTE-024. The switching-adjusted dataset was explored in a scenario analysis.

For the OS outcomes, the standard parametric curves tested did not provide a good visual fit to the observed KM data, so a two-phase piecewise model was applied. Cut points were determined by estimating structural changes to the KM curve using Chow tests [14, 15]. The area under the KM curve was used directly for the first phase until the cut-off, and an exponential distribution was used to estimate OS for up to 5 years of follow-up. The cut-off point identified by the Chow test was Week 32 for pembrolizumab, Week 38 for SoC (without adjustment), and Week 25 for SoC with adjustment. A constant hazard rate, derived from long-term external data for patients with stage IV NSCLC from the Surveillance, Epidemiology and End Results (SEER) database, was applied to the OS model after Year 5 [16].

The base-case OS curves are presented in Fig. 4. Alternative parametric functions and the base-case curves with 20-year time horizon are presented in ESM 4.

Fig. 4 Modelled overall survival with the hazard rate from SEER data applied after year 5. a intention-to-treat analysis without switching adjustment; b switching adjusted. KM Kaplan–Meier, OS overall survival, SEER Surveillance, Epidemiology, and End Results Program, SoC standard of care Full size image

Subsequent Therapies

The percentage of patients receiving subsequent therapy (45.51%) was estimated from the Flatiron database [17]. Flatiron is a nationally representative electronic health record data that includes 162 practices from different regions in the USA. We included patients (aged ≥18 years) newly diagnosed with metastatic NSCLC initiating first-line anticancer therapy (November 2012–January 2015, with follow-up through July 2015). In the pembrolizumab arm, and SoC arm with switching adjustment, all such patients were assumed to receive chemotherapy. In the SoC arm without switching adjustment, 43.71% of patients were assumed to receive pembrolizumab and 1.80% were assumed to receive chemotherapy based on the switching rate observed from the KEYNOTE-024 data.

Adverse Events

The model included all-cause AEs of grade 3 or higher reported in ≥5% of patients in KEYNOTE-024 for either arm. Pneumonitis, an immune-mediated AE with a high management cost, was included based on feedback from clinicians received in an advisory panel review of the model. Model input data for each AE are shown in Table 1.

Table 1 Key input data Full size table

Utility Data

The utility data used in the base case (Table 1) were based on EuroQol-5 Dimensions, 3 Levels (EQ-5D-3L) data collected in KEYNOTE-024. The EQ-5D questionnaire was administered at treatment cycle 1, 2, 3, 6, 9, 12 and every third cycle as long as patients were on study treatment, as well as at the treatment discontinuation visit and at a 30-day post-treatment safety follow-up visit. The generic health statuses assessed from the EQ-5D questionnaire were converted to population-based utility values using published algorithms. For the base-case analysis, US-based scores [18] were applied to US patients, UK-based scores [19] for UK patients and EU-based scores [20] for all other patients. Two approaches of defining health state utilities were considered: time-to-death and progression-based health states. The time-to-death approach, recently described by Hatswell et al. [21], reflects the decline in cancer patients’ quality of life as they approach death. Four time-to-death categories (over 360, 180–360, 30–180 days, and under 30 days) were examined for use in the model base case. Pooled data were used as there were no significant differences in utility values in each time-to-death category between the treatment arms [22].

Resource Utilization and Cost Inputs

The cost inputs considered in the cost-effectiveness analysis were regimen-related costs, including drug acquisition, administration and pre-medication costs, disease management costs, subsequent therapy costs, terminal care costs and AE management costs (summarized in Table 1). It was assumed that 80% of healthcare costs would be paid by public healthcare payers [23]. All costs were updated to $US, year 2016 values using the Medical Care Consumer Price Index [24].

Regimen-Related Costs

Pembrolizumab is available in single-use vials of 100 mg and is administered at a dose of 200 mg every 3 weeks. The list price for pembrolizumab is $US4381 per 100-mg vial, therefore the cost per dose is $US8762 [25]. Dosage of SoC chemotherapy regimens is based on patients’ body surface area. The average number of vials per dose of each regimen was calculated using the body surface area distribution [mean 1.84 m2, standard deviation (SD) 0.26 m2] of patients in the Flatiron database [17]. The cost of SoC was $US3472 per dose, based on the distribution of each regimen in KEYNOTE-024 (ESM 1). The cost per dose of pemetrexed maintenance was estimated at $US4907. Drug administration costs for intravenous infusions based on the year 2016 Center for Medicare and Medicaid Services (CMS) payment rates [23] are shown in Table 1. The number of treatment cycles in the model was based on the estimated ToT. Treatment duration was modelled per KEYNOTE-024 and FDA-approved label up to 4–6 cycles for platinum and up to 2 years for pembrolizumab [9].

Costs of anti-emetic prophylaxis were identified from National Comprehensive Cancer Network (NCCN) guidelines and estimated at $US286.55 and $US5.95 per cycle for cisplatin or carboplatin, respectively [26].

Disease Management Costs

For the progression-free state, disease management costs were based on healthcare resource utilization (HCRU) data observed in KEYNOTE-024. The HCRU data captured non-study-related healthcare visits (including visits for home-based hospice, home healthcare, non-study laboratory, radiation therapy, nurse, primary care, and medical specialists), hospitalization and emergency room visits. Pembrolizumab was associated with lower HCRUs than SoC. The progression-free state HCRU in the KEYNOTE-024 was described in detail in Huang et al. [27]. The unit costs were based on 2016 CMS payment rates [28]. To inform disease management costs for the progressive disease state, a literature review was conducted, which identified published studies whose estimates were based on US commercial claims [29, 30] and SEER Medicare [31,32,33]. However, the SEER-Medicare studies over-estimated disease management costs as defined for the model, as intravenous anti-cancer therapy costs were not excluded. Thus, the model base-case value (Table 1) is based on reported results that used commercial claims [29] from initiation of second-line intravenous anti-cancer therapy after excluding intravenous anti-cancer therapy costs. Sensitivity analyses of this parameter embrace the range of reported values from the literature [29, 30]. Disease management costs in the post-progression health state were estimated based on cost data reported by Ramsey et al. [29]. Disease management costs were estimated by evaluating the 1704 patients with NSCLC, and included the following cost categories: (1) inpatient, (2) emergency department, (3) ambulatory (including chemotherapy administration costs in office or outpatient setting), (4) other medical (captured laboratory costs, including the actual processing of the specimen), (5) retail pharmacy. We excluded the cost of ‘chemotherapy administration’ as a disease management cost, as it was included separately in the model within subsequent therapy costs.

Subsequent Therapy Costs

To estimate the post-discontinuation therapy costs for SoC patients, the weighted average of subsequent treatment weighted by the rate of switching to pembrolizumab was implemented. Average treatment duration with pembrolizumab was assumed to be 183 days based on the treatment-switching patients in KEYNOTE-024. Patients receiving pembrolizumab were assigned second-line chemotherapy post-progression. The average cost of second-line chemotherapy was estimated to be $US24,358 per patient based on the cost of chemotherapy administrations in a US healthcare claim database [29].

Terminal Care Costs

A literature review was conducted to inform the cost of terminal care in the final 30 days of life. Five studies were identified that reported direct medical costs in the end-of life period [30, 34,35,36,37]. All studies were limited to patients with lung cancer, with the exception of Chastek et al. [34], who included all cancer patients with analyses stratified by primary cancer site. The studies used varied sources, including SEER-Medicare [30], integrated health systems databases [35, 36] and commercial administrative claims [34, 37]. Costs in these studies were reported over a variety of intervals, including the period of time from last chemotherapy dose to death [30], the time from initial disease progression to death [35] and the last 6 months of life [34, 36, 37]. Chastek et al. [34] was the only study to report costs within the terminal period by 30-day increments, showing that direct medical costs increased during the last 30 days of life because of increases in hospitalization and hospice costs; these were used for the model base case. Importantly, the costs in the last 30-day period ($US31,114) were higher than the average monthly cost over the 6-month period ($US14,748). Chastek et al. [34] did not report resource utilization, but their reported cost estimates reflected the sum of health plan and patient paid amounts, by cancer-related cost category. These categories included (1) inpatient, (2) hospice (inpatient and outpatient), (3) chemotherapy, (4) growth factors [erythropoiesis-stimulating agents (ESAs) and granulocyte colony-stimulating factors (GCSFs)], (5) radiation, (6) emergency room visits, (7) office visits, (8) hospital outpatient procedures and (9) other outpatient cancer-related services.

Adverse Event Management Costs

Incidence and costs of selected grade 3 or higher AEs are summarized in Table 1. Hospitalization costs related to AE management were considered in the model. The rates of hospitalization were provided by clinical experts. The hospitalization costs for managing each AE were obtained from the 2016 CMS payment rates [38]. Based on incidences of AEs and management costs, the total average cost per patient for managing AEs was estimated to be $US2334 for pembrolizumab and $US521 for SoC.

Sensitivity Analyses

One-way deterministic sensitivity analyses (DSAs) were conducted to evaluate the sensitivity of the model to plausible changes to each key model parameter. A probabilistic sensitivity analysis (PSA) was conducted to test the robustness of the model with respect to uncertainty in model input parameters. In this analysis, each key parameter was assigned a theoretical probability distribution. Correlated parameters were assigned a joint distribution. For example, the two parameters in the Weibull function fitted to ToT were assigned a multivariate-normal distribution. The PSA was performed using a second-order Monte Carlo simulation with 1000 iterations. DSA ranges and parameter distributions used in the PSA are detailed in ESM 6.

Multiple scenario-based sensitivity analyses were also performed. One such analysis explored the impact of differing practice patterns implied by selection of alternative OS data. In particular, analyses representing settings where PD-1 inhibitors are unavailable as second-line or later treatment were explored using a two-stage adjustment of the treatment OS that removed the effect of switching treatment from chemotherapy to pembrolizumab. Additional scenario-based sensitivity analyses adjusted the model time horizon, the discounting of costs and outcomes, and the impact of PD-L1 testing costs, as well as the application of alternative plausible parametric functions to the extrapolation of OS, PFS and ToT.

Model Validation

The modelling process followed good practice. Health economists, clinicians and pharmacy professionals who were experts in oncology economics evaluated this model and other analogous versions. In addition to these singular evaluations, an expert panel consisting of three medical oncologists, one oncology pharmacist and one biostatistician provided detailed review and feedback. Evaluation covered not only the conceptual model but also the model data, assumptions, methods, analyses and results. The panel feedback was used to provide insight into what AEs were important for inclusion in the model, It also provided verification to the modelling approach, analysis plans, clinical plausibility of clinical inputs (e.g. hospitalization rates for AEs), duration of treatment effects and modelled outcomes such as projections of OS and PFS. Where consensus was not achieved, the panel provided guidance on the scope of sensitivity analyses. The modelling process, conceptual model and the computerized model were evaluated against and satisfied the published AdViSHE criteria for assessment of the validation status of health-economic decision models [39].

The OS extrapolation was validated with external observation data. The clinical plausibility of the projected long-term outcomes was also verified by independent clinical experts. The projected OS curve of SoC was compared with the survival data collected from the Flatiron Health database [17] in patients aged ≥18 years newly diagnosed with metastatic NSCLC initiating first-line anticancer therapy (November 2012–January 2015, with follow-up through July 2015). As shown in ESM 5, the OS curve from the Flatiron data matched closely with the projected OS curve of SoC with switching adjustment. The model-projected 5-year survival with SoC with switching adjustment was 3.1%, which is close to the 5-year survival rate of 3.21% estimated from the SEER data [16] for metastatic NSCLC.

No observational data are currently available to validate the OS extrapolation of first-line pembrolizumab in NSCLC as this is a newly approved indication. The plausibility of the approach to modelling OS was supported by feedback from clinician advisory panel review of the projected treatment effects of pembrolizumab and 5-year and 10-year survival percentages.