FormalPara Key Summary Points

Why carry out this study?

Women with persistent, recurrent, or metastatic cervical cancer have a poor prognosis and an unmet need for life-extending treatment options.

A study based on an interim analysis of the phase III KEYNOTE-826 trial previously demonstrated that pembrolizumab + chemotherapy provided a cost-effective alternative to standard-of-care chemotherapy alone for the treatment of advanced cervical cancer.

This study further explores the cost-effectiveness of pembrolizumab + chemotherapy using data from the final analysis of KEYNOTE-826, which showed a prolonged plateau in progression-free survival for pembrolizumab + chemotherapy-treated patients, and considers a societal perspective in the United States (US).

What was learned from the study?

Pembrolizumab + chemotherapy provides a twofold increase in life-years and quality-adjusted life-years (QALYs) in people with advanced cervical cancer compared with standard-of-care chemotherapy.

Pembrolizumab + chemotherapy was shown to be cost-effective at a willingness-to-pay threshold of US $150,000/QALY gained compared with standard-of-care chemotherapy, and including productivity gains led to an incremental cost-effectiveness ratio of US $58,385 per QALY.

The clinical and cost-effectiveness results from this study can be used to assist US payer decision-making on the use of pembrolizumab + chemotherapy for the treatment of advanced cervical cancer.

Introduction

Cervical cancer is the fourth most common cancer in women [1]. In the United States, there will be an estimated 13,820 cases of cervical cancer in 2024, which may result in 4360 deaths [2]. Despite screening in the United States, it remains the second leading cause of cancer death in women aged 20–39 years [3].

Cervical cancer is characterized as advanced if it is persistent, recurrent, or metastatic. Symptoms at an advanced stage include fatigue, feeling unwell, weight loss, gripping abdominal pain, blood in urine, and excessive vomiting. Besides physical symptoms, worries about premature death increase anxiety and depression in young women with advanced cervical cancer [4].

At an advanced stage where cervical cancer has metastasized, the 5-year survival rate is 19% [2] and treatment options are limited. Around 15% of cases are diagnosed when advanced [2], highlighting the unmet need for effective treatments for patients with advanced cervical cancer.

In October 2021, the US Food and Drug Administration (FDA) approved KEYTRUDA® (pembrolizumab), in combination with chemotherapy, as a first-line treatment for patients with advanced cervical cancer who have a programmed death-ligand 1 (PD-L1)-combined positive score ≥ 1 as determined by an FDA-approved test, based on KEYNOTE-826 trial results (NCT03635567) [5].

The interim analysis of KEYNOTE-826 (May 2021 data cutoff) demonstrated that pembrolizumab + standard-of-care chemotherapy (with or without bevacizumab) provided statistically significant overall and progression-free survival improvements in patients with advanced cervical cancer [6]. In the final analysis (October 2022 data cutoff), median overall survival in the pembrolizumab group was 28.6 months versus 16.5 months for chemotherapy [7]. Incidence of adverse events was similar across arms and consistent with the known safety profiles of pembrolizumab and chemotherapy and the KEYNOTE-826 trial indication [6, 7].

Based on KEYNOTE-826 results, the National Comprehensive Cancer Network added pembrolizumab combination therapy to the list of preferred regimens in its 2022 guidelines as first-line therapy for the treatment of PD-L1-positive cervical cancer in the United States. Pembrolizumab is distinct from standard chemotherapies (cisplatin/carboplatin + paclitaxel ± bevacizumab) as it represents the only first-line immuno-oncology agent approved by the FDA to treat advanced cervical cancer [8].

A cost-effectiveness study using patient-level data from an interim analysis of KEYNOTE-826 demonstrated that pembrolizumab + chemotherapy provided a cost-effective alternative to chemotherapy alone [9]. Here we use the final analysis of KEYNOTE-826 to further explore the cost-effectiveness of pembrolizumab + chemotherapy.

Methods

This study was based on previously collected, anonymized clinical trial data from KEYNOTE-826 provided by the study sponsor; as such, it was exempt from institutional review board or ethical approval. The KEYNOTE-826 trial was approved by the appropriate ethics body at each participating center [6, 7].

We built a cost-effectiveness model in Microsoft Excel with a 1-week cycle length and 50-year time horizon. The model captures lifetime effects and costs, given an average age of 51.0 years at diagnosis. The cycle length allowed accurate accounting for the dosing schedules of included interventions and avoided material half-cycle correction. We adopted a US healthcare perspective and followed relevant guidelines, including the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) 2022 statement [10,11,12,13].

In KEYNOTE-826, progression-free survival was seen to plateau for patients receiving pembrolizumab + chemotherapy. This resulted in scenarios where the extrapolations of progression-free survival and overall survival crossed in the partitioned survival model, as this approach includes no structural link between these endpoints. As the model structure was anticipated to create uncertainty around the results, we followed best practice and developed both a state-transition and partitioned survival analysis approach [14]. We used the state-transition approach in our base case, leveraging KEYNOTE-826 individual patient-level data to derive time-to-progression and post-progression survival estimates to inform transition, alongside progression-free and overall survival (Fig. 1). The partitioned survival approach is considered to quantify structural uncertainty [15].

Fig. 1
figure 1

Three-health state semi-Markov state-transition model. Numbers in square brackets indicate transition probabilities, defined in the figure. PFS(t, arm), TTP(t, arm), and PPS(t, arm) are the survivor functions for progression-free survival, time to progression, and post-progression survival, respectively, at time t and the given treatment arm. PFS progression-free survival, PPS post-progression survival, TTP time to progression

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Decision Problem

The population modeled was patients with advanced cervical cancer and a combined positive score ≥ 1 [16]. This population accounted for 89% of the patients in KEYNOTE-826 [6]. KEYNOTE-826 was designed to evaluate pembrolizumab + chemotherapy (paclitaxel + cisplatin or paclitaxel + carboplatin) ± bevacizumab versus chemotherapy ± bevacizumab in women with cervical cancer [6].

Within the model, dosing for pembrolizumab + chemotherapy is aligned with KEYNOTE-826 and the FDA license [16]. Pembrolizumab is given until progression for up to 2 years or 35 treatment cycles; hence, a 105-week stopping rule was applied. Except for bevacizumab, which has no stopping rule, chemotherapy in both arms is given until progression for a maximum of six treatment cycles; hence, an 18-week stopping rule was applied for the concerned regimens.

Subsequent treatment was modeled as per KEYNOTE-826: 48% of patients who progressed in KEYNOTE-826 received second-line chemotherapy, which aligns with US real-world data [3]. For simplification, the model only included second-line treatment costs administered to ≥ 3% of patients in either arm of KEYNOTE-826.

Cost-effectiveness was assessed in terms of costs per life-year and quality-adjusted life-year (QALY) gained. Consistent with the current Institute for Clinical and Economic Review Value Assessment Framework, a US $150,000/QALY willingness-to-pay threshold was used [10].

Model Inputs

Efficacy

The median follow-up period in KEYNOTE-826 was 28.6 months; therefore, it was necessary to perform extrapolations to estimate long-term outcomes for a lifetime time horizon of up to 50 years. Standard parametric survival models (exponential, Weibull, log-normal, log-logistic, Gompertz, and generalized gamma) were fit to the full individual patient-level data separately for pembrolizumab + chemotherapy and chemotherapy (Supplementary Material 1). More flexible methods were explored if one-piece models did not provide a reasonable fit to the patient-level data. These methods included “two-piece” models with the Kaplan–Meier curve followed by a parametric survival model fit to the data from a certain time point onwards, and Royston–Parmar spline models with up to three knots [17]. Curve selection was based on visual fit to patient-level data, the clinical plausibility of long-term extrapolations and hazard functions, and the statistical fit to the patient-level data. Data from the Gynecologic Oncology Group (GOG)-240 trial were used to validate extrapolations [18].

Time-to-Event Modeling

Time-to-progression and progression-free and post-progression survival inputs were informed by data from the primary KEYNOTE-826 analysis. In the base case, three-knot spline models were used for time to progression and progression-free survival. Deaths were recorded as an event for progression-free survival but as a censoring event for time to progression. Therefore, progression-free survival is the time to a composite endpoint of progression or death, whereas time to progression is based on disease progression alone. The flexible spline model follows the change in hazard, meaning it captured the plateau in pembrolizumab patient survival well. The model assumptions and selection process are described in Supplementary Materials 1 and 2, respectively.

As the number of deaths in KEYNOTE-826 before progression was small, pre-progression mortality was estimated from time to progression and progression-free survival using the formula

$${\text{Prob}}\left( {{\text{Death pre-progression from }}t{\text{ to }}t + 1} \right) = S_{{{\text{TTP}}}} \left( {t + 1} \right)/S_{{{\text{TTP}}}} \left( t \right) \, {-} \, S_{{{\text{PFS}}}} \left( {t + 1} \right)/S_{{{\text{PFS}}}} \left( t \right),$$

where Sx(t) is the survival function for endpoint(x) at time(t).

To avoid clinically implausible pre-progression mortality estimates in the cost-effectiveness model, the same modeling approach and specification were selected for progression-free survival as for time to progression (Fig. 2).

Fig. 2
figure 2

Kaplan–Meier curves and long-term extrapolations of survival outcomes from KEYNOTE-826. OS overall survival, PEM pembrolizumab, PFS progression-free survival, PPS post-progression survival, pSoC pembrolizumab + standard-of-care chemotherapy, SoC standard of care, TTP time to progression

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In the base-case analysis, post-progression survival data from KEYNOTE-826 were extrapolated using generalized gamma models fit to all Kaplan–Meier data (Supplementary Material 1).

When using the partitioned survival model approach, progression-free survival was modeled using the same three-knot spline as the state-transition model base case. Similarly, overall survival was modeled using three-knot spline models.

Safety

In the base case, we included grade ≥ 3 drug-related adverse events that occurred in ≥ 5% of patients in either arm, including anemia, neutropenia, urinary tract infection, hypertension, thrombocytopenia, febrile neutropenia, and decreased platelet, white blood cell, and neutrophil counts. The mean duration of adverse events was obtained from KEYNOTE-826.

Health-Related Quality of Life

The European Quality of Life 5-Dimension 5-Level version instrument was used to measure generic health status in patients enrolled in KEYNOTE-826. We used patient-level data from KEYNOTE-826 and the US-specific value set to calculate utilities [19].

We computed utilities by time to death to capture how pembrolizumab impacts quality of life (QoL) in patients with advanced cervical cancer. Time-to-death utilities were deemed more appropriate than utility inputs stratified by progression status because “pseudoprogression” issues can be encountered with immuno-oncology drugs, where the action of treatment may be mistaken for disease progression. They also provide finer gradations in utility across patients compared with progression-based utilities [20]. In the base case, we applied a regression model with time-to-death categories and grade ≥ 3 adverse events to predict treatment-independent utilities for use in the analysis (Supplementary Material 1). Utilities in the model were capped using US-specific data on general population utility [21].

Costs

The analysis considered drug acquisition (wholesale acquisition cost) [22], treatment administration, PD-L1 marker testing, adverse events, resource use, and end-of-life costs. Costs were adjusted to 2022 US dollars using the US Bureau of Labor Statistics, Consumer Price Index, and Medical Care inflation calculator. Costs and QALYs were discounted at 3% per year.

Time on treatment was based on KEYNOTE-826 patient-level data. Extrapolation was not needed for pembrolizumab, paclitaxel, cisplatin, or carboplatin, as the clinical trial covered the maximum duration these treatments were given in both arms. For bevacizumab, time-on-treatment patient-level data were extrapolated using a two-piece log-logistic model that was fit from 46 weeks onward (Supplementary Material 1). We sourced wholesale acquisition cost prices from the AnalySource database [22]. Treatment administration, PD-L1 testing, and other resource use costs were sourced from the Centers for Medicare and Medicaid Services [22, 23]. The cost of PD-L1 testing reflected the cost per PD-L1-positive patient identified rather than the cost per test, with an average of 1.1 tests required per positive patient identified. Clinical experts informed resource use frequencies in the pre- and post-progression health states, assuming no differences between treatment arms. Adverse events were costed using Medicare severity diagnosis-related groups [10]. We retrieved end-of-life costs from the literature [24]. The tested scenarios, model assumptions, and inputs are reported in Supplementary Material 1.

Sensitivity Analyses

We performed one-way sensitivity analyses to explore the impact of uncertainty in individuals’ input parameters on results and a probabilistic sensitivity analysis of 5000 iterations to explore the combined uncertainty of all inputs on results.

Scenario Analyses

Multiple scenario analyses were conducted to investigate various modeling assumptions (Supplementary Material 3).

A key scenario was assessing the structural uncertainty introduced by the choice of modeling approach—state-transition or partitioned survival. The partitioned survival model was parameterized using the same inputs as the state-transition model in every plausible instance. The only difference was modeling survival using an extrapolation of overall survival data from KEYNOTE-826. Overall survival was extrapolated using a Royston–Parmar spline model that had three knots.

Treatment options in second line are rapidly evolving, and real-world data suggest that tisotumab vedotin and pembrolizumab are widely administered at second line in this US subpopulation [25]. Therefore, we performed a scenario analysis where tisotumab vedotin and pembrolizumab were added to subsequent treatment options. Inputs for this scenario were clinically informed and resulted in the following assumptions: in the pembrolizumab arm, 60% of progressed patients receive tisotumab vedotin, 20% pembrolizumab, and 20% chemotherapy. In second-line chemotherapy, we assumed that 75% received tisotumab vedotin and 25% pembrolizumab. We evaluated results separately in patients who received bevacizumab and those who did not using a 90-day friction-cost method [26].

We also explored pembrolizumab’s societal impact by including productivity gains. These were calculated using inputs from Supplementary Material 1 and assume that patients with progressed disease no longer work, while those who are progression-free work part-time.

Results

Pembrolizumab extended the mean life expectancy of patients with advanced cervical cancer from 1.8 to 6.7 life-years (mean life-years gained/patient: 4.9). The gain in life expectancy was mostly attributable to delayed disease progression as a result of pembrolizumab treatment; hence, in most life-years gained, patients have a favorable health-related QoL. Total QALYs were 5.0 and 1.3/patient for pembrolizumab + chemotherapy and chemotherapy, respectively (mean discounted QALYs gained/patient: 3.7) (Table 1). The discounted costs incurred with pembrolizumab + chemotherapy were US $320,247 versus US $105,446 with chemotherapy (mean incremental costs/patient: US $214,801). The difference in costs between pembrolizumab + chemotherapy and chemotherapy was mainly driven by pembrolizumab acquisition costs. The incremental cost-effectiveness ratio for pembrolizumab + chemotherapy versus chemotherapy was US $44,037/life-year and US $58,446/QALY.

Table 1 Results of the cost-effectiveness evaluation (discounted)
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Probabilistic sensitivity analysis results are presented in Fig. 3. The probabilistic incremental cost-effectiveness ratios of US $43,395/life-year and US $57,597/QALY align with deterministic incremental cost-effectiveness ratios. The cost-effectiveness acceptability curve shows that, at willingness-to-pay thresholds of US $50,000, US $100,000, and US $150,000 per QALY, pembrolizumab + chemotherapy was cost-effective in 14.9%, 99.5%, and 99.6% of the iterations, respectively. The one-way sensitivity analyses represented in Fig. 4 demonstrate that the cost-effectiveness of pembrolizumab + chemotherapy is relatively robust to uncertainty in model inputs.

Fig. 3
figure 3

Probabilistic sensitivity analysis: cost-effectiveness acceptability curve (A) and incremental cost-effectiveness plane (B). PEM pembrolizumab, QALY quality-adjusted life-year, SoC standard of care, US United States

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Fig. 4
figure 4

One-way sensitivity and scenario analysis results. S represents the scenarios resulting in significant changes in the ICER. bev bevacizumab, BICR blinded independent central review, ICER incremental cost-effectiveness ratio, PEM pembrolizumab, PFS progression-free survival, pSoC pembrolizumab + standard-of-care chemotherapy, QALY quality-adjusted life-year, OWSA one-way sensitivity analysis, TTP time to progression, TV tisotumab vedotin

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The scenario analysis where pembrolizumab and tisotumab vedotin were included at second line resulted in a slightly less favorable incremental cost-effectiveness ratio (US $60,783/QALY). The scenario exploring productivity gains due to treatment with pembrolizumab + chemotherapy led to a similar incremental cost-effectiveness ratio (US $58,385/QALY).

Scenarios and deterministic sensitivity analysis exploring bevacizumab use suggested that, although costs and outcomes differed between patients who had or had not received bevacizumab, incremental cost-effectiveness results between pembrolizumab strategies were very similar and insensitive to bevacizumab use. This aligns with recent evidence demonstrating that pembrolizumab provides a meaningful benefit regardless of whether it is provided in combination with bevacizumab [27].

Model results are sensitive to the approach used (state-transition or partitioned survival), with the partitioned survival approach resulting in an incremental cost-effectiveness ratio of US $73,636/QALY. This increased the incremental cost-effectiveness ratio but, along with all the other scenarios, is still below the Institute for Clinical and Economic Review’s willingness-to-pay threshold of US $150,000/QALY [10].

Discussion

Summary of Main Results

This cost-effectiveness model has demonstrated that, over a lifetime, pembrolizumab results in an approximate doubling of expected life-years and QALYs per patient. The incremental cost-effectiveness ratio for pembrolizumab + chemotherapy versus chemotherapy was US $58,446/QALY, which is below the US $150,000 willingness-to-pay threshold proposed in the Institute for Clinical and Economic Review’s framework [10]. Decision uncertainty is also low, with each of the 1000 probabilistic iterations falling below this threshold.

The probabilistic sensitivity analysis and one-way sensitivity analyses demonstrate that the cost-effectiveness of pembrolizumab + chemotherapy is relatively robust to uncertainty in model inputs. The scenario analyses demonstrate that the base case incremental cost-effectiveness ratios may be conservative, with many scenarios producing more favorable incremental cost-effectiveness ratios.

Results in the Context of Published Literature

Our results differ from those reported by Shi et al. and Barrington et al. [28, 29]. Shi et al. report an incremental cost-effectiveness ratio of US $253,322/QALY, compared with the US $73,636/QALY in our analysis using the equivalent model structure. The difference in incremental cost-effectiveness ratios is mainly caused by differences in estimated life-years, and consequently, QALYs gained between the analyses. While we estimate QALYs gained to be 2.9 with the partitioned survival model approach, Shi et al. estimate 0.8 [29]. This is likely driven by the use of the final analysis for KEYNOTE-826 in our study, which further accentuates the plateau in progression-free survival for patients who received pembrolizumab + chemotherapy, whereas Shi et al. used data from published reports on the interim analysis. Barrington et al. report an incremental cost-effectiveness ratio of US $341,361/QALY but present an insufficient description of the analytical framework, survival inputs, and settings to clarify whether differences in the modeling approach drove the incremental cost-effectiveness ratio differences [28]. Moreover, Barrington et al. used KEYNOTE-826 median values for survival instead of established methods to estimate mean and long-term survival, which is likely to underestimate benefits for the pembrolizumab + chemotherapy group compared with chemotherapy alone. Neither study can be as robust as the one presented in this paper, as both relied on the literature for efficacy and utility estimates, whereas our study presents analyses using patient-level data from KEYNOTE-826.

Strengths and Weaknesses

By using KEYNOTE-826 patient-level data to derive model inputs, our study provides an accurate analysis of pembrolizumab + chemotherapy cost-effectiveness. Furthermore, we have demonstrated that the cost-effectiveness of pembrolizumab + chemotherapy is not dependent on the modeling approach employed. Also, we modeled subsequent treatments in line with US clinical practice and performed a scenario analysis to assess the impact of tisotumab vedotin and pembrolizumab at second line, relying on expert advice and external trial data to inform costs and post-progression survival [30,31,32]. Finally, we validated long-term extrapolations of efficacy with observed 4-year data from the GOG-240 study [18].

However, even with final analysis data the follow-up was incomplete, so some extrapolation was necessary. Also, some subsequent treatments received by patients in the trial did not align with real-world treatments.

Implications for Practice and Future Research

With current standard of care, the 5-year survival rate for women with advanced cervical cancer is 17% and treatment options are few. Adding pembrolizumab to first-line treatment extends the long-term survival of patients with advanced cervical cancer by delaying progression and improving QoL; its cost-effectiveness ratio lies below that considered cost-effective by the Institute for Clinical and Economic Review [10].

This is the first cost-effectiveness evaluation of pembrolizumab in advanced cervical cancer to consider indirect costs in a scenario analysis from a US payer perspective. This analysis shows that pembrolizumab offers substantial productivity gains—highlighting the importance of indirect costs in relatively young populations. Future research should aim to validate subsequent treatment proportions for tisotumab vedotin and pembrolizumab when more published data become available.

Conclusion

This evaluation demonstrates that pembrolizumab + chemotherapy is cost-effective for first-line treatment of patients in the United States who have advanced cervical cancer with a combined positive score ≥ 1, at a willingness-to-pay threshold of US $150,000/QALY.