Cost-effectiveness of nivolumab versus surveillance for the adjuvant treatment of patients with urothelial carcinoma who are at high risk of recurrence: a US payer perspective.

AIM: To evaluate the cost-effectiveness of adjuvant nivolumab compared with surveillance for the treatment of patients with high-risk muscle-invasive urothelial carcinoma (MIUC) after radical resection from a US healthcare payer perspective and to investigate the impact of alternative modeling approaches on the cost-effectiveness results. MATERIAL AND METHODS: A four-state, semi-Markov model consisting of disease free, local recurrence, distant recurrence, and death health states was developed to investigate the cost-effectiveness of nivolumab compared with surveillance over a 30-year time horizon. The model used data from the randomized CheckMate 274 trial (NCT02632409) and published literature to inform transitions among health states, and inputs on cost, utility, adverse event, and disease management. Scenario analyses were conducted to investigate the impact of model structure and key assumptions on the results. One-way deterministic and probabilistic sensitivity analysis were conducted to investigate the robustness of the results. RESULTS: Total expected costs were higher with nivolumab ($162,278) compared with surveillance ($63,027). Nivolumab was associated with improved survival (1.61 life-years gained compared with surveillance) and an incremental gain of 0.98 quality-adjusted life-years (QALYs). Although total treatment costs were higher for nivolumab, cost offsets were observed because of delayed or avoided recurrences and deaths experienced with nivolumab compared with observation. The incremental cost-effectiveness and cost-utility ratios were $61,462/life-year and $100,930/QALY. LIMITATIONS: At the time of analysis, CheckMate 274 had limited follow-up on disease-free survival and no overall survival data. The limited evidence necessitated assumptions on modeling survival after each type of recurrence. CONCLUSIONS: Nivolumab is estimated to be a life-extending and cost-effective option for adjuvant treatment of MIUC for patients who are at high risk of recurrence after undergoing radical resection in the United States. Using a threshold of $150,000/QALY, the cost-effectiveness conclusions remained consistent across the scenario and sensitivity analyses conducted.

Challenges, considerations, and approaches for developing a cost-effectiveness model for the adjuvant treatment of muscle-invasive urothelial carcinoma: with a spotlight on nivolumab versus placebo.

AIMS: To present alternative approaches related to both structural assumptions and data sources for the development of a decision analytic model for evaluating the cost-effectiveness of adjuvant nivolumab compared with surveillance in patients with high-risk muscle-invasive urothelial carcinoma (MIUC) after radical resection. METHODS AND RESULTS: Alternative approaches related to both structural assumptions and data sources are presented to address challenges and data gaps, as well as discussion of strengths and limitations of each approach. Specifically, challenges and considerations related to the following are presented: (1) selection of a modeling approach (partitioned survival model or state transition model) given the available evidence, (2) choice of health state structure (three- or four-state) to model disease progression and subsequent therapy, (3) modeling of outcomes from subsequent therapy using tunnel states to account for time-dependent transition probabilities or absorbing health states with one-off costs and outcomes applied, and (4) methods for modeling health-state transitions in a setting where treatment has curative intent and available survival data are immature. CONCLUSIONS: Multiple considerations must be taken into account when developing an economic model for new, emerging oncology treatments in early lines of therapy, all of which can affect the model's overall ability to estimate (quality-adjusted) survival benefits over a lifetime horizon. This paper identifies a series of key structural and analytic considerations regarding modeling of nivolumab treatment in the adjuvant MIUC setting. Several alternative approaches with regard to structure and data have been included in a flexible cost-effectiveness model so the impact of the alternative approaches on model results can be explored. The impact of these alternative approaches on cost-effectiveness results are presented in a companion article. Our findings may also help inform the development of future models for other treatments and settings in early-stage cancer.

Challenges in conducting fractional polynomial and standard parametric network meta-analyses of immune checkpoint inhibitors for first-line advanced renal cell carcinoma.

Aim: Network meta-analyses (NMAs) increasingly feature time-varying hazards to account for non-proportional hazards between different drug classes. This paper outlines an algorithm for selecting clinically plausible fractional polynomial NMA models. Methods: The NMA of four immune checkpoint inhibitors (ICIs) + tyrosine kinase inhibitors (TKIs) and one TKI therapy for renal cell carcinoma (RCC) served as case study. Overall survival (OS) and progression free survival (PFS) data were reconstructed from the literature, 46 models were fitted. The algorithm entailed a-priori face validity criteria for survival and hazards, based on clinical expert input, and predictive accuracy against trial data. Selected models were compared with statistically best-fitting models. Results: Three valid PFS and two OS models were identified. All models overestimated PFS, the OS model featured crossing ICI + TKI versus TKI curves as per expert opinion. Conventionally selected models showed implausible survival. Conclusion: The selection algorithm considering face validity, predictive accuracy, and expert opinion improved the clinical plausibility of first-line RCC survival models.

Cost-Utility of Nivolumab Plus Ipilimumab in First-Line Treatment of Advanced Melanoma in the United States: An Analysis Using Long-Term Overall Survival Data from Checkmate 067.

OBJECTIVE: The aim of this study was to evaluate the cost-utility of nivolumab plus ipilimumab (NIVO + IPI) versus other first-line therapies for advanced melanoma in the United States (US) from the third-party payer perspective. METHODS: This analysis estimated total expected life-years (LYs), quality-adjusted LYs (QALYs), and costs for first-line treatments of advanced melanoma during a 30-year time horizon using indirect treatment comparisons based on time-varying hazard ratios (HRs) and a three-state partitioned survival model. Overall survival (OS) and progression-free survival reference curves were extrapolated based on 5-year follow-up from the phase III Checkmate 067 trial (NCT01844505). Comparators of NIVO + IPI were NIVO, IPI, pembrolizumab, dabrafenib plus trametinib, encorafenib plus binimetinib (ENCO + BINI), and vemurafenib plus cobimetinib. Drug acquisition costs, treatment administration costs, follow-up time, subsequent therapy data, and adverse event frequencies were obtained from published sources. Utility weights were estimated from Checkmate 067, which compared NIVO + IPI or NIVO monotherapy with IPI monotherapy as first-line therapy in advanced melanoma. A 3% annual discount rate was applied to costs and outcomes. Sensitivity scenarios for BRAF-mutant subgroups were conducted. RESULTS: NIVO + IPI was estimated to generate the longest OS and the highest total costs versus all comparators, accruing 6.99 LYs, 5.70 QALYs, and $469,469 over the 30-year time horizon. The incremental cost utility of NIVO + IPI versus comparators ranged from $2130 per QALY (versus ENCO + BINI) to $76,169 per QALY (versus NIVO). In all base-case and most sensitivity analyses, the incremental cost-utility ratios for NIVO + IPI were below $100,000 per QALY. CONCLUSIONS: NIVO + IPI is estimated to be a life-extending and cost-effective treatment versus other therapies in the US, with base-case incremental cost-utility ratios below $100,000 per QALY.

Heterogeneity in Survival with Immune Checkpoint Inhibitors and Its Implications for Survival Extrapolations: A Case Study in Advanced Melanoma.

Background: Survival heterogeneity and limited trial follow-up present challenges for estimating lifetime benefits of oncology therapies. This study used CheckMate 067 (NCT01844505) extended follow-up data to assess the predictive accuracy of standard parametric and flexible models in estimating the long-term overall survival benefit of nivolumab plus ipilimumab (an immune checkpoint inhibitor combination) in advanced melanoma. Methods: Six sets of survival models (standard parametric, piecewise, cubic spline, mixture cure, parametric mixture, and landmark response models) were independently fitted to overall survival data for treatments in CheckMate 067 (nivolumab plus ipilimumab, nivolumab, and ipilimumab) using successive data cuts (28, 40, 52, and 60 mo). Standard parametric models allow survival extrapolation in the absence of a complex hazard. Piecewise and cubic spline models allow additional flexibility in fitting the hazard function. Mixture cure, parametric mixture, and landmark response models provide flexibility by explicitly incorporating survival heterogeneity. Sixty-month follow-up data, external ipilimumab data, and clinical expert opinion were used to evaluate model estimation accuracy. Lifetime survival projections were compared using a 5% discount rate. Results: Standard parametric, piecewise, and cubic spline models underestimated overall survival at 60 mo for the 28-mo data cut. Compared with other models, mixture cure, parametric mixture, and landmark response models provided more accurate long-term overall survival estimates versus external data, higher mean survival benefit over 20 y for the 28-mo data cut, and more consistent 20-y mean overall survival estimates across data cuts. Conclusion: This case study demonstrates that survival models explicitly incorporating survival heterogeneity showed greater accuracy for early data cuts than standard parametric models did, consistent with similar immune checkpoint inhibitor survival validation studies in advanced melanoma. Research is required to assess generalizability to other tumors and disease stages. Highlights: Given that short clinical trial follow-up periods and survival heterogeneity introduce uncertainty in the health technology assessment of oncology therapies, this study evaluated the suitability of conventional parametric survival modeling approaches as compared with more flexible models in the context of immune checkpoint inhibitors that have the potential to provide lasting survival benefits.This study used extended follow-up data from the phase III CheckMate 067 trial (NCT01844505) to assess the predictive accuracy of standard parametric models in comparison with more flexible methods for estimating the long-term survival benefit of the immune checkpoint inhibitor combination of nivolumab plus ipilimumab in advanced melanoma.Mixture cure, parametric mixture, and landmark response models provided more accurate estimates of long-term overall survival versus external data than other models tested.In this case study with immune checkpoint inhibitor therapies in advanced melanoma, extrapolation models that explicitly incorporate differences in cancer survival between observed or latent subgroups showed greater accuracy with both early and later data cuts than other approaches did.

What Did Time Tell Us? A Comparison and Retrospective Validation of Different Survival Extrapolation Methods for Immuno-Oncologic Therapy in Advanced or Metastatic Renal Cell Carcinoma.

BACKGROUND: The immuno-oncologic (IO) mechanism of action may lead to an overall survival (OS) hazard that changes over time, producing shapes that standard parametric extrapolation methods may struggle to reflect. Furthermore, selection of the most appropriate extrapolation method for health technology assessment is often based on trial data with limited follow-up. OBJECTIVE: To examine this problem, we fitted a range of extrapolation methods to patient-level survival data from CheckMate 025 (NCT01668784, CM-025), a phase III trial comparing nivolumab with everolimus for previously treated advanced renal cell carcinoma (aRCC), to assess their predictive accuracy over time. METHODS: Six extrapolation methods were examined: standard parametric models, natural cubic splines, piecewise models combining Kaplan-Meier data with an exponential or non-exponential distribution, response-based landmark models, and parametric mixture models. We produced three database locks (DBLs) at minimum follow-ups of 15, 27, and 39 months to align with previously published CM-025 data. A three-step evaluation process was adopted: (1) selection of the distribution family for each method in each of the three DBLs, (2) internal validation comparing extrapolation-based landmark and mean survival with the latest CM-025 dataset (minimum follow-up, 64 months), and (3) external validation of survival projections using clinical expert opinion and long-term follow-up data from other nivolumab studies in aRCC (CheckMate 003 and CheckMate 010). RESULTS: All extrapolation methods, with the exception of mixture models, underestimated landmark and mean OS for nivolumab compared with CM-025 long-term follow-up data. OS estimates for everolimus tended to be more accurate, with four of the six methods providing landmark OS estimates within the 95% confidence interval of observed OS as per the latest dataset. The predictive accuracy of survival extrapolation methods fitted to nivolumab also showed greater variation than for everolimus. The proportional hazards assumption held for all DBLs, and a dependent log-logistic model provided reliable estimates of longer-term survival for both nivolumab and everolimus across the DBLs. Although mixture models and response-based landmark models provided reasonable estimates of OS based on the 39-month DBL, this was not the case for the two earlier DBLs. The piecewise exponential models consistently underestimated OS for both nivolumab and everolimus at clinically meaningful pre-specified landmark time points. CONCLUSIONS: This aRCC case study identified marked differences in the predictive accuracy of survival extrapolation methods for nivolumab but less so for everolimus. The dependent log-logistic model did not suffer from overfitting to early DBLs to the same extent as more complex methods. Methods that provide more degrees of freedom may accurately represent survival for IO therapy, particularly if data are more mature or external data are available to inform the long-term extrapolations.

From Data to Improved Decisions: Operations Research in Healthcare Delivery.

BACKGROUND: The Operations Research Interest Group (ORIG) within the Society of Medical Decision Making (SMDM) is a multidisciplinary interest group of professionals that specializes in taking an analytical approach to medical decision making and healthcare delivery. ORIG is interested in leveraging mathematical methods associated with the field of Operations Research (OR) to obtain data-driven solutions to complex healthcare problems and encourage collaborations across disciplines. This paper introduces OR for the non-expert and draws attention to opportunities where OR can be utilized to facilitate solutions to healthcare problems. METHODS: Decision making is the process of choosing between possible solutions to a problem with respect to certain metrics. OR concepts can help systematically improve decision making through efficient modeling techniques while accounting for relevant constraints. Depending on the problem, methods that are part of OR (e.g., linear programming, Markov Decision Processes) or methods that are derived from related fields (e.g., regression from statistics) can be incorporated into the solution approach. This paper highlights the characteristics of different OR methods that have been applied to healthcare decision making and provides examples of emerging research opportunities. EXAMPLES: We illustrate OR applications in healthcare using previous studies, including diagnosis and treatment of diseases, organ transplants, and patient flow decisions. Further, we provide a selection of emerging areas for utilizing OR. CONCLUSIONS: There is a timely need to inform practitioners and policy makers of the benefits of using OR techniques in solving healthcare problems. OR methods can support the development of sustainable long-term solutions across disease management, service delivery, and health policies by optimizing the performance of system elements and analyzing their interaction while considering relevant constraints.

Optimizing statin treatment decisions for diabetes patients in the presence of uncertain future adherence.

BACKGROUND: Statins are an important part of the treatment plan for patients with type 2 diabetes. However, patients who are prescribed statins often take less than the prescribed amount or stop taking the drug altogether. This suboptimal adherence may decrease the benefit of statin initiation. OBJECTIVE: To estimate the influence of adherence on the optimal timing of statin initiation for patients with type 2 diabetes. METHOD: The authors use a Markov decision process (MDP) model to optimize the treatment decision for patients with type 2 diabetes. Their model incorporates a Markov model linking adherence to treatment effectiveness and long-term health outcomes. They determine the optimal time of statin initiation that minimizes expected costs and maximizes expected quality-adjusted life years (QALYs). RESULTS: In the long run, approximately 25% of patients remain highly adherent to statins. Based on the MDP model, generic statins lower costs in men and result in a small increase in costs in women relative to no treatment. Patients are able to noticeably increase their expected QALYs by 0.5 to 2 years depending on the level of adherence. CONCLUSIONS: Adherence-improving interventions can increase expected QALYs by as much as 1.5 years. Given suboptimal adherence to statins, it is optimal to delay the start time for statins; however, changing the start time alone does not lead to significant changes in costs or QALYs.

Comparative effectiveness of guidelines for the management of hyperlipidemia and hypertension for type 2 diabetes patients.

BACKGROUND: Several guidelines to reduce cardiovascular risk in diabetes patients exist in North America, Europe, and Australia. Their ability to achieve this goal efficiently is unclear. METHODS AND FINDINGS: Decision analysis was used to compare the efficiency and effectiveness of international contemporary guidelines for the management of hypertension and hyperlipidemia for patients aged 40-80 with type 2 diabetes. Measures of comparative effectiveness included the expected probability of a coronary or stroke event, incremental medication costs per event, and number-needed-to-treat (NNT) to prevent an event. All guidelines are equally effective, but they differ significantly in their medication costs. The range of NNT to prevent an event was small across guidelines (6.5-7.6 for males and 6.5-7.5 for females); a larger range of differences were observed for expected cost per event avoided (ranges, $117,269-$157,186 for males and $115,999-$163,775 for females). Australian and U.S. guidelines result in the highest and lowest expected costs, respectively. CONCLUSIONS: International guidelines based on the same evidence and seeking the same goal are similar in their effectiveness; however, there are large differences in expected medication costs.

Optimizing the start time of statin therapy for patients with diabetes.

BACKGROUND: Clinicians often use validated risk models to guide treatment decisions for cardiovascular risk reduction. The most common risk models for predicting cardiovascular risk are the UKPDS, Framingham, and Archimedes models. In this article, the authors propose a model to optimize the selection of patients for statin therapy of hypercholesterolemia, for patients with type 2 diabetes, using each of the risk models. For each model,they evaluate the role of age, gender, and metabolic state on the optimal start time for statins. METHOD: Using clinical data from the Mayo Clinic electronic medical record, the authors construct a Markov decision process model with health states composed of cardiovascular events and metabolic factors such as total cholesterol and high-density lipoproteins. They use it to evaluate the optimal start time of statin treatment for different combinations of cardiovascular risk models and patient attributes. RESULTS: The authors find that treatment decisions depend on the cardiovascular risk model used and the age, gender, and metabolic state of the patient. Using the UKPDS risk model to estimate the probability of coronary heart disease and stroke events, they find that all white male patients should eventually start statin therapy; however, using Framingham and Archimedes models in place of UKPDS, they find that for male patients at lower risk, it is never optimal to initiate statins. For white female patients, the authors also find some patients for whom it is never optimal to initiate statins. Assuming that age 40 is the earliest possible start time, the authors find that the earliest optimal start times for UKPDS, Framingham, and Archimedes are 50, 46, and 40, respectively, for women. For men, the earliest optimal start times are 40, 40, and 40, respectively. CONCLUSIONS: In addition to age, gender, and metabolic state, the choice of cardiovascular risk model influences the apparent optimal time for starting statins in patients with diabetes.