Cost-effectiveness Of New Treatments Research Articles

Strategies to decrease inequalities in cancer therapeutics, care and prevention: Proceedings on a conference organized by the Pontifical Academy of Sciences and the European Academy of Cancer Sciences, Vatican City, February 23-24, 2023.

Analyses of inequalities related to prevention and cancer therapeutics/care show disparities between countries with different economic standing, and within countries with high Gross Domestic Product. The development of basic technological and biological research provides clinical and prevention opportunities that make their implementation into healthcare systems more complex, mainly due to the growth of Personalized/Precision Cancer Medicine (PCM). Initiatives like the USA-Cancer Moonshot and the EU-Mission on Cancer and Europe's Beating Cancer Plan are initiated to boost cancer prevention and therapeutics/care innovation and to mitigate present inequalities. The conference organized by the Pontifical Academy of Sciences in collaboration with the European Academy of Cancer Sciences discussed the inequality problem, dependent on the economic status of a country, the increasing demands for infrastructure supportive of innovative research and its implementation in healthcare and prevention programs. Establishing translational research defined as a coherent cancer research continuum is still a challenge. Research has to cover the entire continuum from basic to outcomes research for clinical and prevention modalities. Comprehensive Cancer Centres (CCCs) are of critical importance for integrating research innovations to preclinical and clinical research, as for ensuring state-of-the-art patient care within healthcare systems. International collaborative networks between CCCs are necessary to reach the critical mass of infrastructures and patients for PCM research, and for introducing prevention modalities and new treatments effectively. Outcomes and health economics research are required to assess the cost-effectiveness of new interventions, currently a missing element in the research portfolio. Data sharing and critical mass are essential for innovative research to develop PCM. Despite advances in cancer research, cancer incidence and prevalence is growing. Making cancer research infrastructures accessible for all patients, considering the increasing inequalities, requires science policy actions incentivizing research aimed at prevention and cancer therapeutics/care with an increased focus on patients' needs and cost-effective healthcare.

Key considerations for modelling the long-term costs and benefits of treatments for ANCA-associated vasculitis.

Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a group of severe and chronic autoimmune diseases. Patients undergo two treatment phases: inducing remission and maintaining remission to prevent organ damage. Immunosuppressants, including glucocorticoids (GCs) are used as first-line treatment, but long-term GC use is associated with toxic effects. Novel treatments reduce or replace the need for long-term GC, and therefore can reduce GC-related toxicity. The evolving treatment landscape has presented new challenges for health technology assessment (HTA) of new treatments in AAV and long-term modelling of costs and outcomes in this disease. Using the appraisal of avacopan in England (NICE) as a case study, this paper aims to identify the key challenges involved in the economic evaluation of new treatments for AAV, with a particular focus on the long-term modelling of the treatment costs and benefits for the purpose of HTA. The outcome of this study is a set of recommendations for modelling the cost-effectiveness of new treatments for AAV from the HTA perspective. The discussion focuses on the appropriate model structure, approach to modelling end-stage renal disease (ESRD) as a key determinant of costeffectiveness, capturing the impact of GC-related adverse events, and estimation of short and long-term costs of AAV. Economic evaluation of new treatments for AAV needs to capture all relevant downstream effects. ESRD is a key driver of cost-effectiveness but is associated with major uncertainty. Future observational studies need to offer sufficient detail to allow for differentiation in event rates across treatment options.

Reconstructing the value puzzle in health technology assessment: a pragmatic review to determine which modelling methods can account for additional value elements.

Health technology assessment (HTA) has traditionally relied on cost-effectiveness analysis (CEA) as a cornerstone of evaluation of new therapies, assessing the clinical validity and utility, the efficacy, and the cost-effectiveness of new interventions. The current format of cost-effectiveness analysis, however, does not allow for inclusion of more holistic aspects of health and, therefore, value elements for new technologies such as the impact on patients and society beyond its pure clinical and economic value. This study aimed to review the recent modelling attempts to expand the traditional cost-effectiveness analysis approach by incorporating additional elements of value in health technology assessment. A pragmatic literature review was conducted for articles published between 2012 and 2022 reporting cost-effectiveness analysis including value aspects beyond the clinical and cost-effectiveness estimates; searches identified 13 articles that were eligible for inclusion. These expanded modelling approaches mainly focused on integrating the impact of societal values and health equity in cost-effectiveness analysis, both of which were championed as important aspects of health technology assessment that should be incorporated into future technology assessments. The reviewed cost-effectiveness analysis methods included modification of the current cost-effectiveness analysis methodology (distributional cost-effectiveness analysis, augmented cost-effectiveness analysis, extended cost-effectiveness analysis) or the use of multi-criteria decision analysis. Of these approaches, augmented cost-effectiveness analysis appears to have the most potential by expanding traditional aspects of value, as it uses techniques already familiar to health technology assessment agencies but also allows space for incorporation of qualitative aspects of a product's value. This review showcases that methods to unravel additional value elements for technology assessment exist, therefore, patient access to promising technologies can be improved by moving the discussion from "if" to "how" additional value elements can inform decision-making.

Real-world evidence on the economic implications of CGRP-mAbs as preventive treatment of migraine

BackgroundCalcitonin gene-related peptide (CGRP) monoclonal antibodies (mAbs) are approved in Europe as preventive treatment of migraine in patients with at least four monthly migraine days. Migraine gives rise to direct healthcare expenditures, but most of the economic burden of migraine is socioeconomic. Evidence on the socioeconomic implications of CGRP-mAbs is, however, limited. There is an increasing interest in supplementing evidence from randomised controlled trials (RCTs) with real-world evidence (RWE) to aid clinical decision making and inform decision making for migraine management. The objective of this study was to generate RWE on the health economic and socioeconomic implications of administering CGRP-mAbs to patients with chronic migraine (CM) and episodic migraine (high-frequency episodic migraine (HFEM), and low-frequency episodic migraine (LFEM)).MethodsReal-world data (RWD) on Danish patients with CM, HFEM, and LFEM were collected via two Danish patient organisations and two informal patient networks and used in a tailored economic model. Treatment effects of CGRP-mAbs on health economic and socioeconomic outcomes were estimated using a sub-sample of patients with CM who receive CGRP-mAb treatment.ResultsA total of 362 patients (CM: 199 [55.0%], HFEM: 80 [22.1%], LFEM: 83 [22.9%]) were included in the health economic model (mean age 44.1 ± 11.5, 97.5% female, 16.3% received treatment with CGRP-mAbs), and 303 patients were included in the socioeconomic model (15.2% received treatment with CGRP-mAbs). Health economic savings from initiating CGRP-mAb treatment totalled €1,179 per patient with CM per year on average (HFEM: €264, LFEM: €175). Socioeconomic gains from initiating CGRP-mAb treatment totalled an average gross domestic product (GDP) gain of €13,329 per patient with CM per year (HFEM: €10,449, LFEM: €9,947).ConclusionOur results indicate that CGRP-mAbs have the potential to reduce both health economic expenditures and the socioeconomic burden of migraine. Health economic savings are used as a basis for health technology assessments (HTAs) of the cost-effectiveness of new treatments, which implies that important socioeconomic gains may not be given enough importance in decision making for migraine management.

Use of Minimal Residual Disease Status to Reduce Uncertainty in Estimating Long-term Survival Outcomes for Newly Diagnosed Multiple Myeloma Patients.

Background: Demonstrating the cost-effectiveness of new treatments for multiple myeloma (MM) often relies on the extrapolation of overall survival (OS) trial data. This method can introduce uncertainty in long-term survival estimates if OS data are immature, as is often the case in newly diagnosed MM (NDMM). We explore the use of the relationship between minimal residual disease (MRD) status and OS to reduce uncertainty of long-term survival outcomes. Objectives: To evaluate if uncertainty in long-term modeled outcomes in NDMM is reduced using a response-based partitioned survival model (PSM), whereby patients were categorized as MRD-positive or -negative, relative to a standard PSM, when OS data are immature. Methods: Standard and response-based PSMs, estimating patient life-years (LYs) over a lifetime horizon, were developed for NDMM patients treated with bortezomib, thalidomide, and dexamethasone (BTd) with or without daratumumab as induction and consolidation therapy. In the standard PSM, LYs were determined by extrapolations from individual patient data from CASSIOPEIA. In the response-based PSM, survival was dependent on MRD status at the time of the response assessment via a landmark analysis. Cox-proportional hazard ratios from external sources and CASSIOPEIA informed the relationship for OS between MRD-positive and MRD-negative, and between patients receiving BTd and daratumumab plus BTd, respectively. Uncertainty was assessed by comparing LYs and OS extrapolations from deterministic and probabilistic analyses. Results: This response-based PSM demonstrated reduced uncertainty in long-term survival outcomes compared with the standard PSM (range across extrapolations of 3.4 and 7.7 LYs for daratumumab plus BTd and BTd, respectively, vs 14.8 and 11.8 LYs for the standard PSM). It also estimated a narrower interquartile range of LYs in the probabilistic analyses for the majority of parametric extrapolations. Discussion: Alternative methods to estimate long-term survival outcomes, such as a response-based PSM, can reduce uncertainty in modeling predictions around cost-effectiveness estimates for health technology assessment bodies and payers, thereby supporting faster market access for novel therapies with immature survival data. Conclusions: Use of MRD status in a response-based PSM reduces uncertainty in modeling long-term survival in patients with NDMM and provides a greater number of clinically plausible extrapolations compared with a standard PSM.

Use of Minimal Residual Disease Status to Reduce Uncertainty in Estimating Long-term Survival Outcomes for Newly Diagnosed Multiple Myeloma Patients

Background: Demonstrating the cost-effectiveness of new treatments for multiple myeloma (MM) often relies on the extrapolation of overall survival (OS) trial data. This method can introduce uncertainty in long-term survival estimates if OS data are immature, as is often the case in newly diagnosed MM (NDMM). We explore the use of the relationship between minimal residual disease (MRD) status and OS to reduce uncertainty of long-term survival outcomes. Objectives: To evaluate if uncertainty in long-term modeled outcomes in NDMM is reduced using a response-based partitioned survival model (PSM), whereby patients were categorized as MRD-positive or -negative, relative to a standard PSM, when OS data are immature. Methods: Standard and response-based PSMs, estimating patient life-years (LYs) over a lifetime horizon, were developed for NDMM patients treated with bortezomib, thalidomide, and dexamethasone (BTd) with or without daratumumab as induction and consolidation therapy. In the standard PSM, LYs were determined by extrapolations from individual patient data from CASSIOPEIA. In the response-based PSM, survival was dependent on MRD status at the time of the response assessment via a landmark analysis. Cox-proportional hazard ratios from external sources and CASSIOPEIA informed the relationship for OS between MRD-positive and MRD-negative, and between patients receiving BTd and daratumumab plus BTd, respectively. Uncertainty was assessed by comparing LYs and OS extrapolations from deterministic and probabilistic analyses. Results: This response-based PSM demonstrated reduced uncertainty in long-term survival outcomes compared with the standard PSM (range across extrapolations of 3.4 and 7.7 LYs for daratumumab plus BTd and BTd, respectively, vs 14.8 and 11.8 LYs for the standard PSM). It also estimated a narrower interquartile range of LYs in the probabilistic analyses for the majority of parametric extrapolations. Discussion: Alternative methods to estimate long-term survival outcomes, such as a response-based PSM, can reduce uncertainty in modeling predictions around cost-effectiveness estimates for health technology assessment bodies and payers, thereby supporting faster market access for novel therapies with immature survival data. Conclusions: Use of MRD status in a response-based PSM reduces uncertainty in modeling long-term survival in patients with NDMM and provides a greater number of clinically plausible extrapolations compared with a standard PSM.

Elicitation of Health State Utility Values in Retinitis Pigmentosa by Time Trade-off in the United Kingdom.

Retinitis pigmentosa (RP) is an inherited retinal pathology associated with "night blindness" and progressive loss of peripheral vision, in some cases leading to complete blindness. Health state utility values are required for activities such as modelling disease burden or the cost-effectiveness of new interventions. The current study aimed to generate utility values for health states of varying levels of functional vision in RP, with members of the general public in the UK. Five health states were defined according to standard clinical measures of visual ability. Health state descriptions were developed following interviews with patients with RP in the UK (n=5). Further interviews were conducted for confirmation with healthcare professionals with specific experience of managing patients with RP in the UK (n=2). Interviews with members of the general public in the UK were conducted to value health states. A time trade-off (TTO) process based on the established Measurement and Valuation of Health (MVH) protocol was used. Due to the ongoing COVID-19 pandemic, all interviews were web-enabled and conducted 1:1 by a trained moderator. In total, n=110 TTO interviews were conducted with members of the UK general public. Mean TTO utility values followed the logical and expected order, with increasing visual impairment leading to decreased utility. Mean values varied between 0.78 ± 0.20 ("moderate impairment"), and 0.33 ± 0.26 ("hand motion" to "no light perception"). Supplementary visual analogue scale (VAS) scores also followed the logical and expected order: mean VAS values varied between 47.95 ± 15.38 ("moderate impairment") and 17.22 ± 12.49 in ("hand motion" to "no light perception"). These data suggest that individuals living with RP have substantially impaired quality of life. Utility values for RP have been elicited here using a method and sample that is suitable for economic modelling and health technology assessment purposes.

Network meta-analysis (NMA) of first-line advanced renal cell carcinoma (1L aRCC) treatments: Development of a decision algorithm for fractional polynomial (FP) model selection.

391 Background: The landscape of 1L aRCC treatment is rapidly evolving from tyrosine kinase inhibitor (TKI) monotherapy to their combination with immuno-oncology (IO) agents. Differences in mechanisms of action (MoA) of different IO+TKIs and of TKI monotherapy lead to differences in survival trends and generate non-constant hazards over time. To assess the cost-effectiveness of new treatments, trials comparing all relevant treatments are needed. The lack of these necessitate indirect treatment comparisons accounting for non-proportional hazards, such as a FP NMA. Multiple FP models are available to model the time behavior of hazards. Often model selection is solely based on statistical fit, which can result in clinically implausible long-term survival estimates. Methods: We sought to develop an algorithm that improves FP model selection for predictive accuracy, clinical plausibility and goodness of fit. The network included FDA-approved treatments avelumab + axitinib, pembrolizumab + lenvatinib (P+L), nivolumab + cabozantinib (N+C), and pembrolizumab + axitinib, with sunitinib (S) as a common comparator. Synthetic progression-free survival (PFS) and overall survival (OS) data were reconstructed from 4 phase 3 clinical trials and 44 FP models were explored. For model selection, a priori criteria (face validity and predictive accuracy against trial data [median survival, landmark survival at 24 months, and restricted mean survival time]) were used along with statistical fit criteria. Criteria for clinical plausibility, and long-term survival extrapolations and hazard functions of viable models were discussed with clinical experts to select the optimal models. Results: The selected FP models based solely on statistical fit criteria led to clinically implausible survival extrapolations. Using our proposed selection algorithm, three models were considered viable for PFS and two models for OS. The optimal FP model predicted the highest PFS for P+L and for N+C (medians of 23.2 and 19.0 months), and the lowest PFS for S (9.8 months). The PFS models overestimated outcomes for most treatments. The OS model was selected in line with expert opinion that subsequent treatment would cause IO + TKI vs. S survival curves to cross over time. OS was similar for all IO + TKI treatments. Conclusions: The use of additional model selection criteria based on face validity, predictive accuracy and expert opinion helped improve the clinical plausibility of survival outcomes compared to models based on statistical fit only. In our study, FP models generally performed imperfectly against some of the trial data. This likely reflects heterogeneity between trial populations, differing MoAs and subsequent treatments and highlights further research is needed on the integration of adjustment for heterogeneity in the fitting of FP models.

The evolution of ICER's review process for new medical interventions and a critical review of economic evaluations (2018-2019): how stakeholders can collaborate with ICER to improve the quality of evidence in ICER's reports.

BACKGROUND: Since its inception in 2006, the Institute for Clinical and Economic Review (ICER) has rapidly gained influence on drug pricing and reimbursement decisions despite historical resistance to the use of cost-effectiveness thresholds in the US health care system. Although patient groups, physicians, and pharmaceutical manufacturers voiced their concerns about the potential negative effects of increased use of ICER's assessments on patient access to innovative medications, there is little guidance and consensus on how the stakeholders should collaborate with ICER to ensure that its reviews reflect the best clinical and economic evidence. OBJECTIVES: To (1) summarize the evolution of ICER's evaluation procedure, scope, and topics; (2) evaluate the effectiveness of stakeholder engagement approaches; and (3) inform stakeholders of their potential role in collaborating with ICER in estimating the cost-effectiveness of new interventions. METHODS: Publicly available ICER evaluations from 2008 to 2019 were systematically reviewed. Changes in evaluation procedures, scope, and topics were summarized. For evaluations that occurred in 2018 (n = 12) and 2019 (n = 8), key characteristics were extracted from 172 letters documenting interactions between ICER and all stakeholders who provided comments to draft reports. Stakeholder suggestions were analyzed in terms of their effectiveness indicated by ICER's reconsideration of its original cost-effectiveness analysis approach. RESULTS: The number of ICER evaluations increased consistently from 2 to 12 per year between 2008 and 2018 but declined to 8 in 2019. Stakeholder opportunity to engage with ICER increased from 1 to 3 per evaluation between 2008 and 2015. ICER initially focused on reviewing general treatment strategies but shifted its focus to specific pharmaceuticals and medical devices in 2014. In 2018 and 2019, 30% of 172 stakeholder letters resulted in a revision in the base-case analysis (49 comments in 2018, 23 in 2019); nearly half of comments in these letters included specific alternative data or a published article to rationalize recommendations. Other common types of suggestions that resulted in ICER's base-case analysis revisions included comments relating to inconsistent methods used to derive model inputs across different treatments (12/49 in 2018, 5/23 in 2019); clinical justifications (12/49 in 2018, 0/23 in 2019); and justifications based on patient perspectives (1/49 in 2018, 5/23 in 2019). These revisions rarely affected ICER's conclusions on the cost-effectiveness of evaluated interventions. Among the 20 assessments that involved 172 stakeholder engagements in 2018 and 2019, only 2% (n = 3) of the engagements (all from 2018) were associated with a change in the cost-effectiveness conclusion. CONCLUSIONS: Between 2018 and 2019, stakeholders leveraged ICER evaluations as opportunities to promote dialogue for better understanding of the value of technologies. Actionable, evidence-based recommendations were accepted more often than other recommendations. DISCLOSURES: No outside funding supported this study. The authors have no conflicts of interest to disclose. Findings from this study were presented as a poster at Virtual ISPOR, May 17-20, 2021.

Expert Elicitation of Long-Term Survival for Pediatric Acute Lymphoblastic Leukemia Patients Receiving CTL019 in Eliana Phase II Study

Introduction: Decision-makers need to understand long-term outcomes to assess cost-effectiveness of new treatments. In the absence of long-term data from clinical trials, current practice to extrapolate overall survival (OS) is typically based on how well the model fits to the observed data without formally involving clinical experts to evaluate the plausibility of the results. At best, 1-2 clinicians may be asked to select the most ‘realistic’ model. Therefore, a more systematic and transparent process is needed to ensure clinical expertise is fully integrated in an unbiased manner. In the global multicenter ELIANA trial (NCT02435849) for CTL019 (chimeric antigen receptor T-cell (CAR-T therapy)), OS data has been reported, but only up to 1.5 years. In the absence of expert opinion regarding long-term OS, the predicted OS varies dramatically depending on the model used to extrapolate the observed data. The aim of this study was to estimate expected OS based on observed data from ELIANA and expert elicited OS rates at 2 years, 3 years, 4 years, and 5 years for patients with relapsed or refractory B-cell pediatric acute lymphoblastic leukemia (r/r/ pALL) treated with CTL019.Methods: A prospective qualitative research study was performed incorporating semi-structured interviews, adapted from the SHeffield ELicitation Framework. Pediatric oncologists and hematologists experienced with CAR-T therapies were recruited. Experts and the study sponsor were not identified to one another. Relevant evidence regarding the patient population from the CTL019 trials, historical first-line pALL data, and historical data on r/r/ pALL for FDA approved interventions was summarized to provide a common basis for expert judgments. Facilitators guided experts through a web-based application for the elicitation of OS rates at 2, 3, 4, and 5 years for patients treated with CTL019 in the ELIANA trial. Experts were first asked to estimate upper and lower plausible limits and then the most likely actual OS value at each time point. Results from each expert were combined with the observed data from ELIANA using time-to-event parametric models (i.e. fractional polynomials) in a Bayesian framework that accounted for each expert's uncertainty in their estimates, which produced an overall distribution of OS over time. In addition, the model was fully extrapolated assuming patients followed general population mortality, adjusted for having had pALL (standard mortality rate=15.2 based on Armstrong 2016).Results: Of the 19 clinical experts contacted, 7 participated in the final elicitation exercise. During a follow-up group meeting, experts agreed that the Gompertz model fit to the observed trial data reflected an appropriate consensus distribution for their estimates. When a Gompertz model was fit to the observed data (up to 1.5 years) and each expert's elicited data (24 to 60 months), the most likely values of OS for patients from the ELIANA trial treated with CTL019 at 2, 3, 4, and 5 years were estimated to be approximately 67.5% (Credible intervals (CrI): 49.5%, 83.1%), 61.1% (39.5%, 81.3%), 57.2% (31.5%, 80.7%), and 54.9% (24.5%, 80.5%) (Figure 1). In the fully extrapolated model, the 10-year OS is estimated to be 52.4% (CrI: 23.4%, 76.9%) with a median survival of approximately 13.4 years (CrI: 1.96, 35.5). Results were similar to OS rates from a phase I/II single-center study (NCT01626495) at 2 years (61%) and slightly higher at three years (52%).Conclusions: Predictions based on observed data and expert elicitations suggest that more than half of the patients in the ELIANA trial treated with CLT019 will survive up to five years. This study provides an example of how expert opinion can be elicited and synthesized with observed survival data using a transparent and unbiased process, which captures expert uncertainty and ensures that projected long-term survival is clinical plausible.Disclaimer: The study was sponsored by Novartis Pharmaceuticals Corporation [Display omitted] DisclosuresCope:Precision Health Economics: Employment; Novartis Pharmaceuticals Corporation: Consultancy. Ayers:Precision Health Economics: Employment; Novartis Pharmaceuticals Corporation: Consultancy. Shih:Precision Health Economics: Employment; Novartis Pharmaceuticals Corporation: Consultancy. Zhang:Novartis Pharmaceuticals Corporation: Employment. Jansen:Novartis Pharmaceuticals Corporation: Consultancy; Precision Health Economics: Employment. Batt:Precision Health Economics: Consultancy; Novartis Pharmaceuticals Corporation: Consultancy; Bayer: Consultancy.

HT1 Effects of Stakeholder Engagement with Icer on Cost-Effectiveness of New Interventions: Lessons Learned from a Critical Review of Evaluations from 2018 to 2019

Since its foundation in 2006, the Institute for Clinical and Economic Review (ICER) has had increasing influence on drug pricing and reimbursement decisions in the US. Such decisions directly affect all stakeholders—insurers, providers, manufacturers, patients, and others—yet there is little synthesis of the effectiveness of engagements by stakeholders on ICER’s assessment of the cost-effectiveness of new interventions. The objective of this study is to evaluate the effectiveness of stakeholder engagement approaches and inform stakeholders of their potential role in collaborating with ICER.

MO516A STRUCTURED EXPERT ELICITATION TO INFORM AND VALIDATE MORTALITY EXTRAPOLATIONS FOR A COST-EFFECTIVENESS ANALYSIS OF DAPAGLIFLOZIN

Abstract Background and Aims Elevated albuminuria in patients with chronic kidney disease (CKD) is associated with increased risks of CKD progression, cardiovascular events and all-cause death. In the DAPA-CKD study, dapagliflozin significantly reduced the risk of all-cause death in patients with elevated albuminuria compared with placebo (hazard ratio: 0.69; 95% confidence interval 0.53–0.88). To assess the cost-effectiveness of new treatments, decision makers require survival estimates over a longer period than that of a typical clinical trial, usually over a lifetime time horizon. A formal elicitation process is currently underway to obtain estimates of long-term survival of patients with albuminuric CKD from clinical experts. Their responses will be used to validate extrapolations of all-cause mortality data from DAPA-CKD, which could inform cost-effectiveness analyses for dapagliflozin. Method Targeted literature searches were conducted to collate data on all-cause mortality in patients with CKD and elevated albuminuria. Clinical trials and observational studies were included if they involved non-dialysis-dependent patients with CKD aged 18 years and over, had more than 500 participants per study arm and reported incidence of all-cause death and/or all-cause mortality/survival Kaplan–Meier (KM) curves. To estimate long-term survival, KM curves were extrapolated to 20 years by calculating standard mortality ratios (SMRs) using age- and sex-adjusted general-population lifetable data. Study and patient characteristics and mortality data from relevant studies were provided to clinical experts to inform their judgements in a formal elicitation process. After receiving training on the elicitation process, six leading disease area experts were invited to complete the elicitation survey using an Excel-based tool, which consisted of 10 calibration questions, and three questions regarding the survival of patients in the placebo arm of the DAPA-CKD study at 10 and 20 years. The elicited estimates will be weighted and aggregated using Cooke’s method. Results Literature searches identified 13 relevant articles (seven clinical trials and six observational studies), with a range of 1094 to 5674 participants. Mean age varied across studies (range: 55–70 years). Where reported, median follow-up was 9–144 months, and mean estimated glomerular filtration rate (eGFR) at baseline was 22.4–56.3 mL/min/1.73 m2. Five studies exclusively included patients with type 2 diabetes (T2D). The incidence of all-cause death was reported in nine studies and was 1.5–9.4 deaths per 100 patient-years, with the highest incidence observed in a study reporting data for patients with CKD stage 4 and 5 (8.0 and 9.4 deaths per 100 patient-years, respectively). Nine studies provided KM curves; from these, estimated survival at 2 years ranged from 86% (study population mean age 67 years, eGFR < 15 mL/min/1.73 m2) to 98% (study population mean age 58 years, mean eGFR 46.2 mL/min/1.73 m2). The SMR-extrapolated survival at 10 and 20 years was 36–80% and 2–69%, respectively. The ranges defined by the expert judgements collected to date for survival at 10 and 20 years are in line with the variability of the extrapolated KM survival curves. The elicitation process is ongoing and therefore, to avoid biasing the judgements that remain to be collected, preliminary results are not reported here. Results of the expert elicitation will be presented in full at the congress. Conclusion Initial results from the survey calibration questions suggest that the expert elicitation process provides expert judgements that are both informative and precise. The elicitation of survival estimates for patients with CKD and elevated albuminuria at 10 and 20 years will provide greater insight than extrapolated data alone, and will increase the validity of long-term survival projections for dapagliflozin cost-effectiveness analyses.

PMS19 Validation of a Cost-Effectiveness MODEL in Rheumatoid Arthritis

Rheumatoid arthritis (RA) has multiple previous health technology assessment (HTA) appraisals for advanced therapies. Cost-effectiveness models (CEMs) in RA are complex, especially with the increasing use of patient-level simulation, therefore, replicating them is an important step prior to HTA submission. Validation can ensure that models provide consistent representation of the disease and intervention, and a solid basis for decision-making. The objective of this study was to validate our patient-level CEM against a published model that was developed by academic experts in previous NICE appraisals. A patient-level, discrete-event simulation model was developed to evaluate the cost-effectiveness of new treatments for moderate-to-severe RA, following an approach developed by Stevenson et al.1 which estimated cost and health benefits using long-term, non-linear health-assessment questionnaire disability index trajectories. Our model was validated against a cost-effectiveness analysis of sequences of biologics (abatacept, adalimumab, certolizumab pegol, etanercept, golimumab, infliximab, tocilizumab) for RA patients in England published by Stevenson et al. in 2017.2 It was possible to replicate the analysis of Stevenson et al.2 2017 in most respects. Stevenson et al.2 reported lifetime incremental QALY gains of 1.5-2.0 and incremental costs of £60,000-£100,000 for the seven drugs. Our model predicted incremental QALYs of 1.54-1.71 and incremental costs of £69,000-£102,000. Estimated ICERs were £48,800-£52,300 and £44,700-£66,700 for Stevenson et al.2 and our model, respectively. The upper ICERs from our model were considered overestimates due to using list prices where patient access scheme prices were confidential. The results of the model validation demonstrate that it is possible to successfully replicate a complex, patient-level model. This illustrates the importance of validation when developing models for HTA to ensure consistency with previous assessments and provide a reliable basis for decision-making. REFERENCES 1. Stevenson M et al. Health Technology Assessment. 2016;20(35):1-610. 2. Stevenson M et al. J Rheumatol. 2017;44(7):973-80.

PCN136 ECONOMIC VIABILITY OF CHIMERIC ANTIGEN RECEPTOR T-CELL THERAPY FOR ADULTS WITH ADVANCED LEUKEMIA: EVIDENCE FROM AN EARLY ECONOMIC MODELLING

This study aims to: 1) estimate the minimum magnitude of improvement on patient outcomes needed and maximum reimbursable prices that will make CAR-T cell therapy cost-effective for adult patients with relapsed/refractory acute lymphoblastic leukemia (ALL) and; 2) identify parameters that future research on CAR-T therapy should focus on. We developed a probabilistic Markov model to predict the long-term health outcomes and costs of adult patients with relapsed/refractory ALL who receive each of the available treatment modalities including CAR-T therapy, blinatumomab, inotuzumab, bone marrow transplant, and salvage chemotherapy. We simulate the inclusion of CAR-T into each treatment modality using a wide range of potential treatment effects to determine what clinical effects and what size of effect would be necessary for CAR-T to be cost-effective. Sources of data necessary to populate this model include a combination of published systematic reviews and routinely-collected databases. Bone marrow transplant was associated with the largest improvement in life expectancy among adult patients with ALL, followed by CAR T-cell therapy blinatumomab, inotuzumab, and salvage chemotherapy. Maximum reimbursable price of CAR T-cell therapy depends on willingness to pay (WTP) values, treatment comparator, the sequence of treatments. If CAR T-cells are used to treat first relapse of ALL, at a WTP of $50,000 per QALY gained, the maximum reimbursable price of CAR T-cell therapy were $19,431, $125,686 and $132,130 when compared to salvage chemotherapy, blinatumomab and inotuzumab, respectively. However, if CAR T-cells are used as a subsequent relapse treatment, its maximum reimbursable prices decreased substantially. CAR T-cell therapy has no therapeutic headroom compared to bone marrow transplant. Maximum reimbursable price of CAR T-cell therapy depends on willingness to pay values, treatment comparator, and sequence of treatments. Early economic evaluation can determine which factors of treatment can impact the cost-effectiveness of new interventions.

The cost of blood: a study of the total cost of red blood cell transfusion in patients with β‐thalassemia using time‐driven activity‐based costing

To accurately quantify the costs of care for patients with transfusion-dependent thalassemia (TDT), and to evaluate cost-effectiveness of new treatments, data are required on costs of regular red blood cell (RBC) transfusions. However, no previous studies have evaluated the costs of RBC transfusion specifically in chronically transfused patients. We performed a time-driven activity-based costing (TDABC) study using a health care provider perspective. This was performed over a 1-month period, capturing every step of the transfusion pathway for patients with TDT at a designated provider of specialist thalassemia services in Australia. Detailed process maps were developed to outline treatments and processes directly related to transfusion. For each process map, detailed data collection, including timing of activities, was performed multiple times to account for variation in practice. Costs associated with RBC transfusion were broken down into fixed, process, and RBC procurement costs. The total per-unit cost was US$695.59 (95% confidence interval, US$694.45-US$696.73). Approximately 40% of cost was for procurement of the RBC unit, with process costs accounting for 55%. The single largest contributor to process costs was attributed to iron chelation medication (approximately 80%). In sensitivity analyses, seniority of staff, time to perform processes, and probabilities of different processes occurring did not substantially influence the RBC transfusion cost; however the number of RBC units per transfusion episode did impact the overall cost per RBC unit. We found significant costs associated with RBC transfusion for TDT, with the product cost contributing less than one-half of the total cost.

Integrating expert opinion with clinical trial data to extrapolate long-term survival: a case study of CAR-T therapy for children and young adults with relapsed or refractory acute lymphoblastic leukemia

BackgroundLong-term clinical outcomes are necessary to assess the cost-effectiveness of new treatments over a lifetime horizon. Without long-term clinical trial data, current practice to extrapolate survival beyond the trial period involves fitting alternative parametric models to the observed survival. Choosing the most appropriate model is based on how well each model fits to the observed data. Supplementing trial data with feedback from experts may improve the plausibility of survival extrapolations. We demonstrate the feasibility of formally integrating long-term survival estimates from experts with empirical clinical trial data to provide more credible extrapolated survival curves.MethodsThe case study involved relapsed or refractory B-cell pediatric and young adult acute lymphoblastic leukemia (r/r pALL) regarding long-term survival for tisagenlecleucel (chimeric antigen receptor T-cell [CAR-T]) with evidence from the phase II ELIANA trial. Seven pediatric oncologists and hematologists experienced with CAR-T therapies were recruited. Relevant evidence regarding r/r pALL and tisagenlecleucel provided a common basis for expert judgments. Survival rates and related uncertainty at 2, 3, 4, and 5 years were elicited from experts using a web-based application adapted from Sheffield Elicitation Framework. Estimates from each expert were combined with observed data using time-to-event parametric models that accounted for experts’ uncertainty, producing an overall distribution of survival over time. These results were validated based on longer term follow-up (median duration 24.2 months) from ELIANA following the elicitation.ResultsExtrapolated survival curves based on ELIANA trial without expert information were highly uncertain, differing substantially depending on the model choice. Survival estimates between 2 to 5 years from individual experts varied with a fair amount of uncertainty. However, incorporating expert estimates improved the precision in the extrapolated survival curves. Predictions from a Gompertz model, which experts believed was most appropriate, suggested that more than half of the ELIANA patients treated with tisagenlecleucel will survive up to 5 years. Expert estimates at 24 months were validated by longer follow-up.ConclusionsThis study provides an example of how expert opinion can be elicited and synthesized with observed survival data using a transparent and formal procedure, capturing expert uncertainty, and ensuring projected long-term survival is clinically plausible.

Cost-Effectiveness of Sacubitril/Valsartan in Germany: An Application of the Efficiency Frontier

BackgroundTo assess the cost-effectiveness of new treatments in Germany, the efficiency frontier (EF) method has been developed. We compared the cost-effectiveness analysis using international standards and the German methodology, using the heart failure drug sacubitril/valsartan as an example. MethodsA previously developed Markov model was adapted to include 4 treatment options: no treatment, enalapril, candesartan, and sacubitril/valsartan. The internationally used incremental cost-effectiveness ratio (ICER) was calculated, as well as cost-effectiveness acceptability curves. Additionally, EFs, net monetary benefits (NMBs), and price-acceptability curves were created according to German guidelines. All analyses were performed from the perspective of the German Statutory Health Insurance. ResultsThe base-case ICER for sacubitril/valsartan compared to enalapril is €19 300/quality-adjusted life-year. On the cost-effectiveness acceptability curve, sacubitril/valsartan is most likely to be cost-effective, out of all included comparators, from a hypothetical willingness-to-pay threshold of €18 250/quality-adjusted life-year onward. No EF could be constructed for the base case. Taking the uncertainty of the input parameters into account for the probabilistic sensitivity analysis, a NMB of around −€14 000 was calculated, depending on the outcome considered, with the NMB being zero at a daily price for sacubitril/valsartan ranging from €1.52 to €1.67. ConclusionWe calculated an ICER for Germany, comparable to previously published cost-effectiveness analyses for Europe, which widely concluded sacubitril/valsartan to be cost-effective. Using the German EF approach, a considerable discount needs to be applied before sacubitril/valsartan can be considered cost-effective.

Abstract LB-160: Glioblastoma and mesothelioma: Do estimates of health state utilities compare in rare cancers treatable with tumor treating fields

Abstract Objective: To compare the estimated health state utilities of glioblastoma (GBM) and malignant pleural mesothelioma (MPM) given that they are both rare cancers treatable with tumor treating fields (TTFields) Background: Glioblastoma (GBM) is the most aggressive form of primary brain cancer in adults. MPMis an invasive and generally fatal malignancy of the lung mainly caused by exposure to asbestos fibers. To understand the comparative economic value of new treatments, the quality-adjusted life year (QALY) has been developed and is widely used in health economic literature. Given that GBM and MPM are both rare and aggressive cancers, both potentially treatable with TTFields, we aimed to understand whether the estimated health state utilities for each disease state were comparable. Clinical results show a comparable effect on median overall survival (OS). The EF-14 Trial showed that the addition of TTFields treatment increases median OS by 4.9 months in GBM, while the EF-23 showed that TTFields adds a median OS of 6.1 months compared to historical control in MPM. Differing health state utilities by disease, in this case a central nervous system tumor versus a pleural tumor, could influence the adoption of a new treatment regardless of whether the efficacy and safety of that new treatment is comparable for both disease states. Methods: We reviewed the structure and results of the EF-14 and STELLAR trials. We determined the appropriate health states for evaluation in both diseases as stable disease, progressed disease, and death. We then performed a comprehensive review of the published literature regarding health utility values in GBM and MPM patients using a boolean search in the Medline database. The estimated health state utilities were then compared by disease. Results: All publications that reference health utilities for GBM are derived from the same source. Estimates of utility were obtained from the NHS Value of Health Panel (VoHP) and based on the standard gamble method for preference elicitation, rating a total of nine descriptive health state scenarios. Utilities for stable disease in glioblastoma were 0.85 and 0.73 for progressed disease as a base case respectively. The estimates for MPM utilities were obtained using varying methods not correlated to stable or progressive disease. One method elicited utilities describing 243 distinct states from the EQ-5 questionnaire data collected during the trial at an indiviual patient level. Conclusions: Health utility estimates published so far for GBM are not comparable to the helth utilities published and used for MPM. While the utilities in GBM are scarce, but allow for use in a three health state disease model, utilities for MPM are more diverse and do at the moment not support a health state model. MPM utilities elicited from the EQ-5 however describe more adequately the individual utilities and their change during the course of the disease. The lack of such detailed utilities e.g. in GBM could lead to disparities in the assessment of cost effectiveness of new treatments like tumor treating fields for different indications, despite similar costs and clinical efficacy. Citation Format: Christina Proescholdt, Justin Kelly. Glioblastoma and mesothelioma: Do estimates of health state utilities compare in rare cancers treatable with tumor treating fields [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-160.

PND13 ESTIMATING STATE TRANSITIONS IN ALZHEIMER'S DISEASE BY AGE AND GENDER: ANALYSIS FROM A LONGITUDINAL COHORT

Age is the most critical risk factor for Alzheimers Disease (AD). Females are more likely to be diagnosed with the disease than males. It is unclear if this difference in risk translates to differences in disease progression. This study aimed to estimate the probabilities of transition between stages of severity (mild, moderate, severe, and death) in AD. We sampled 9,365 patients aged 65 years or older with a diagnosis of AD from the National Alzheimer’s Coordinating Center (ACC) longitudinal database. All selected patients had more than one annual visits to the Alzheimer’s Disease Centers between 2005-2015. We developed a multistate model using msm package in R to calculate annual transition probabilities by age group and gender. Age groups examined were: 65-74, 75-84 and 85 or older years. The annual probabilities of death in patients aged 65-74 years with mild, moderate, and severe AD were higher in males (0.8%, 10.0%, and 31.8%) compared with females (0.4%, 4.2%, and 20.4%). The trend was similar in patients 75-84 years of age with mild, moderate and severe AD (Males: 1.7%, 12.2%, and 31.9%; Females: 0.9%, 6.0%, and 20.7%) and those aged 85 years or older (Males: 5.5%, 15.7%, and 39.6%; Females: 4.2%, 13.5%, and 28.4%). The annual probabilities of moving to severe AD in male patients aged 65-74 years with mild or moderate AD were 2.2% and 29.6%, respectively. In females, they were 1.9% and 29.4%. Results for patients aged 75-84 and 85 years or older followed a trend similar to that seen in patients aged 65-74 years. Transitions for AD across stages of severity were slightly higher in males than females and older patients than younger ones. These probabilities can be used to evaluate age and gender-specific cost-effectiveness of new treatments in state-transition models.

PMH74 - QUALITY OF LIFE ACCORDING TO TEMPORAL ILLICIT OPIOID USE AND RESPONDER RATE IN ADULTS RECEIVING BUPRENORPHINE DEPOT INJECTIONS (CAM2038) TO TREAT OPIOID USE DISORDER IN A 48-WEEK OPEN LABEL STUDY

Investigational weekly and monthly buprenorphine depot (CAM2038) for treatment of opioid use disorder (OUD) offers a novel approach to patient management. The potential of CAM2038 to improve health related quality of life (hrQoL) and Quality Adjusted Life Years (QALYs) are important criteria for decision makers to consider in evaluating cost effectiveness of new treatments in OUD and is assessed in this research. The long-term safety of investigational CAM2038 was evaluated in a Phase 3, 48-week, open-label study in patients with moderate to severe OUD. A post hoc responder rate was defined according to the proportion of urine toxicology tests testing negative for illicit opioids including self-reported (SR) illicit opioid use, evaluated at quartiles(q1-4). HrQoL was evaluated with the EQ-5D-5L in order to derive QALYs for each responder rate. Trends in EQ-5D-5L scores were evaluated descriptively with respect to responder rates and temporal abuse. A total of 188 subjects (out of 228 enrolled, 82%) with at least a single EQ-5D-5L survey were included for analysis. The surveys were collected from week 20 to week 48 with an average of 6.7 surveys per participant. EQ-5D-5L index scores were positively correlated to clean urine toxicology test/SR with an average of 0.05 +/- 0.01 points higher hrQoL score for clean urine toxicology tests/SR (p = 0.01). The subjects were split in responder groups defined by response of q1 (n = 26, 14%), q2 (n = 21, 11%), q3 (n = 18, 10%), and q4 (n = 123, 65%). The responder groups EQ-5D-5L averages were 0.88, 0.86, 0.90, and 0.92 respectively and statistically different (p < 0.001). Although careful interpretation is warranted due to trial design, this analysis demonstrated hrQoL and QALY improvement related to both individual temporal illicit opioid use as well as overall groups of responders.