Cost-Effectiveness Analysis of MammaPrint® to Guide the Use of Endocrine Therapy in Patients with Early-Stage Breast Cancer.

BACKGROUND: Gene expression profiling tests can predict the risk of disease recurrence and select patients who are expected to benefit from therapy, while allowing other patients to forgo therapy. For breast cancers, these tests were initially designed to tailor chemotherapy decisions, but recent evidence suggests that they may also guide the use of endocrine therapy. This study evaluated the cost effectiveness of a prognostic test, MammaPrint®, to guide the use of adjuvant endocrine therapy in patients eligible according to Dutch treatment guidelines. METHODS: We constructed a Markov decision model to calculate the lifetime costs (in 2020 Euros) and effects (survival and quality-adjusted life-years) of MammaPrint® testing versus usual care (endocrine therapy for all patients) in a simulated cohort of patients. The population of interest includes patients for whom MammaPrint® testing is currently not indicated, but for whom it may be possible to safely omit endocrine therapy. We applied both a health care perspective and a societal perspective and discounted costs (4%) and effects (1.5%). Model inputs were obtained from published research (including randomized controlled trials), nationwide cancer registry data, cohort data and publicly available data sources. Scenario and sensitivity analyses were conducted to explore the impact of uncertainty around input parameters. Additionally, threshold analyses were performed to identify under which circumstances MammaPrint® testing would be cost effective. RESULTS: Adjuvant endocrine therapy guided by MammaPrint® resulted in fewer side effects, more (quality-adjusted) life-years (0.10 and 0.07 incremental QALYS and LYs, respectively) and higher costs (€18,323 incremental costs) compared with the usual care strategy in which all patients receive endocrine therapy. While costs for hospital visits, medication costs and productivity costs were somewhat higher in the usual care strategy, these did not outweigh costs of testing in the MammaPrint® strategy. The incremental cost-effectiveness ratio was €185,644 per QALY gained from a healthcare perspective and €180,617 from a societal perspective. Sensitivity and scenario analyses showed that the conclusions remained the same under changed input parameters and assumptions. Our results show that MammaPrint® can become a cost-effective strategy when either the price of the test is reduced (> 50%), or the proportion of patients for which treatment is altered (i.e. those with ultra-low risk) increases to > 26%. CONCLUSION: Standard MammaPrint® testing to guide the use of endocrine therapy in our simulated patient population appears not to be a cost-effective strategy compared with usual care. The cost effectiveness of the test can be improved by reducing the price or preselecting a population more likely to benefit from the test.

Towards sustainability and affordability of expensive cell and gene therapies? Applying a cost-based pricing model to estimate prices for Libmeldy and Zolgensma.

BACKGROUND AIMS: Drug prices are regarded as one of the most influential factors in determining accessibility and affordability to novel therapies. Cell and gene therapies such as OTL-200 (brand name: Libmeldy) and AVXS-101 (brand name: Zolgensma) with (expected) list prices of 3.0 million EUR and 1.9 million EUR per treatment, respectively, spark a global debate on the affordability of such therapies. The aim of this study was to use a recently published cost-based pricing model to calculate prices for cell and gene therapies, with OTL-200 and AVXS-101 as case study examples. METHODS: Using the pricing model proposed by Uyl-de Groot and Löwenberg, we estimated a price for both therapies. We searched the literature and online public sources to estimate (i) research and development (R&D) expenses adjusted for risk of failure and cost of capital, (ii) the eligible patient population and (iii) costs of drug manufacturing to calculate a base-case price for OTL-200 and AVXS-101. All model input parameters were varied in a stepwise, deterministic sensitivity analysis and scenario analyses to assess their impact on the calculated prices. RESULTS: Prices for OTL-200 and AVXS-101 were estimated at 1 048 138 EUR and 380 444 EUR per treatment, respectively. In deterministic sensitivity analyses, varying R&D estimates had the greatest impact on the price for OTL-200, whereas for AVXS-101, changes in the profit margin changed the calculated price substantially. Highest prices in scenario analyses were achieved when assuming the lowest number of patients for OTL-200 and highest R&D expenses for AVXS-101. The lowest R&D expenses scenario resulted in lowest prices for either therapy. CONCLUSIONS: Our results show that, using the proposed model, prices for both OTL-200 and AVXS-101 lie substantially below the currently (proposed) list prices for both therapies. Nevertheless, the uncertainty of the used model input parameters is considerable, which translates in a wide range of estimated prices. This is mainly because of a lack of transparency from pharmaceutical companies regarding R&D expenses and the costs of drug manufacturing. Simultaneously, the disease indications for both therapies remain heavily understudied in terms of their epidemiological profile. Despite the considerable variation in the estimated prices, our results may support the public debate on value-based and cost-based pricing models, and on "fair" drug prices in general.

Multinomial network meta-analysis using response rates: relapsed/refractory multiple myeloma treatment rankings differ depending on the choice of outcome.

BACKGROUND: Due to the fast growing relapsed/refractory multiple myeloma (RRMM) treatment landscape, a comparison of all the available treatments was warranted. For clinical practice it is important to consider both immediate effects such as response quality and prolonged benefits such as progression-free survival (PFS) in a meta-analysis. The objective of this study was to assess the impact of the choice of outcome on the treatment rankings in RRMM. METHODS: A multinomial logistic network meta-analysis was conducted to estimate the ranking of sixteen treatments based on both complete and objective response rates (CRR and ORR). Seventeen phase III randomized controlled trials from a previously performed systematic literature review were included. Treatment ranking was based on the surface under the cumulative ranking curve (SUCRA). Sensitivity analysis was conducted. RESULTS: The ranking of treatments differed when comparing PFS hazard ratios rankings with rankings based on CRR. Pomalidomide, bortezomib and dexamethasone ranked highest, while a substantial lower ranking was observed for the triplet elotuzumab, lenalidomide, dexamethasone. The ranking of treatments did not differ when comparing PFS hazard ratios and ORR. The scenario analyses showed that the results were robust. In all scenarios the top three was dominated by the same triplets. The treatment with the highest probability of having the best PFS and ORR was the triplet daratumumab, lenalidomide plus dexamethasone in the base case. CONCLUSION: This analysis shows that depending on the chosen outcome treatment rankings in RRMM may differ. When conducting NMAs, the response rate, a clinically recognized outcome, should therefore be more frequently considered.

Being Transparent About Brilliant Failures: An Attempt to Use Real-World Data in a Disease Model for Patients with Castration-Resistant Prostate Cancer.

BACKGROUND: Real-world disease models spanning multiple treatment lines can provide insight into the (cost) effectiveness of treatment sequences in clinical practice. OBJECTIVE: Our objective was to explore whether a disease model based solely on real-world data (RWD) could be used to estimate the effectiveness of treatments for patients with castration-resistant prostate cancer (CRPC) that could then be suitably used in a cost-effectiveness analysis. METHODS: We developed a patient-level simulation model using patient-level data from the Dutch CAPRI registry as input parameters. Time to event (TTE) and overall survival (OS) were estimated with multivariate regression models, and type of event (i.e., next treatment or death) was estimated with multivariate logistic regression models. To test internal validity, TTE and OS from the simulation model were compared with the observed outcomes in the registry. RESULTS: Although patient characteristics and survival outcomes of the simulated data were comparable to those in the observed data (median OS 20.6 vs. 19.8 months, respectively), the disease model was less accurate in estimating differences between treatments (median OS simulated vs. observed population: 18.6 vs. 17.9 [abiraterone acetate plus prednisone], 24.0 vs. 25.0 [enzalutamide], 20.2 vs. 18.7 [docetaxel], and 20.0 vs. 23.8 months [radium-223]). CONCLUSIONS: Overall, the disease model accurately approximated the observed data in the total CRPC population. However, the disease model was unable to predict differences in survival between treatments due to unobserved differences. Therefore, the model is not suitable for cost-effectiveness analysis of CRPC treatment. Using a combination of RWD and data from randomised controlled trials to estimate treatment effectiveness may improve the model.

High-Intensity Care in the End-of-Life Phase of Castration-Resistant Prostate Cancer Patients: Results from the Dutch CAPRI-Registry.

Background: Intensive end-of-life care (i.e., the overuse of treatments and hospital resources in the last months of life), is undesirable since it has a minimal clinical benefit with a substantial financial burden. The aim was to investigate the care in the last three months of life (end-of-life [EOL]) in castration-resistant prostate cancer (CRPC). Methods: Castration-resistant prostate cancer registry (CAPRI) is an investigator-initiated, observational multicenter cohort study in 20 hospitals retrospectively including patients diagnosed with CRPC between 2010 and 2016. High-intensity care was defined as the initiation of life-prolonging drugs (LPDs) in the last month, continuation of LPD in last 14 days, >1 admission, admission duration ≥14 days, and/or intensive care admission in last three months of life. Descriptive and binary logistic regression analyses were performed. Results: High-intensity care was experienced by 41% of 2429 patients in the EOL period. Multivariable analysis showed that age (odds ratio [OR] 0.98, 95% confidence interval [CI] 0.97-0.99), performance status (OR 0.57, 95% CI 0.33-0.97), time from CRPC to EOL (OR 0.98, 95% CI 0.97-0.98), referral to a medical oncologist (OR 1.99, 95% CI 1.55-2.55), prior LPD treatment (>1 line OR 1.72, 95% CI 1.31-2.28), and opioid use (OR 1.45, 95% CI 1.08-1.95) were significantly associated with high-intensity care. Conclusions: High-intensity care in EOL is not easily justifiable due to high economic cost and little effect on life span, but further research is awaited to give insight in the effect on patients' and their caregivers' quality of life.

Cost of health care for paediatric patients with sickle cell disease: An analysis of resource use and costs in a European country.

BACKGROUND: While multiple studies have examined the cost of health care for one aspect of sickle cell disease care, few have focussed on the overall cost of comprehensive care for sickle cell disease. METHODS: We conducted a retrospective cohort study of children with sickle cell disease treated in a comprehensive care centre from 1 January 2015 to 31 December 2016. Health care utilisation of included patients was based upon data from two main sources. The clinical practice guideline was used to determine the expected resource use of routine comprehensive care (planned elective care), and the financial claims database was used to estimate real-world resource use associated with acute and inpatient care (additional care). RESULTS: A total of 125 children with sickle cell disease were analysed. Expenditures for these patients averaged €5049 [standard deviation (SD) €1634] per child per year. Total yearly costs per patient varied considerably, ranging from €669 to €84 010, and less than 15% of patients were responsible for 50% of the health care costs. The majority (37%) of costs was associated with inpatient hospital care, which increased by age group, 27% with diagnostics, 19% with treatment, 11% with outpatients' visits and 6% with emergency care. CONCLUSION: We have described real-world resource use and expenditures for children with sickle cell disease in a European comprehensive care centre. It seems that costs of a comprehensive approach with effective management in the outpatient setting is favourable when compared to episodic health care.

A clinician's guide for developing a prediction model: a case study using real-world data of patients with castration-resistant prostate cancer.

PURPOSE: With the increasing interest in treatment decision-making based on risk prediction models, it is essential for clinicians to understand the steps in developing and interpreting such models. METHODS: A retrospective registry of 20 Dutch hospitals with data on patients treated for castration-resistant prostate cancer was used to guide clinicians through the steps of developing a prediction model. The model of choice was the Cox proportional hazard model. RESULTS: Using the exemplary dataset several essential steps in prediction modelling are discussed including: coding of predictors, missing values, interaction, model specification and performance. An advanced method for appropriate selection of main effects, e.g. Least Absolute Shrinkage and Selection Operator (LASSO) regression, is described. Furthermore, the assumptions of Cox proportional hazard model are discussed, and how to handle violations of the proportional hazard assumption using time-varying coefficients. CONCLUSION: This study provides a comprehensive detailed guide to bridge the gap between the statistician and clinician, based on a large dataset of real-world patients treated for castration-resistant prostate cancer.