The cost of expensive breast cancer drugs

Background Increasing healthcare costs are a major challenge in medical oncology, since the total costs of oncology can account for up to 30% of the total hospital expenditures. As many novel (expensive) cancer treatments are being developed, it is important to be transparent about drug prices from an early research stage on. To assess the potential financial impact of pipeline drugs, their expected future prices can be deducted from prices of currently used drugs. As an overview of the standard prices of expensive breast cancer treatments in European countries is lacking, this review aimed to synthesize all evidence on costs of approved, expensive breast cancer drugs in the Netherlands. Methods A literature review was performed to create an overview of all approved, expensive drugs in the Netherlands. Standard drug costs were retrieved via the Dutch administrative health authority (ZINL). Drugs were considered expensive if the standard price of the drug was more than €10 per unit or if the cost of a treatment with that particular drug exceeded €1000 on average per patient. Results In the Netherlands 25 breast cancer drugs are approved with a standard price of more than €10 per unit. After excluding drugs with expected treatment costs less than €1000, 19 drugs were included in the analysis. The standard drug price is €7,943 on average (range €63 - €45,452), and the average number of cycles per patient is 10.5 (range 4 - 25.3 cycles). This results in average treatment costs per patient of expensive drugs of €17,968 (range €1,103 - €87,123). Four drugs that initially ranked low based on standard drug unit prices (rank 10-19), rank substantially higher (rank 1-10) when ranking total treatment costs. Conclusions Ranking standard drug prices per unit may not be very informative. It would be valuable to rank drug treatment costs, based on treatment length and dosage estimates. However, in the Netherlands the expected treatment length for a particular drug is not standardly reported in official approval reports. Furthermore, actual prices of expensive drugs may differ from standard drug prices, by which treatment costs might be deviant. Extending standardization of reporting and calculation of drug treatment costs would be valuable and particularly relevant when extending this type of cost calculations to other countries

Estimating The Drug Treatment Cost of Breast Cancer

Objectives Overall treatment costs in oncology are increasing rapidly due to the increasing availability of expensive drugs. Comparing the costs of currently used drugs and assessing the cost-effectiveness of new drugs requires a transparent overview of actual breast cancer treatment prices. As such an overview is lacking, this study aims to synthesize evidence on the reimbursement and costs to estimate the total treatment cost of expensive breast cancer drugs for the Netherlands. Methods Evidence on the approval, reimbursement and list prices of expensive breast cancer drugs was identified from the Dutch Administrative Health Authority (ZINL). Data on the average length of treatment and dosing schedules was obtained from European Parliament Assessment Reports (EPARs) or ZINL reports. All evidence was aggregated in the estimation of actual treatment cost. Results In the Netherlands, 31 breast cancer drugs are approved (available in 41 different forms). Based on drug list prices Pertuzumab, Trastuzumab Emtansine and Trastuzumab are the most expensive drugs. For 17/41 (41.5%), no evidence on the average treatment length was available in EPARs or ZINL reports. Comparing list prices to the estimated treatment cost per patient resulted in substantial differences in the ranking of expensiveness of the drugs. Overall, estimated treatment costs were highest for Bevacizumab, Pertuzumab and Trastuzumab Emtansine. Conclusions Estimating treatment costs is far from trivial, given the wide range of evidence sources that need to be synthesized. This complicates rapid and transparent assessment of actual cancer drug treatment cost, which is necessary to focus strategies aiming to limit the increasing healthcare costs. Differences exist in list prices within countries and between countries, thereby influencing the corresponding estimated treatment costs and resulting in list prices having limited value in this context. Therefore, extending standardization in presenting information on costs per cancer drug and implementing real world price estimates in such calculations is highly recommended

WHAT DO YOU BELIEVE? AN ONLINE TOOL FOR COST-EFFECTIVENESS ANALYSIS OF CIRCULATING TUMOR CELL DETECTION BASED ON USER BELIEFS

Purpose: Early HTA of new health technology is typically performed during technology development, when evidence on technology characteristics and performance is still very limited. Consequently, assumptions need to be made to evaluate the potential impact of the technology, which may not be acceptable, or supported, by all stakeholders. For example, we previously assessed the cost-effectiveness of circulating tumor cell (CTC) detection to guide systemic therapy in early breast cancer, compared to usual care, in a model based analysis. However, the lack of evidence required multiple assumptions on input parameters for this model. In this study we developed an online tool for clinicians and decision makers, allowing them to using their own beliefs and expertise as input for the cost-effectiveness analysis. Methods: The online tool was based on the previous model based cost-effectiveness analysis and was built in R using the Shiny package. The mean estimated value and a range of uncertainty of several input parameters that have substantial influence on the outcome can be adjusted in the tool. Parameters classified as substantially influencing parameters are for example sensitivity, specificity and costs of CTC detection. Outcomes calculated by the tool are the base-case results (including the ICER) and an ICER plot of the probabilistic sensitivity analysis. Results: The new tool allows easy and rapid reevaluation of the cost-effectiveness of CTCs compared to usual care under the user’s own beliefs. We found that different beliefs used as input for the analysis had substantial effect on the expected impact of CTC detection in terms of health benefits, cost savings and cost-effectiveness. However, as common in model based cost-effectiveness analysis, not all input parameters had substantial influence on the outcomes. Conclusion: When evidence on new health technology is still limited, the outcomes of a cost-effectiveness analysis can vary widely with the assumptions and beliefs used as input for the model. Therefore, it may be valuable to have decision makers perform their ‘own’ analysis, that is, (re)calculating the cost-effectiveness under their own beliefs. Our tool enables such an approach and can facilitate discussion on different beliefs and their plausibility, recognizing that beliefs on some parameters affect cost-effectiveness more than beliefs on others. Research to further tailor the online tool to decision maker’s requirements is ongoing

The Impact Of Cluster Selection Methods In Two-Stage Bootstrapping To Assess Uncertainty In Health Economic Outcomes In Cluster Randomized Controlled Trials

OBJECTIVES : Bootstrapping is often used to assess uncertainty in outcomes of randomized controlled trials (RCTs) due to sampling variation and limited sample sizes. Although guidance is available on two-stage bootstrapping for cluster-RCTs, specific guidance is lacking on sampling clusters within bootstrap samples to address the uncertainty in variation across clusters. This study assesses the impact of using different selection approaches to sample clusters in two-stage bootstrapping in a case study on procalcitonin-based antibiotic treatment in IC patients with sepsis. METHODS : The case study was a cluster-RCT including 16 hospitals (4 academic, 12 non-academic) with on average 48 patients per hospital (range n: 1-185). Five cluster sampling approaches were investigated, based on random sampling of: 1) the intended number of patients, 2) 16 hospitals, 3) 16 hospitals maintaining the original ratio academic/non-academic hospitals, 4) as method 2 while maintaining the total number of patients, 5) as method 3 while maintaining the total number of patients. Additionally, a scenario analysis using half of the data was performed. Incremental cost differences and corresponding 95%CIs were determined based on 10,000 bootstrap samples. RESULTS : Different approaches of bootstrapping resulted in variation in the incremental costs per patient (data mean: €16, bootstrap range: €-24 - €183), with approach 5 deviating most from the observed mean incremental cost. 95%CIs also varied in size (smallest 95%CI: €-5,123 - €5,986 [method 5], largest 95%CI: €-5,699 - €6,566 [method 2]). Differences in outcomes were more pronounced when using half of the data. CONCLUSIONS : Using different approaches for sampling clusters in two-stage bootstrapping may influence the mean outcomes and 95%CIs. Determining the most appropriate sampling method based on outcomes and 95%CIs is dependent on the approach for selection used in the real-world trial. When the inclusion strategy is unknown, sensitivity analysis is recommended to assess uncertainty arising from this unknown cluster inclusion process

EpCAMhigh and EpCAMlow circulating tumour cells in metastatic prostate and breast cancer patients

The presence of high expressing epithelial cell adhesion molecule (EpCAMhigh) circulating tumor cells (CTC) enumerated by CellSearch® in blood of cancer patients is strongly associated with poor prognosis. This raises the question about the presence and relation with clinical outcome of low EpCAM expressing CTC (EpCAMlow CTC). In the EU-FP7 CTC-Trap program, we investigated the presence of EpCAMhigh and EpCAMlow CTC using CellSearch, followed by microfiltration of the EpCAMhigh CTC depleted blood. Blood samples of 108 castration-resistant prostate cancer patients and 22 metastatic breast cancer patients were processed at six participating sites, using protocols and tools developed in the CTC-Trap program. Of the prostate cancer patients, 53% had ≥5 EpCAMhigh CTC and 28% had ≥5 EpCAMlow CTC. For breast cancer patients, 32% had ≥5 EpCAMhigh CTC and 36% had ≥5 EpCAMlow CTC. 70% of prostate cancer patients and 64% of breast cancer patients had in total ≥5 EpCAMhigh and/or EpCAMlow CTC, increasing the number of patients in whom CTC are detected. Castration-resistant prostate cancer patients with ≥5 EpCAMhigh CTC had shorter overall survival versus those with <5 EpCAMhigh CTC (p = 0.000). However, presence of EpCAMlow CTC had no relation with overall survival. This emphasizes the importance to demonstrate the relation with clinical outcome when presence of CTC identified with different technologies are reported, as different CTC subpopulations can have different relations with clinical outcome

EpCAMhigh and EpCAMlow circulating tumor cells in metastatic prostate and breast cancer patients

The presence of high expressing epithelial cell adhesion molecule (EpCAMhigh) circulating tumor cells (CTC) enumerated by CellSearch® in blood of cancer patients is strongly associated with poor prognosis. This raises the question about the presence and relation with clinical outcome of low EpCAM expressing CTC (EpCAMlow CTC). In the EU-FP7 CTC-Trap program, we investigated the presence of EpCAMhigh and EpCAMlow CTC using CellSearch, followed by microfiltration of the EpCAMhigh CTC depleted blood. Blood samples of 108 castration-resistant prostate cancer patients and 22 metastatic breast cancer patients were processed at six participating sites, using protocols and tools developed in the CTC-Trap program. Of the prostate cancer patients, 53% had ≥5 EpCAMhigh CTC and 28% had ≥5 EpCAMlow CTC. For breast cancer patients, 32% had ≥5 EpCAMhigh CTC and 36% had ≥5 EpCAMlow CTC. 70% of prostate cancer patients and 64% of breast cancer patients had in total ≥5 EpCAMhigh and/or EpCAMlow CTC, increasing the number of patients in whom CTC are detected. Castration-resistant prostate cancer patients with ≥5 EpCAMhigh CTC had shorter overall survival versus those with <5 EpCAMhigh CTC (p = 0.000). However, presence of EpCAMlow CTC had no relation with overall survival. This emphasizes the importance to demonstrate the relation with clinical outcome when presence of CTC identified with different technologies are reported, as different CTC subpopulations can have different relations with clinical outcome