Early health economic analysis of 1.5 T MRI-guided radiotherapy for localized prostate cancer: Decision analytic modelling

Background and purpose 1.5 Tesla magnetic resonance imaging radiotherapy linear accelerator (MR-Linac) is gaining interest for treatment of localized prostate cancer. Clinical evidence is lacking and it therefore remains uncertain whether MR-Linac is cost-effective. An early health economic analysis was performed to calculate the necessary relative reduction in complications and the maximum price of MR-Linac (5 fractions) to be cost-effective compared to 5, 20 and 39 fractionation schedules of external beam radiotherapy (EBRT) and low-dose-rate (LDR) brachytherapy. Materials and methods A state transition model was developed for men with localized prostate cancer. Complication rates such as grade ≥2 urinary, grade ≥2 bowel and sexual complications, and utilities were based on systematic literature searches. Costs were estimated from a Dutch healthcare perspective. Threshold analyses were performed to identify the thresholds of complications and costs for MR-Linac to be cost-effective, while holding other outcomes such as biochemical progression and mortality constant. One-way sensitivity analyses were performed to outline uncertainty outcomes. Results At €6460 per patient, no reductions in complications were needed to consider MR-Linac cost-effective compared to EBRT 20 and 39 fractions. Compared to EBRT 5 fractions and LDR brachytherapy, MR-Linac was found to be cost-effective when complications are relatively reduced by 54% and 66% respectively. Results are highly sensitive to the utilities of urinary, bowel and sexual complications and the probability of biochemical progression. Conclusions MR-Linac is found to be cost-effective compared to 20 and 39 fractions EBRT at baseline. For MR-Linac to become cost-effective over 5 fractions EBRT and LDR brachytherapy, it has to reduce complications substantially or be offered at lower costs

What does cell therapy manufacturing cost? A framework and methodology to facilitate academic and other small-scale cell therapy manufacturing costings

Background aims: Recent technical and clinical advances with cell-based therapies (CBTs) hold great promise in the treatment of patients with rare diseases and those with high unmet medical need. Currently the majority of CBTs are developed and manufactured in specialized academic facilities. Due to small scale, unique characteristics and specific supply chain, CBT manufacturing is considered costly compared to more conventional medicinal products. As a result, biomedical researchers and clinicians are increasingly faced with cost considerations in CBT development. The objective of this research was to develop a costing framework and methodology for academic and other small-scale facilities that manufacture cell-based therapies. Methods: We conducted an international multi-center costing study in four facilities in Europe using eight CBTs as case studies. This study includes costs from cell or tissue procurement to release of final product for clinical use. First, via interviews with research scientists, clinicians, biomedical scientists, pharmacists and technicians, we designed a high-level costing framework. Next, we developed a more detailed uniform methodology to allocate cost items. Costs were divided into steps (tissue procurement, manufacturing and fill-finish). The steps were each subdivided into cost categories (materials, equipment, personnel and facility), and each category was broken down into facility running (fixed) costs and operational (variable) costs. The methodology was tested via the case studies and validated in developer interviews. Costs are expressed in 2018 euros (€). Results: The framework and methodology were applicable across facilities and proved sensitive to differences in product and facility characteristics. Case study cost estimates ranged between €23 033 and €190 799 Euros per batch, with batch yield varying between 1 and 88 doses. The cost estimations revealed hidden costs to developers and provided insights into cost drivers to help design manufacturing best practices. Conclusions: This framework and methodology provide step-by-step guidance to estimate manufacturing costs specifically for cell-based therapies manufactured in academic and other small-scale enterprises. The framework and methodology can be used to inform and plan cost-conscious strategies for CBTs

PRM121 Assessment of Validation of Health-Economics Decision Models In Intervention Studies of Seasonal Influenza and Breast Cancer

Objectives: We aimed to review recently published health-economic (HE) decision models to assess the reporting of validation efforts. An infectious disease (seasonal influenza, SI) and a chronic disease (breast cancer, BC) were used as examples, giving a preliminary insight in the reporting of validation efforts in the overall HE literature. Methods: A literature search was performed in Pubmed and Embase to retrieve full-text HE modeling studies, published between 2008 and 2014. Type of evaluation, model and intervention were extracted, as well as information on model outcomes, journal and funding. Reporting on model validation was evaluated by checking for the presence of the word validation and its conjugates, and by using AdViSHE, a tool which contains a structured list of relevant items for validation. Results: The literature search resulted in 53 SI and 45 BC studies. In 41 studies (42%) the word validation or its conjugates was mentioned, but only in a small percentage in the context of model validation. The terminology used around validation was found to be ambiguous. Model validation efforts were reported in a minority of studies. However, some studies do show good reporting examples. Cross validation of study outcomes was reported most often, but the quantity and quality of this reporting varied. More validation efforts were reported in BC than in SI. Conclusions: Only a limited number of studies reported on model validation efforts, although it may be assumed that more efforts have been taken than were reported. In particular, the differences between SI and BC may not mean that less efforts were undertaken to validate SI models. Although validation is deemed important by many researchers, this is not reflected in the reporting habits of HE modeling studies. Better reporting of validation efforts would be desirable to further enhance decision-makers' confidence in HE models and their outcomes

Assessment of Validation of Health-Economics Decision Models In Intervention Studies of Seasonal Influenza and Breast Cancer

Objectives: We aimed to review recently published health-economic (HE) decision models to assess the reporting of validation efforts. An infectious disease (seasonal influenza, SI) and a chronic disease (breast cancer, BC) were used as examples, giving a preliminary insight in the reporting of validation efforts in the overall HE literature. Methods: A literature search was performed in Pubmed and Embase to retrieve full-text HE modeling studies, published between 2008 and 2014. Type of evaluation, model and intervention were extracted, as well as information on model outcomes, journal and funding. Reporting on model validation was evaluated by checking for the presence of the word validation and its conjugates, and by using AdViSHE, a tool which contains a structured list of relevant items for validation. Results: The literature search resulted in 53 SI and 45 BC studies. In 41 studies (42%) the word validation or its conjugates was mentioned, but only in a small percentage in the context of model validation. The terminology used around validation was found to be ambiguous. Model validation efforts were reported in a minority of studies. However, some studies do show good reporting examples. Cross validation of study outcomes was reported most often, but the quantity and quality of this reporting varied. More validation efforts were reported in BC than in SI. Conclusions: Only a limited number of studies reported on model validation efforts, although it may be assumed that more efforts have been taken than were reported. In particular, the differences between SI and BC may not mean that less efforts were undertaken to validate SI models. Although validation is deemed important by many researchers, this is not reflected in the reporting habits of HE modeling studies. Better reporting of validation efforts would be desirable to further enhance decision-makers' confidence in HE models and their outcomes