BACKGROUND AND PURPOSE: Innovative therapies are not only characterized by major uncertainties regarding clinical benefit and cost but also the expected recruitment of patients. An original model was developed to simulate patient recruitment to a costly particle therapy by varying layout of the facility and patient referral (one vs. several countries) and by weighting the treated indication by the expected benefit of particle therapy. MATERIAL AND METHODS: A multi-step probabilistic spatial model was used to allocate patients to the optimal treatment strategy and facility taking into account the estimated therapeutic gain from the new therapy for each tumour type, the geographical accessibility of the facilities and patient preference. Recruitment was simulated under different assumptions relating to the demand and supply. RESULTS: Extending the recruitment area, reducing treatment capacity, equipping all treatment rooms with a carbon ion gantry and inclusion of proton protocols in carbon ion facilities led to an increased proportion of indications with the highest expected benefit. Assuming the existence of a competing carbon ions facility, lower values of therapeutic gain, and a greater unwillingness of patients to travel for treatment increased the proportion of indications with low expected benefit. CONCLUSIONS: Modelling patient recruitment may aid decision-making when planning new and expensive treatments. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.
BACKGROUND AND PURPOSE: Innovative therapies are not only characterized by major uncertainties regarding clinical benefit and cost but also the expected recruitment of patients. An original model was developed to simulate patient recruitment to a costly particle therapy by varying layout of the facility and patient referral (one vs. several countries) and by weighting the treated indication by the expected benefit of particle therapy. MATERIAL AND METHODS: A multi-step probabilistic spatial model was used to allocate patients to the optimal treatment strategy and facility taking into account the estimated therapeutic gain from the new therapy for each tumour type, the geographical accessibility of the facilities and patient preference. Recruitment was simulated under different assumptions relating to the demand and supply. RESULTS: Extending the recruitment area, reducing treatment capacity, equipping all treatment rooms with a carbon ion gantry and inclusion of proton protocols in carbon ion facilities led to an increased proportion of indications with the highest expected benefit. Assuming the existence of a competing carbon ions facility, lower values of therapeutic gain, and a greater unwillingness of patients to travel for treatment increased the proportion of indications with low expected benefit. CONCLUSIONS: Modelling patient recruitment may aid decision-making when planning new and expensive treatments. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.
Authors: Nils H Nicolay; Yingying Liang; Ramon Lopez Perez; Tilman Bostel; Thuy Trinh; Sonevisay Sisombath; Klaus-Josef Weber; Anthony D Ho; Jürgen Debus; Rainer Saffrich; Peter E Huber Journal: Oncotarget Date: 2015-02-10
Authors: Timothy D Malouff; Laura A Vallow; Danushka Seneviratne; Anita Mahajan; Robert L Foote; Bradford Hoppe; Chris Beltran; Steven J Buskirk; Sunil Krishnan; Daniel M Trifiletti Journal: Int J Part Ther Date: 2020-11-05