Department of Radiation Oncology, Centre Léon Bérard, Lyon, France.
Radiother Oncol. 2010 Aug;96(2):243-9. doi: 10.1016/j.radonc.2010.04.010. Epub 2010 May 7.
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.
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.
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.
Modelling patient recruitment may aid decision-making when planning new and expensive treatments.
创新疗法不仅具有临床获益和成本的重大不确定性，还具有预期的患者招募问题。我们开发了一个原始模型，通过改变设施布局和患者转诊（一个国家与多个国家）以及根据粒子治疗的预期获益对治疗适应症进行加权，来模拟昂贵的粒子治疗的患者招募情况。
使用多步骤概率空间模型，根据新疗法对每种肿瘤类型的估计治疗增益、设施的地理可及性和患者偏好，为每个患者分配最佳治疗策略和设施。根据需求和供应的不同假设进行了招募模拟。
扩大招募区域、减少治疗能力、为所有治疗室配备碳离子回旋加速器，并在碳离子设施中纳入质子方案，导致具有最高预期获益的适应症比例增加。假设存在竞争的碳离子设施、较低的治疗增益值以及患者更不愿意为治疗而旅行，这会增加具有低预期获益的适应症比例。
在规划新的昂贵治疗方法时，模拟患者招募可能有助于决策。
