Evaluation of novel radiotherapy technologies: what evidence is needed to assess their clinical and cost effectiveness, and how should we get it?
Evaluation of novel radiotherapy technologies: what evidence is needed to assess their clinical and cost effectiveness, and how should we get it?
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DOI：
10.1016/s1470-2045(11)70379-5
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发表时间：
2012-04-01
期刊：
影响因子：
51.1
通讯作者：
Macbeth, Fergus
中科院分区：
文献类型：
--
作者：
van Loon, Judith;Grutters, Janneke P. C.;Macbeth, Fergus
放射肿瘤学的技术革新，如调强放射治疗、立体定向放射治疗和粒子治疗，可以迅速发展并引入临床，即使与使用它们相关的费用比传统放射治疗高得多。尽管基于优越的生物学和物理特性，人们期望临床获益，但关于新放疗技术临床有效性的数据很少。来自随机临床试验的证据是理想的，但这类研究主要集中在新药上。随着时间的推移，高投资成本和修改使得在临床试验中评估新的放疗技术更加复杂。在这里，我们提出了一种算法来评估新的放射治疗技术的临床和成本效益。我们建议在随机试验可能可行的情况下，以及在随机试验不可行时应该进行的试验类型。此外，我们还讨论了剂量分布模型在估计预期临床获益和选择具有最高预期获益的患者群体方面的有用性。经济模型，包括实物期权分析的方法，可以告知一项技术的实施是应该开始（基于现有的证据）还是推迟（直到有更多的数据可用），它可以指示最佳的试验设计和所需的样本量。
Technical innovations in radiation oncology-eg, intensity-modulated radiotherapy, stereotactic radio therapy, and particle therapy-can be developed rapidly and introduced into the clinic even when costs associated with their use are much higher than those for conventional radiotherapy. Although clinical benefit is expected on the basis of superior biological and physical characteristics, data for clinical effectiveness of new radiotherapy techniques are scarce. Evidence from randomised clinical trials would be ideal but such studies focus mostly on new drugs. High investment costs and modifications over time make evaluation of novel radiotherapy technologies in clinical trials more complex. Here, we propose an algorithm for evaluation of the clinical and cost effectiveness of novel radiotherapy technologies. We suggest situations when randomised trials might be feasible and the type of trial that should be undertaken when they are not. Furthermore, we discuss the usefulness of dose-distribution models for estimation of expected clinical benefit and for selection of the patients' population with the highest expected benefit. Economic modelling, including the approach of real options analysis, can inform whether implementation of a technology should begin (based on available evidence) or be delayed (until further data are available), and it can indicate the best trial design and required sample size.