Liverpool Reviews and Implementation Group (LRiG), University of Liverpool, UK.
Med Decis Making. 2014 Apr;34(3):343-51. doi: 10.1177/0272989X13497998. Epub 2013 Jul 30.
A recent publication includes a review of survival extrapolation methods used in technology appraisals of treatments for advanced cancers. The author of the article also noted shortcomings and inconsistencies in the analytical methods used in appraisals. He then proposed a survival model selection process algorithm to guide modelers' choice of projective models for use in future appraisals. This article examines the proposed algorithm and highlights various shortcomings that involve questionable assumptions, including researchers' access to patient-level data, the relevance of proportional hazards modeling, and the appropriateness of standard probability functions for characterizing risk, which may mislead practitioners into employing biased structures for projecting limited data in decision models. An alternative paradigm is outlined. This paradigm is based on the primacy of the experimental data and adherence to the scientific method through hypothesis formulation and validation. Drawing on extensive experience of survival modeling and extrapolation in the United Kingdom, practical advice is presented on issues of importance when using data from clinical trials terminated without complete follow-up as a basis for survival extrapolation.
最近的一篇出版物对用于评估晚期癌症治疗技术的生存外推方法进行了综述。文章的作者还指出了评估中使用的分析方法存在的缺陷和不一致之处。然后，他提出了一种生存模型选择过程算法，以指导建模者选择用于未来评估的预测模型。本文对所提出的算法进行了检验，并强调了涉及可疑假设的各种缺陷，包括研究人员获取患者水平数据的机会、比例风险建模的相关性以及标准概率函数对风险特征的适当性，这些可能会误导从业者在决策模型中使用有偏结构来预测有限数据。本文还概述了一种替代范式。该范式基于实验数据的首要地位，并通过假设的制定和验证来坚持科学方法。本文借鉴了在英国进行生存建模和外推的丰富经验，就使用临床试验中未完成随访的数据作为生存外推基础时的重要问题提供了实用建议。
