Adjusting overall survival for treatment switches: commonly used methods and practical application
Adjusting overall survival for treatment switches: commonly used methods and practical application
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DOI：
10.1002/pst.1602
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发表时间：
2013-11-01
影响因子：
1.5
通讯作者：
Wright, Elaine J.
中科院分区：
文献类型：
--
作者：
Watkins, Claire;Huang, Xin;Wright, Elaine J.
在平行组试验中，如果一些患者在事件发生前转换或交叉至替代治疗组，则长期疗效终点可能会受到影响。在肿瘤学试验中，对照组在疾病进展后可能会转换为实验性治疗，并可能影响总生存期。这可能是一个临床相关的问题，以估计如果没有患者转换，将观察到的疗效，例如，估计现实生活中的医疗技术评估的临床有效性。有几种常用的统计方法可用于调整至事件发生时间数据，以解释治疗转换，从朴素排除和删失方法到更复杂的删失加权逆概率和秩保持结构失效时间模型。这些描述，沿着其关键假设，优势和局限性。当预期转换时，为试验设计和分析提供了最佳实践指南。现有的统计软件进行了总结，并提供了这些方法在肿瘤试验的卫生技术评估的应用实例。关键考虑因素包括明确阐述的原理和研究问题以及设计良好的试验，并收集足够的高质量数据，以进行稳健的统计分析。没有一种分析方法是普遍适用于所有情况的，每一种方法都有很强的不可检验的假设。需要进一步研究新的或改进的技术。这些信息应有助于统计学家及其同事改进预期治疗转换的临床试验的设计和分析。版权所有（c）2013约翰威利父子有限公司
In parallel group trials, long-term efficacy endpoints may be affected if some patients switch or cross over to the alternative treatment arm prior to the event. In oncology trials, switch to the experimental treatment can occur in the control arm following disease progression and potentially impact overall survival. It may be a clinically relevant question to estimate the efficacy that would have been observed if no patients had switched, for example, to estimate real-life' clinical effectiveness for a health technology assessment. Several commonly used statistical methods are available that try to adjust time-to-event data to account for treatment switching, ranging from naive exclusion and censoring approaches to more complex inverse probability of censoring weighting and rank-preserving structural failure time models. These are described, along with their key assumptions, strengths, and limitations. Best practice guidance is provided for both trial design and analysis when switching is anticipated. Available statistical software is summarized, and examples are provided of the application of these methods in health technology assessments of oncology trials. Key considerations include having a clearly articulated rationale and research question and a well-designed trial with sufficient good quality data collection to enable robust statistical analysis. No analysis method is universally suitable in all situations, and each makes strong untestable assumptions. There is a need for further research into new or improved techniques. This information should aid statisticians and their colleagues to improve the design and analysis of clinical trials where treatment switch is anticipated. Copyright (c) 2013 John Wiley & Sons, Ltd.