Quantifying Treatment Benefit in Molecular Subgroups to Assess a Predictive Biomarker.
Quantifying Treatment Benefit in Molecular Subgroups to Assess a Predictive Biomarker.
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DOI：
10.1158/1078-0432.ccr-15-2517
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发表时间：
2016-05-01
期刊：
影响因子：
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通讯作者：
Satagopan JM
中科院分区：
文献类型：
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作者：
Iasonos A;Chapman PB;Satagopan JM
人们越来越关注寻找预测性生物标志物，以指导突变携带者和非携带者的治疗选择。根据生物标志物携带状态对治疗获益（TB）变化的统计评估在评估预测性生物标志物中起着重要作用。对于至事件时间终点，比例风险回归模型中治疗与生物标志物之间相互作用的风险比（HR）通常用作治疗获益变化的指标。虽然这可以很容易地获得使用现有的统计软件包，人力资源的解释是不简单的。在这篇文章中，我们提出了不同的总结措施的变化，结核病的生存概率的规模评估预测生物标志物。拟议的汇总措施可以很容易地解释为量化结核病的相对风险或过度绝对风险方面的差异，由于在运营商与非运营商的治疗。我们说明了使用和解释的建议措施，使用已完成的临床试验的数据。我们鼓励临床医生根据生存概率的测量来解释结核病的变化，特别是根据超额绝对风险，而不是HR。
There is an increased interest in finding predictive biomarkers that can guide treatment options for both mutation carriers and non-carriers. The statistical assessment of variation in treatment benefit (TB) according to the biomarker carrier status plays an important role in evaluating predictive biomarkers. For time to event endpoints, the hazard ratio (HR) for interaction between treatment and a biomarker from a Proportional Hazards regression model is commonly used as a measure of variation in treatment benefit. While this can be easily obtained using available statistical software packages, the interpretation of HR is not straightforward. In this article, we propose different summary measures of variation in TB on the scale of survival probabilities for evaluating a predictive biomarker. The proposed summary measures can be easily interpreted as quantifying differential in TB in terms of relative risk or excess absolute risk due to treatment in carriers versus non-carriers. We illustrate the use and interpretation of the proposed measures using data from completed clinical trials. We encourage clinical practitioners to interpret variation in TB in terms of measures based on survival probabilities, particularly in terms of excess absolute risk, as opposed to HR.