Iasonos Alexia, Chapman Paul B, Satagopan Jaya M
Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York.
Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.
Clin Cancer Res. 2016 May 1;22(9):2114-20. doi: 10.1158/1078-0432.CCR-15-2517.
An increased interest has been expressed in finding predictive biomarkers that can guide treatment options for both mutation carriers and noncarriers. 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 TB. Although 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 noncarriers. We illustrate the use and interpretation of the proposed measures with 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. Clin Cancer Res; 22(9); 2114-20. ©2016 AACR.
人们对寻找能够指导突变携带者和非携带者治疗方案的预测性生物标志物的兴趣日益浓厚。根据生物标志物携带状态对治疗获益（TB）差异进行统计学评估在评估预测性生物标志物中起着重要作用。对于事件发生时间终点，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）通常用作治疗获益差异的度量。尽管使用现有的统计软件包可以轻松获得该值，但HR的解释并不直观。在本文中，我们提出了基于生存概率尺度的治疗获益差异的不同汇总度量，用于评估预测性生物标志物。所提出的汇总度量可以很容易地解释为量化携带者与非携带者因治疗导致的相对风险或绝对风险增加方面的治疗获益差异。我们用来自已完成临床试验的数据说明了所提出度量的使用和解释。我们鼓励临床医生根据基于生存概率的度量来解释治疗获益差异，特别是在绝对风险增加方面，而不是使用HR。《临床癌症研究》；22(9)；2114 - 20。©2016美国癌症研究协会。
