Satagopan Jaya M, Iasonos Alexia
Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 485 Lexington Avenue, New York, NY 10017, United States.
Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 485 Lexington Avenue, New York, NY 10017, United States.
Contemp Clin Trials. 2017 Dec;63:40-50. doi: 10.1016/j.cct.2017.02.007. Epub 2017 Feb 22.
Clinical and epidemiological studies of anticancer therapies increasingly seek to identify predictive biomarkers to obtain insights into variation in treatment benefit. For time to event endpoints, a predictive biomarker is typically assessed using the interaction between the biomarker and treatment in a proportional hazards model. Interactions are contrasts of summaries of outcomes and depend upon the choice of the outcome scale. In this paper, we investigate interaction contrasts under three scales - the natural logarithm of hazard ratio, the natural logarithm of survival probability, and survival probability at a pre-specified time. We illustrate that we can have a non-zero interaction on survival or logarithm of survival probability scales even when there is no interaction on the logarithm of hazard ratio scale. Since survival probabilities have clinically useful interpretation and are easier to convey to patients than hazard ratios, we recommend evaluating a predictive biomarker using survival probabilities. We provide empirical illustration of the three scales of interaction for evaluating a predictive biomarker using reconstructed data from a published melanoma study.
抗癌疗法的临床和流行病学研究越来越多地寻求识别预测性生物标志物，以深入了解治疗效果的差异。对于事件发生时间终点，通常在比例风险模型中使用生物标志物与治疗之间的相互作用来评估预测性生物标志物。相互作用是结果摘要的对比，并且取决于结果量表的选择。在本文中，我们研究了三种量表下的相互作用对比——风险比的自然对数、生存概率的自然对数以及预先指定时间的生存概率。我们表明，即使在风险比对数量表上没有相互作用，在生存或生存概率对数量表上也可能存在非零相互作用。由于生存概率具有临床有用的解释，并且比风险比更容易向患者传达，因此我们建议使用生存概率来评估预测性生物标志物。我们使用来自一项已发表的黑色素瘤研究的重建数据，对评估预测性生物标志物的三种相互作用量表进行了实证说明。
