Huang Ying, Gilbert Peter B, Janes Holly
Fred Hutchinson Cancer Research Center, Seattle, Washington, 98109, USA.
Biometrics. 2012 Sep;68(3):687-96. doi: 10.1111/j.1541-0420.2011.01722.x. Epub 2012 Feb 2.
Treatment-selection markers are biological molecules or patient characteristics associated with one's response to treatment. They can be used to predict treatment effects for individual subjects and subsequently help deliver treatment to those most likely to benefit from it. Statistical tools are needed to evaluate a marker's capacity to help with treatment selection. The commonly adopted criterion for a good treatment-selection marker has been the interaction between marker and treatment. While a strong interaction is important, it is, however, not sufficient for good marker performance. In this article, we develop novel measures for assessing a continuous treatment-selection marker, based on a potential outcomes framework. Under a set of assumptions, we derive the optimal decision rule based on the marker to classify individuals according to treatment benefit, and characterize the marker's performance using the corresponding classification accuracy as well as the overall distribution of the classifier. We develop a constrained maximum-likelihood method for estimation and testing in a randomized trial setting. Simulation studies are conducted to demonstrate the performance of our methods. Finally, we illustrate the methods using an HIV vaccine trial where we explore the value of the level of preexisting immunity to adenovirus serotype 5 for predicting a vaccine-induced increase in the risk of HIV acquisition.
治疗选择标志物是与个体对治疗的反应相关的生物分子或患者特征。它们可用于预测个体受试者的治疗效果，进而帮助将治疗提供给最有可能从中受益的患者。需要统计工具来评估标志物在辅助治疗选择方面的能力。对于一个好的治疗选择标志物，普遍采用的标准是标志物与治疗之间的相互作用。虽然强烈的相互作用很重要，但对于良好的标志物性能而言，这还不够。在本文中，我们基于潜在结果框架，开发了用于评估连续治疗选择标志物的新方法。在一组假设下，我们推导出基于标志物的最优决策规则，以便根据治疗获益对个体进行分类，并使用相应的分类准确性以及分类器的总体分布来描述标志物的性能。我们开发了一种约束最大似然方法，用于在随机试验环境中进行估计和检验。进行了模拟研究以证明我们方法的性能。最后，我们通过一项HIV疫苗试验来说明这些方法，在该试验中，我们探讨了对5型腺病毒的预先存在的免疫水平对于预测疫苗诱导的HIV感染风险增加的价值。
