1 Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ, USA.
2 Department of Biostatistics, Harvard University, Boston, MA, USA.
Stat Methods Med Res. 2018 Apr;27(4):1099-1114. doi: 10.1177/0962280216653427. Epub 2016 Jun 2.
In cancer studies, patients often experience two different types of events: a non-terminal event such as recurrence or metastasis, and a terminal event such as cancer-specific death. Identifying pathways and networks of genes associated with one or both of these events is an important step in understanding disease development and targeting new biological processes for potential intervention. These correlated outcomes are commonly dealt with by modeling progression-free survival, where the event time is the minimum between the times of recurrence and death. However, identifying pathways only associated with progression-free survival may miss out on pathways that affect time to recurrence but not death, or vice versa. We propose a combined testing procedure for a pathway's association with both the cause-specific hazard of recurrence and the marginal hazard of death. The dependency between the two outcomes is accounted for through perturbation resampling to approximate the test's null distribution, without any further assumption on the nature of the dependency. Even complex non-linear relationships between pathways and disease progression or death can be uncovered thanks to a flexible kernel machine framework. The superior statistical power of our approach is demonstrated in numerical studies and in a gene expression study of breast cancer.
在癌症研究中，患者通常会经历两种不同类型的事件：非终末事件，如复发或转移，以及终末事件，如癌症特异性死亡。确定与这些事件之一或两者都相关的途径和基因网络是理解疾病发展和针对潜在干预的新生物学过程的重要步骤。这些相关的结局通常通过建模无进展生存期来处理，其中事件时间是复发和死亡时间之间的最小值。然而，仅识别与无进展生存期相关的途径可能会错过影响复发时间而不影响死亡时间的途径，反之亦然。我们提出了一种联合检验程序，用于检验途径与复发的特异性危险和死亡的边缘危险之间的关联。通过扰动重采样来考虑两个结果之间的依赖性，以近似检验的零分布，而无需对依赖性的性质做出任何进一步的假设。由于具有灵活的核机器框架，甚至可以揭示途径与疾病进展或死亡之间的复杂非线性关系。在数值研究和乳腺癌基因表达研究中，我们的方法具有优越的统计功效。
