Li Gang, Yang Qing
Department of Biostatistics, University of California Los Angeles, Los Angeles, CA, USA.
School of Nursing, Duke University, Durham, NC, USA.
J Am Stat Assoc. 2016;111(515):1289-1300. doi: 10.1080/01621459.2015.1093942. Epub 2016 Oct 18.
This article develops joint inferential methods for the cause-specific hazard function and the cumulative incidence function of a specific type of failure to assess the effects of a variable on the time to the type of failure of interest in the presence of competing risks. Joint inference for the two functions are needed in practice because (i) they describe different characteristics of a given type of failure, (ii) they do not uniquely determine each other, and (iii) the effects of a variable on the two functions can be different and one often does not know which effects are to be expected. We study both the group comparison problem and the regression problem. We also discuss joint inference for other related functions. Our simulation shows that our joint tests can be considerably more powerful than the Bonferroni method, which has important practical implications to the analysis and design of clinical studies with competing risks data. We illustrate our method using a Hodgkin disease data and a lymphoma data. Supplementary materials for this article are available online.
本文针对特定类型失败的病因特异性风险函数和累积发病率函数开发了联合推断方法，以在存在竞争风险的情况下评估变量对感兴趣的失败类型发生时间的影响。在实践中需要对这两个函数进行联合推断，原因如下：（i）它们描述了给定类型失败的不同特征；（ii）它们不能唯一地相互确定；（iii）变量对这两个函数的影响可能不同，而且人们通常不知道会出现哪种影响。我们研究了组间比较问题和回归问题。我们还讨论了其他相关函数的联合推断。我们的模拟表明，我们的联合检验比邦费罗尼方法的功效要高得多，这对具有竞争风险数据的临床研究的分析和设计具有重要的实际意义。我们使用霍奇金病数据和淋巴瘤数据说明了我们的方法。本文的补充材料可在线获取。
