Qu Yongming, Case Michael
Eli Lilly and Company, Indianapolis, IN 46285, USA.
Stat Med. 2006 Jan 30;25(2):223-31. doi: 10.1002/sim.2176.
As to the proportion of treatment effect (PTE) explained by surrogate markers, existing research has been focused on how to decompose the treatment effect into two parts: the treatment effect via surrogate markers and the treatment effect not explained by surrogate markers. Most proposed methods quantify the PTE explained by a single surrogate marker, or quantify the PTE explained by multiple surrogate markers without considering the cause-effect relationship among surrogate markers. The change of one marker may sometimes be due to changes in other markers. In this case, quantifying the association between multiple treatment effects via surrogates may also be important. In this paper, a method to quantify the possible causal relationship between surrogate markers is proposed. The new method can also be related to path analysis, a widely used analysis in sociology. Therefore, the proposed method can be viewed as a generalization of path analysis to generalized linear models and Cox regression models.
关于替代标志物所解释的治疗效果比例（PTE），现有研究主要集中于如何将治疗效果分解为两部分：通过替代标志物的治疗效果以及未被替代标志物解释的治疗效果。大多数提出的方法量化单个替代标志物所解释的PTE，或者量化多个替代标志物所解释的PTE，而不考虑替代标志物之间的因果关系。一个标志物的变化有时可能是由于其他标志物的变化。在这种情况下，量化通过替代物的多种治疗效果之间的关联可能也很重要。本文提出了一种量化替代标志物之间可能因果关系的方法。新方法还可以与路径分析相关联，路径分析是社会学中广泛使用的一种分析方法。因此，所提出的方法可以被视为路径分析对广义线性模型和Cox回归模型的推广。
