Chen Cong, Wang Hongwei, Snapinn Steven M
Merck Research Laboratories, BL X-27, PO Box 4, West Point, PA 19486, USA.
Stat Med. 2003 Nov 30;22(22):3449-59. doi: 10.1002/sim.1575.
In time-varying covariate analysis of clinical survival data, it is often of interest to estimate the proportion of treatment effect (PTE), along with its confidence intervals, explained by a surrogate marker. The conventional procedure for such an analysis fits data into two working models separately to estimate the treatment effects before and after adjustment of the covariate. The construction of confidence intervals for the PTE under the conventional procedure lacks support by standard statistical software such as SAS, and could be very computationally demanding even after the support is available in the future. To overcome this problem, we propose a new procedure to simplify the computation. Under the new procedure, the treatment effects before and after adjustment of the covariate are simultaneously estimated from a single model. More important than saving computational effort, the new procedure can also be effectively applied to multiple-covariate models for the decomposition of overall treatment effect and for the comparison of PTE among several surrogate markers. The new procedure is applied to the motivating data example from the LIFE study, and demonstrates flexibility that the conventional procedure currently lacks.
在临床生存数据的时变协变量分析中，通常有兴趣估计由替代标志物解释的治疗效果比例（PTE）及其置信区间。此类分析的传统程序是将数据分别拟合到两个工作模型中，以估计协变量调整前后的治疗效果。在传统程序下，PTE置信区间的构建缺乏诸如SAS等标准统计软件的支持，即使未来有了支持，计算量也可能非常大。为克服这一问题，我们提出了一种新程序以简化计算。在新程序下，协变量调整前后的治疗效果从单个模型中同时估计。比节省计算量更重要的是，新程序还可有效应用于多协变量模型，用于总体治疗效果的分解以及几个替代标志物之间PTE的比较。新程序应用于来自LIFE研究的激励性数据示例，并展示了传统程序目前所缺乏的灵活性。
