Akazawa K, Nakamura T, Moriguchi S, Shimada M, Nose Y
Department of Medical Informatics, Kyushu University Hospital, Fukuoka, Japan.
Comput Methods Programs Biomed. 1991 Jul;35(3):203-12. doi: 10.1016/0169-2607(91)90122-a.
Small sample properties of the maximum partial likelihood estimates for Cox's proportional hazards model depend on the sample size, the true values of regression coefficients, covariate structure, censoring pattern and possibly baseline hazard functions. Therefore, it would be difficult to construct a formula or table to calculate the exact power of a statistical test for the treatment effect in any specific clinical trial. The simulation program, written in SAS/IML, described in this paper uses Monte-Carlo methods to provide estimates of the exact power for Cox's proportional hazards model. For illustrative purposes, the program was applied to real data obtained from a clinical trial performed in Japan. Since the program does not assume any specific function for the baseline hazard, it is, in principle, applicable to any censored survival data as long as they follow Cox's proportional hazards model.
Cox比例风险模型最大偏似然估计的小样本性质取决于样本量、回归系数的真实值、协变量结构、删失模式以及可能的基线风险函数。因此，在任何特定的临床试验中，要构建一个公式或表格来计算治疗效果统计检验的精确效能是困难的。本文中描述的用SAS/IML编写的模拟程序使用蒙特卡罗方法来提供Cox比例风险模型精确效能的估计值。为了说明目的，该程序应用于从日本进行的一项临床试验获得的真实数据。由于该程序不假定基线风险有任何特定函数，原则上，只要删失生存数据遵循Cox比例风险模型，它就适用于任何此类数据。
