NParCov3: A SAS/IML Macro for Nonparametric Randomization-Based Analysis of Covariance
NParCov3: A SAS/IML Macro for Nonparametric Randomization-Based Analysis of Covariance
复制标题
NParCov3：用于基于非参数随机化的协方差分析的 SAS/IML 宏
DOI：
10.18637/jss.v050.i03
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发表时间：
2012
影响因子：
5.8
通讯作者：
G. Koch
中科院分区：
文献类型：
--
作者：
Richard C. Zink;G. Koch
在随机临床试验中，协方差分析有两个重要目的。首先，治疗效果的方差减小，这提供了更强大的统计检验和更精确的置信区间。其次，它提供了治疗效应的估计值，该估计值针对治疗组之间协变量的随机失衡进行了调整。Koch、Tangen、Jung和阿马拉（1998）的非参数协方差分析方法定义了一种非常通用的方法，使用加权最小二乘法以最小假设生成协变量校正的治疗效应。该方法一般适用于各种结局，无论是连续的、二元的、有序的、发病密度或至事件发生时间。此外，它的使用已在许多临床试验环境中得到说明，如多中心、剂量反应和非劣效性试验。NParCov 3是一个SAS/IML宏，用于执行Koch等人（1998）的基于非参数随机化的协方差分析。该软件可以分析各种结果，并可以解释分层。将使用多项临床试验的数据进行说明。
Analysis of covariance serves two important purposes in a randomized clinical trial. First, there is a reduction of variance for the treatment effect which provides more powerful statistical tests and more precise confidence intervals. Second, it provides estimates of the treatment effect which are adjusted for random imbalances of covariates between the treatment groups. The nonparametric analysis of covariance method of Koch, Tangen, Jung, and Amara (1998) defines a very general methodology using weighted least-squares to generate covariate-adjusted treatment effects with minimal assumptions. This methodology is general in its applicability to a variety of outcomes, whether continuous, binary, ordinal, incidence density or time-to-event. Further, its use has been illustrated in many clinical trial settings, such as multi-center, dose-response and non-inferiority trials. NParCov3 is a SAS/IML macro written to conduct the nonparametric randomization-based covariance analyses of Koch et al. (1998). The software can analyze a variety of outcomes and can account for stratification. Data from multiple clinical trials will be used for illustration.