Guo Ying, Manatunga Amita K
Department of Biostatistics and Bioinformatics, Rollins School of Public Health of Emory University, 1518 Clifton RD, Atlanta, Georgia 30322, USA.
Biometrics. 2009 Mar;65(1):125-34. doi: 10.1111/j.1541-0420.2008.01054.x. Epub 2008 May 23.
Assessing agreement is often of interest in clinical studies to evaluate the similarity of measurements produced by different raters or methods on the same subjects. We present a modified weighted kappa coefficient to measure agreement between bivariate discrete survival times. The proposed kappa coefficient accommodates censoring by redistributing the mass of censored observations within the grid where the unobserved events may potentially happen. A generalized modified weighted kappa is proposed for multivariate discrete survival times. We estimate the modified kappa coefficients nonparametrically through a multivariate survival function estimator. The asymptotic properties of the kappa estimators are established and the performance of the estimators are examined through simulation studies of bivariate and trivariate survival times. We illustrate the application of the modified kappa coefficient in the presence of censored observations with data from a prostate cancer study.
在临床研究中，评估一致性通常很重要，目的是评估不同评分者或方法对同一受试者所产生测量结果的相似性。我们提出了一种修正的加权kappa系数，用于测量双变量离散生存时间之间的一致性。所提出的kappa系数通过在未观察到的事件可能发生的网格内重新分配删失观察值的权重来处理删失情况。针对多变量离散生存时间，我们提出了一种广义的修正加权kappa系数。我们通过多变量生存函数估计器对修正的kappa系数进行非参数估计。建立了kappa估计量的渐近性质，并通过对双变量和三变量生存时间的模拟研究来检验估计量的性能。我们用一项前列腺癌研究的数据说明了在存在删失观察值的情况下修正kappa系数的应用。
