Rank tests for association with right censored data
Rank tests for association with right censored data
复制标题
与右删失数据关联的排名测试
DOI：
10.1093/biomet/69.2.351
复制
发表时间：
1982
期刊：
影响因子：
2.7
通讯作者：
J. Cuzick
中科院分区：
文献类型：
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作者：
J. Cuzick
SUMMARY Rank tests for association between a time variable and a continuous covariate, and between two time variables are given in the presence of censoring. In the first case, these tests are related to the semiparametric tests of Cox (1972) and Prentice (1978) but differ in that the covariate is replaced by a score computed from its rank. In the second case, the test statistic is again a simple inner product, which in the proportional hazards model reduces to the correlation between the two vectors of log rank scores. The loss of efficiency arising from incorrect score functions is also explored when there is no censoring. In this paper we consider a general model for association between pairs of observations in which either variable or both variables may be right censored. The aim is to develop tests based on the generalized ranks of both variables which have maximal power at alternatives which are near to the null hypothesis of independence. Attention will be concentrated on two special cases of interest in survival analysis: tests for association between a censored survival time and a possibly censored continuous covariate, and tests for association between the times to different events. Beginning with Cox (1972), the first situation has been studied extensively in a semiparametric set-up in which information based on ranks is used for the time variable but exact values are used for the covariate. Procedures which use only the ranks of both variables have been developed by Brown, Hollander & Korwar (1974) and more recently by O'Brien (1978). However, neither of these procedures have been developed with a particular model in mind and one of the achievements of the present paper is to derive rank tests with good local power when a particular model such as the proportional hazards model holds. In the case of a proportional hazards model with a continuous covariate, the present development may be preferable to the semiparametric methods when the functional form of the relation- ship between the hazard and the covariate is not known in advance, or when errors or outliers may exist in the observed covariate values. Thus, significance levels may be obtained which are not inflated by multiple regroupings or transformations of the covariate data, although it would be expected that parametric model building with variables found to be associated with survival would often be carried out in further analyses.
DOI：
--
发表时间：
1972
期刊：
--
影响因子：
--
作者：
D. Cox
通讯作者：
D. Cox