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协变量部分缺失时删失生存数据的对数线性分析AI 解读

Log-linear models provide a flexible means of extending life table techniques for the analysis of censored survival data with categorical covariates, as discussed by Holford (1980) and Laird and Olivier (1981). We extend this methodology to incorporate cases in which one or more of the categorical covariates are sometimes missing. Maximum likelihood estimates of the parameters are calculated using data from all cases. This can result in large gains in efficiency over…

随机区组设计中删失数据的对齐秩检验AI 解读

The aligned rank test of Hodges & Lehmann (1962) is extended to censored survival data collected in matched pairs or randomized blocks. The test statistic for matched pairs is a direct extension of the original aligned rank test, whereas the test statistic for use with block size k>2 is constructed from the paired data statistics comparing all possible pairs of treatments. The efficiency of recovery of interblock information with censored matched pairs is examined by…

Maximum likelihood procedures for analysing mixed continuous and categorical data with missing values are presented. The general location model of 01kin & Tate (1961) and extensions introduced by Krzanowski (1980, 1982) form the basis for our methods. Maximum likelihood estimation with incomplete data is achieved by an application of the EM algorithm (Dempster, Laird & Rubin, 1977). Special cases of the algorithm include Orchard & Woodbury's (1972) algorithm for incomplete…

随机区组设计中删失数据的对齐秩检验AI 解读

The aligned rank test of Hodges & Lehmann (1962) is extended to censored survival data collected in matched pairs or randomized blocks. The test statistic for matched pairs is a direct extension of the original aligned rank test, whereas the test statistic for use with block size k>2 is constructed from the paired data statistics comparing all possible pairs of treatments. The efficiency of recovery of interblock information with censored matched pairs is examined by…

含缺失值的连续与分类混合数据的最大似然估计AI 解读

Maximum likelihood procedures for analysing mixed continuous and categorical data with missing values are presented. The general location model of 01kin & Tate (1961) and extensions introduced by Krzanowski (1980, 1982) form the basis for our methods. Maximum likelihood estimation with incomplete data is achieved by an application of the EM algorithm (Dempster, Laird & Rubin, 1977). Special cases of the algorithm include Orchard & Woodbury's (1972) algorithm for incomplete…