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不可忽略缺失机制下线性混合效应模型的渐近偏差AI 解读

Summary In longitudinal studies, missingness of data is often an unavoidable problem. Estimators from the linear mixed effects model assume that missing data are missing at random. However, estimators are biased when this assumption is not met. In the paper, theoretical results for the asymptotic bias are established under non-ignorable drop-out, drop-in and other missing data patterns. The asymptotic bias is large when the drop-out subjects have only one or no observation,…

多元线性回归中解释变量缺失的指示变量法与分层法AI 解读

The statistical literature and folklore contain many methods for handling missing explanatory variable data in multiple linear regression. One such approach is to incorporate into the regression model an indicator variable for whether an explanatory variable is observed. Another approach is to stratify the model based on the range of values for an explanatory variable, with a separate stratum for those individuals in which the explanatory variable is missing. For a least…

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生存数据马尔可夫模型的非参数检验AI 解读

Jones & Crowley (1989) introduced a general class of nonparametric statistics for right-censored survival data that includes many of the existing procedures involving a single covariate. This paper considers this class in the framework of a Markov model and suggests some tests especially appropriate for the Markov setting. Application to left-truncated and right-censored data is also given. Small sample properties and asymptotic relative efficiencies for several statistics…

生存分析中一类非参数检验的渐近性质AI 解读

Many of the popular nonparametric test statistics for censored survival data used in two-sample, $s$-sample trend and single continuous covariate situations are special cases of a general statistic, differing only in the choice of covariate-based label and weight function. Formulated in terms of counting processes and martingales this general statistic, standardized by the square root of its consistent variance estimator, is shown to be asymptotically normal under the null…