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协变量测量误差异质时的生存分析AI 解读

This article is motivated by a time-to-event analysis where the covariate of interest was measured at the wrong time. We show that the problem can be formulated as a special case of survival analysis with heterogeneous covariate measurement error and develop a general analytic framework. We study the asymptotic behavior of the naive partial likelihood estimates and analytically demonstrate that under the heterogeneous measurement error structure and the assumption that all…

部分失效类型缺失时的竞争风险生存数据分析AI 解读

We propose a method to analyse competing risks survival data when failure types are missing for some individuals. Our approach is based on a standard proportional hazards structure for each of the failure types, and involves the solution to estimating equations. We present consistent and asymptotically normal estimators of the regression coefficients and related score tests. An appealing feature is that individuals with known failure types make the same contributions as they…

部分失效类型缺失时的竞争风险生存数据分析AI 解读

We propose a method to analyse competing risks survival data when failure types are missing for some individuals. Our approach is based on a standard proportional hazards structure for each of the failure types, and involves the solution to estimating equations. We present consistent and asymptotically normal estimators of the regression coefficients and related score tests. An appealing feature is that individuals with known failure types make the same contributions as they…

失效类型缺失时竞争风险数据的修正对数秩检验AI 解读

We propose a modified log rank test for the analysis of competing risks survival data, when failure type is missing for some individuals. The proposed test reduces to a standard log rank test when all failure types are known. The test arises from a partial likelihood, constructed under semiparametric assumptions on the relationship between cause-specific hazards.

随机效应模型的正态性评估AI 解读

When one uses the unbalanced, mixed linear model $\mathbf{y}_i = \mathbf{X}_i\mathbf{\alpha} + \mathbf{Z}_i\mathbf{\beta}_i + \varepsilon_i, i = 1, \cdots, n$ to analyze data from longitudinal experiments with continuous outcomes, it is customary to assume $\varepsilon_i \sim_{\operatorname{ind}} \mathscr{N}(\mathbf{0}, \sigma^2\mathbf{I}_i)$ independent of $\mathbf{\beta}_i \sim_{\operatorname{iid}} \mathscr{N}(\mathbf{0,\Delta})$, where $\sigma^2$ and the elements of an…