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基于绝对预测误差估计量抽样分布的模型评价AI 解读

The construction of a reliable, practically useful prediction rule for future responses is heavily dependent on the ‘adequacy’ of the fitted regression model. In this article, we consider the absolute prediction error, the expected value of the absolute difference between the future and predicted responses, as the model evaluation criterion. This prediction error is easier to interpret than the average squared error and is equivalent to the misclassification error for a…

使用广义结构均值模型进行因果推断AI 解读

Summary We estimate cause–effect relationships in empirical research where exposures are not completely controlled, as in observational studies or with patient non-compliance and self-selected treatment switches in randomized clinical trials. Additive and multiplicative structural mean models have proved useful for this but suffer from the classical limitations of linear and log-linear models when accommodating binary data. We propose the generalized structural mean model to…

失效类型缺失时竞争风险的修正对数秩检验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.