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含缺失协变量的 Cox 比例风险模型AI 解读

We propose two methods for handling missing covariates in using the Cox proportional hazards model. The maximum partial likelihood estimator based only on study subjects having complete covariates does not utilise all available information. Also it is biased when the probability of missingness depends on the failure or censoring time. Our suggestion is to impute the conditional expectation of the statistic involving missing covariates given the available information. The…

协变量缺失下的拟似然回归模型AI 解读

This paper presents methods to handle missing covariates when the quasi-likelihood equations for the complete data are available. Our suggestion is to replace the functions of the missing data appearing in the quasi-likelihood equation with their conditional means given the observed data or with unbiased predictors, so that the resulting equation is unbiased. We focus on two models. One is a random effects model for count data, where random effects are treated as missing…