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逻辑回归中遗漏协变量与暴露误设造成的效率损失AI 解读

We examine the effects of omitting covariates and misspecifying the explanatory variable of interest on tests of association between the explanatory variable and response based on logistic regression models. We assume throughout that the covariates are statistically independent of the explanatory variable, as under simple randomization. A general expression for the asymptotic loss in efficiency from omitting covariates and misspecifying the explanatory variable is derived.…

模型误设对逻辑回归关联检验的影响AI 解读

We consider the effects of misspecifying the covariate vector on tests of association between a particular covariate and the response variable in logistic regression models. Misspecification can include random measurement error, discretizing a continuous explanatory variable and mismodeling the functional form of an explanatory variable. The effects of misspecification are examined by deriving the asymptotic distribution of the test statistic under a sequence of models.…