论文标题
调整病例对照关联研究中的非共同协变量
Adjusting for non-confounding covariates in case-control association studies
论文作者
论文摘要
关于是否应调整是否应调整非争议的协变量，有大量的文献对照对照逻辑回归。但是，仅在此重要主题上才能获得有限和典型的理论结果。最近提出了一种约束的最大似然方法，该方法通常比有或没有针对非共鸣的协变量的逻辑回归方法更强大。本说明提供了有或没有协变量调整的情况对照逻辑回归的理论澄清，并且根据渐近相对效率，其相对性能的最大可能性方法受到约束。我们表明，在病例对照逻辑回归中协变量调整的好处取决于疾病的患病率。我们还表明，受约束的最大似然估计器给出了渐近最强大的测试。
There is a considerable literature in case-control logistic regression on whether or not non-confounding covariates should be adjusted for. However, only limited and ad hoc theoretical results are available on this important topic. A constrained maximum likelihood method was recently proposed, which appears to be generally more powerful than logistic regression methods with or without adjusting for non-confounding covariates. This note provides a theoretical clarification for the case-control logistic regression with and without covariate adjustment and the constrained maximum likelihood method on their relative performances in terms of asymptotic relative efficiencies. We show that the benefit of covariate adjustment in the case-control logistic regression depends on the disease prevalence. We also show that the constrained maximum likelihood estimator gives an asymptotically uniformly most powerful test.
