The Asymptotic Relative Efficiency of Score Tests in a Generalized Linear Model with Surrogate Covariates
研究了流行病学研究中当暴露变量无法直接测量、使用替代变量时，如何构造最优得分检验，并推导了该检验相对于基于真实暴露的检验的渐近相对效率。
In many epidemiological studies, the exposure variable of interest cannot be measured directly. The classical approaches to errors in variables in regression do not extend easily to the nonlinear models commonly used in epidemiological research. Furthermore, the traditional additive measurement error model cannot adequately represent many surrogate relationships. By considering the effect of using surrogate independent variables on the efficient score statistic, some of the difficulties inherent in the estimation problem may be avoided. For the null hypothesis of no association, a simple and flexible procedure can be used to calculate the optimal score test. The asymptotic relative efficiency of this test to the test based upon the true exposures is derived. The optimal test is also compared to the naive procedure of substituting the surrogate into the score test for the true exposure.