Thoresen M, Laake P
Section of Medical Statistics, University of Oslo, Norway.
Biometrics. 2000 Sep;56(3):868-72. doi: 10.1111/j.0006-341x.2000.00868.x.
Measurement error models in logistic regression have received considerable theoretical interest over the past 10-15 years. In this paper, we present the results of a simulation study that compares four estimation methods: the so-called regression calibration method, probit maximum likelihood as an approximation to the logistic maximum likelihood, the exact maximum likelihood method based on a logistic model, and the naive estimator, which is the result of simply ignoring the fact that some of the explanatory variables are measured with error. We have compared the behavior of these methods in a simple, additive measurement error model. We show that, in this situation, the regression calibration method is a very good alternative to more mathematically sophisticated methods.
在过去10到15年中，逻辑回归中的测量误差模型受到了相当多的理论关注。在本文中，我们展示了一项模拟研究的结果，该研究比较了四种估计方法：所谓的回归校准方法、作为逻辑最大似然近似的概率单位最大似然法、基于逻辑模型的精确最大似然法以及朴素估计器，朴素估计器是简单忽略某些解释变量存在测量误差这一事实的结果。我们在一个简单的加性测量误差模型中比较了这些方法的表现。我们表明，在这种情况下，回归校准方法是比更复杂数学方法更好的选择。
