Lipsitz S R, Parzen M, Ewell M
Department of Biostatistics, Harvard School of Public Health and Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA.
Biometrics. 1998 Mar;54(1):295-303.
When there are many nuisance parameters in a logistic regression model, a popular method for eliminating these nuisance parameters is conditional logistic regression. Unfortunately, another common problem in a logistic regression analysis is missing covariate data. With many nuisance parameters to eliminate and missing covariates, many investigators exclude any subject with missing covariates and then use conditional logistic regression, often called a complete-case analysis. In this article, we derive a modified conditional logistic regression that is appropriate with covariates that are missing at random. Performing a conditional logistic regression with only the complete cases is convenient with existing statistical packages, but it may give bias if missingness is not completely at random.
当逻辑回归模型中存在许多干扰参数时，一种消除这些干扰参数的常用方法是条件逻辑回归。不幸的是，逻辑回归分析中的另一个常见问题是协变量数据缺失。由于有许多干扰参数需要消除且存在协变量缺失的情况，许多研究者会排除任何有协变量缺失的受试者，然后使用条件逻辑回归，通常称为完全病例分析。在本文中，我们推导了一种适用于随机缺失协变量的修正条件逻辑回归。仅对完全病例进行条件逻辑回归在现有的统计软件包中很方便，但如果缺失并非完全随机，可能会产生偏差。
