SOME SURPRISING RESULTS ABOUT COVARIATE ADJUSTMENT IN LOGISTIC-REGRESSION MODELS
SOME SURPRISING RESULTS ABOUT COVARIATE ADJUSTMENT IN LOGISTIC-REGRESSION MODELS
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DOI：
10.2307/1403444
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发表时间：
1991-08-01
影响因子：
2
通讯作者：
JEWELL, NP
中科院分区：
文献类型：
--
作者：
ROBINSON, LD;JEWELL, NP
从经典的线性回归的结果，关于调整协变量后的曝光效应的估计精度的影响，往往被认为适用于更普遍的其他类型的回归模型。 在本文中，我们表明，这样的假设是不合理的逻辑回归的情况下，调整协变量后的精度是完全不同的。 例如，在经典线性回归中，对非混杂预测协变量的调整导致精度提高，而逻辑回归中的这种调整导致精度损失。 然而，当在随机研究中检验治疗效果时，当使用logistic模型时，调整预测协变量总是更有效，因此在这方面，logistic回归的行为与经典线性回归的行为相同。
Results from classic linear regression regarding the effect of adjusting for covariates upon the precision of an estimator of exposure effect are often assumed to apply more generally to other types of regression models. In this paper we show that such an assumption is not justified in the case of logistic regression, where the effect of adjusting for covariates upon precision is quite different. For example, in classic linear regression the adjustment for a non-confounding predictive covariate results in improved precision, whereas such adjustment in logistic regression results in a loss of precision. However, when testing for a treatment effect in randomized studies, it is always more efficient to adjust for predictive covariates when logistic models are used, and thus in this regard the behavior of logistic regression is the same as that of classic linear regression.