MODEL MISSPECIFICATION IN PROPORTIONAL HAZARDS REGRESSION
MODEL MISSPECIFICATION IN PROPORTIONAL HAZARDS REGRESSION
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DOI：
10.1093/biomet/82.3.527
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发表时间：
1995-09-01
期刊：
影响因子：
2.7
通讯作者：
FLEMING, TR
中科院分区：
文献类型：
--
作者：
ANDERSON, GL;FLEMING, TR
比例风险模型常用于评价随机临床试验中治疗对失败时间事件的影响。伴随变量通常是可用的，并且可以考虑在主要分析中使用，假设是合并它们可以减少偏倚或提高效率。在本文中，我们考虑两种方法，包括协变量信息：回归建模和分层。我们专注于设置协变量的影响是不成比例的，我们比较这些方法的偏差，效率和覆盖范围的属性。这些结果表明，我们的直觉基于线性模型分析的协方差是误导。比例风险模型中的协变量调整对方差的影响很小，但可能会显着提高治疗效果估计的准确性。
The proportional hazards model is frequently used to evaluate the effect of treatment on failure time events in randomised clinical trials. Concomitant variables are usually available and may be considered for use in the primary analyses under the assumption that incorporating them may reduce bias or improve efficiency. In this paper we consider two approaches to including covariate information: regression modelling and stratification. We focus on the setting where covariate effects are nonproportional and we compare the bias, efficiency and coverage properties of these approaches. These results indicate that our intuition based on linear model analysis of covariance is misleading. Covariate adjustment in proportional hazards models has little effect on the variance but may significantly improve the accuracy of the treatment effect estimator.