REGRESSION DILUTION IN THE PROPORTIONAL HAZARDS MODEL
REGRESSION DILUTION IN THE PROPORTIONAL HAZARDS MODEL
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DOI：
10.2307/2532247
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发表时间：
1993-12-01
期刊：
影响因子：
1.9
通讯作者：
HUGHES, MD
中科院分区：
文献类型：
--
作者：
HUGHES, MD
使用比例风险模型研究了生存数据中协变量测量误差引起的回归稀释问题。考虑参数估计的朴素方法，即在通常的分析中不适当地使用观察到的协变量值，而不是基础协变量值。得到了大样本下估计参数与真实参数之间的关系，表明当误差服从正态分布时，偏差不依赖于基线危险函数的形式。在高度审查的情况下，通过因子1 + lambda对朴素估计进行调整，其中lambda是关于潜在平均水平的人内变异性与抽样人群中这些水平的变异性的比率，消除了偏倚。随着审查的增加，所需的调整增加，当没有审查时，明显高于1 + lambda，并且还取决于真实的风险关系。
The problem of regression dilution arising from covariate measurement error is investigated for survival data using the proportional hazards model. The naive approach to parameter estimation is considered whereby observed covariate values are used, inappropriately, in the usual analysis instead of the underlying covariate values. A relationship between the estimated parameter in large samples and the true parameter is obtained showing that the bias does not depend on the form of the baseline hazard function when the errors are normally distributed. With high censorship, adjustment of the naive estimate by the factor 1 + lambda, where lambda is the ratio of within-person variability about an underlying mean level to the variability of these levels in the population sampled, removes the bias. As censorship increases, the adjustment required increases and when there is no censorship is markedly higher than 1 + lambda and depends also on the true risk relationship.