A study of regression calibration in a partially observed stratified Cox model |
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Authors: | Jean-François Dupuy Eve Leconte |
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Institution: | 1. Institut de Mathématiques de Toulouse, Laboratoire de Statistique et Probabilités, Université Toulouse 3, France;2. Groupe de Recherche en Economie Mathématique et Quantitative, Université Toulouse 1, France;3. Laboratoire de Statistique et Probabilités, Université Toulouse 3, France |
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Abstract: | Regression calibration is a simple method for estimating regression models when covariate data are missing for some study subjects. It consists in replacing an unobserved covariate by an estimator of its conditional expectation given available covariates. Regression calibration has recently been investigated in various regression models such as the linear, generalized linear, and proportional hazards models. The aim of this paper is to investigate the appropriateness of this method for estimating the stratified Cox regression model with missing values of the covariate defining the strata. Despite its practical relevance, this problem has not yet been discussed in the literature. Asymptotic distribution theory is developed for the regression calibration estimator in this setting. A simulation study is also conducted to investigate the properties of this estimator. |
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Keywords: | Regression calibration Stratified Cox model Missing stratum indicators Censored data |
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