A Multiple Imputation Approach to Linear Regression with Clustered Censored Data

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Abstract

We extend Wei and Tanner's (1991) multiple imputation approach in semi-parametric linear regression for univariate censored data to clustered censored data. The main idea is to iterate the following two steps: 1) using the data augmentation to impute for censored failure times; 2) fitting a linear model with imputed complete data, which takes into consideration of clustering among failure times. In particular, we propose using the generalized estimating equations (GEE) or a linear mixed-effects model to implement the second step. Through simulation studies our proposal compares favorably to the independence approach (Lee et al., 1993), which ignores the within-cluster correlation in estimating the regression coefficient. Our proposal is easy to implement by using existing softwares.

Original languageEnglish (US)
Pages (from-to)111-123
Number of pages13
JournalLifetime Data Analysis
Volume7
Issue number2
DOIs
StatePublished - Dec 1 2001

Keywords

  • Asymptotic normal data augmentation
  • Buckley-James method
  • GEE
  • Generalized least squares
  • Mixed-effects model
  • Poor Man's data augmentation

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