JSM 2005 - Toronto

Abstract #303073

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 447
Type: Contributed
Date/Time: Wednesday, August 10, 2005 : 2:00 PM to 3:50 PM
Sponsor: General Methodology
Abstract - #303073
Title: Imputation Methods for Doubly Censored Survival Data with an Interval-censored Covariate
Author(s): Wei Zhang*+ and Ying Zhang and Kathryn Chaloner
Companies: The University of Iowa and The University of Iowa and The University of Iowa
Address: 200 Hawkins Dr, Iowa City, IA, 52242, United States
Keywords: imputation ; non-parametric ; self-consistency algorithm ; interval censoring ; bootstrap
Abstract:

Imputation methods for statistical data analysis under the Cox model with doubly censored survival data and an interval censored covariate are developed and compared through simulation studies. Those methods impute both the interval censored initial event time and covariate based on Turnbull's self-consistency algorithm for the nonparametric estimation of the survival function with interval-censored data. Bootstrap procedure is used for inference. The motivation for this problem is a study on the effect of GBV-C virus coinfection on the survival of HIV-infected individuals where both the date of HIV infection and age at infection are interval-censored. Our simulation results show midpoint imputation and conditional mean imputation outperform other imputations in terms of bias and mean square error. The coverage probability and power of test based on bootstrap inference is adequate.

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