JSM 2004 - Toronto

Abstract #300564

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Activity Number: 82
Type: Contributed
Date/Time: Monday, August 9, 2004 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #300564
Title: Comparison of Variance Estimation Approaches in a Two-state Markov Model for Longitudinal Data with Misclassification
Author(s): Rhonda J. Rosychuk*+ and Xiaoming Sheng
Companies: University of Alberta and University of Utah School of Medicine
Address: Department of Pediatrics, Edmonton, AB, T6G 2J3, Canada
Keywords: jackknife ; bootstrap ; variance ; Markov process ; misclassification
Abstract:

We examine the behavior of the variance-covariance parameter estimates in an alternating binary Markov model with misclassification. Transition probabilities specify the state transitions for a process that is not directly observable. The state of an observable process, which may not correctly classify the state of the unobservable process, is obtained at discrete time points. Misclassification probabilities capture the two types of classification errors. Variance components of the estimated transition parameters are calculated with three estimation procedures: observed Fisher information, jackknife and Monte Carlo bootstrap techniques. Simulation studies are used to compare variance estimates and reveal the effect of misclassification on transition parameter estimation. The three approaches generally provide similar variance estimates.

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