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Analysis of progressive multi-state models with misclassified states: likelihood and pairwise likelihood methods

Analysis of progressive multi-state models with misclassified states: likelihood and pairwise... Multi-state models are commonly used in studies of disease progression. Methods developed under this framework, however, are often challenged by misclassification in states. In this article, we investigate issues concerning continuous-time progressive multi-state models with state misclassification. We develop inference methods using both the likelihood and pairwise likelihood methods that are based on joint modelling of the progressive and misclassification processes. We assess the performance of the proposed methods by simulation studies, and illustrate their use by the application to the data arising from a coronary allograft vasculopathy study. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biostatistics & Epidemiology Taylor & Francis

Analysis of progressive multi-state models with misclassified states: likelihood and pairwise likelihood methods

Biostatistics & Epidemiology , Volume 1 (1): 14 – Jan 1, 2017

Analysis of progressive multi-state models with misclassified states: likelihood and pairwise likelihood methods

Abstract

Multi-state models are commonly used in studies of disease progression. Methods developed under this framework, however, are often challenged by misclassification in states. In this article, we investigate issues concerning continuous-time progressive multi-state models with state misclassification. We develop inference methods using both the likelihood and pairwise likelihood methods that are based on joint modelling of the progressive and misclassification processes. We assess the...
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Publisher
Taylor & Francis
Copyright
© 2017 International Biometric Society – Chinese Region
ISSN
2470-9379
eISSN
2470-9360
DOI
10.1080/24709360.2017.1359356
Publisher site
See Article on Publisher Site

Abstract

Multi-state models are commonly used in studies of disease progression. Methods developed under this framework, however, are often challenged by misclassification in states. In this article, we investigate issues concerning continuous-time progressive multi-state models with state misclassification. We develop inference methods using both the likelihood and pairwise likelihood methods that are based on joint modelling of the progressive and misclassification processes. We assess the performance of the proposed methods by simulation studies, and illustrate their use by the application to the data arising from a coronary allograft vasculopathy study.

Journal

Biostatistics & EpidemiologyTaylor & Francis

Published: Jan 1, 2017

Keywords: Hidden Markov model; likelihood; misclassification; pairwise likelihood; panel data; progressive model

References