Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

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

Yi, Grace Y.; He, Wenqing; He, Feng
Biostatistics & Epidemiology , Volume 1 (1): 14 – Jan 1, 2017
Download PDF

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...
Loading next page...
 
/lp/taylor-francis/analysis-of-progressive-multi-state-models-with-misclassified-states-V8F5dM2pIm
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 & Epidemiology – Taylor & Francis

Published: Jan 1, 2017

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

References

  • Estimating the extent of tracking in interval-censored chain-of-events data
  • A conditional Markov model for clustered progressive multistate processes under incomplete observation
  • Analysis of interval-censored data from clustered multistate processes: application to joint damage in psoriatic arthritis
  • Analysis of interval-censored disease progression data via multi-state models under a nonignorable inspection process
  • Estimation of parasitic infection dynamics when detectability is imperfect
  • Applications of continuous time hidden Markov models to the study of misclassified disease outcomes
  • Bias correction of two-state latent Markov process parameter estimates under misclassification
  • Parameter identifiability issues in a latent markov model for misclassified binary responses
  • Comparison of variance estimation approaches in a two-state Markov model for longitudinal data with misclassification
  • Parameter estimation in a model for misclassified Markov data: a Bayesian approach
  • Multistate Markov models for disease progression with classification error
  • Multi-state models for panel data: the msm package for R
  • Composite likelihood methods
  • A note on pseudolikelihood constructed from marginal densities
  • Issues and strategies in the selection of composite likelihoods
  • The analysis of panel data under a Markov assumption
  • Standard errors for EM estimation
  • An optimum property of regular maximum likelihood estimation
  • Probabilities of progression of aortic aneurysms: estimates and implications for screening policy
  • Diagnostic accuracy of coronary angiography and risk factors for post-heart-transplant cardiac allograft vasculopathy
  • An overview of composite likelihood methods
  • Estimating life expectancy in health and ill health by using a hidden Markov model