Abstract
Continuous-time multi-state models are commonly used to study diseases with multiple stages. Potential risk factors associated with the disease are added to the transition intensities of the model as covariates, but missing covariate measurements arise frequently in practice. We propose a likelihood-based method that deals efficiently with a missing covariate in these models. Our simulation study showed that the method performs well for both “missing completely at random” and “missing at random” mechanisms. We also applied our method to a real dataset, the Einstein Aging Study.
Original language | English |
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Pages (from-to) | 5733-5747 |
Number of pages | 15 |
Journal | Communications in Statistics - Theory and Methods |
Volume | 48 |
Issue number | 23 |
DOIs | |
State | Published - Dec 2 2019 |
Bibliographical note
Publisher Copyright:© 2018, © 2018 Taylor & Francis Group, LLC.
Keywords
- Longitudinal data
- MAR
- MCAR
- missing covariate
- multi-state model
ASJC Scopus subject areas
- Statistics and Probability