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Estimation of multi-state models with missing covariate values based on observed data likelihood |
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| Authors: | Wenjie Lou Erin L. Abner Lijie Wan David W. Fardo Richard Lipton Mindy Katz |
| Affiliation: | 1. Department of Statistics, University of Kentucky, Lexington, Kentucky, USA;2. Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, USA;3. Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, USA;4. Department of Epidemiology, University of Kentucky, Lexington, Kentucky, USA;5. Department of Biostatistics, University of Kentucky, Lexington, Kentucky, USA;6. Department of Neurology, Albert Einstein College of Medicine, New York City, New York, USA |
| Abstract: | AbstractContinuous-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. |
| Keywords: | Longitudinal data multi-state model missing covariate MAR MCAR |