Estimation of multi-state models with missing covariate values based on observed data likelihood

Wenjie Lou, Erin L. Abner, Lijie Wan, David W. Fardo, Richard Lipton, Mindy Katz, Richard J. Kryscio

Research output: Contribution to journalArticlepeer-review


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 languageEnglish
Pages (from-to)5733-5747
Number of pages15
JournalCommunications in Statistics - Theory and Methods
Issue number23
StatePublished - Dec 2 2019

Bibliographical note

Funding Information:
This research was partially funded with support from the following grants to the University of Kentucky’s Center on Aging: R01 AG038651, P30 AG028383, K25 AG043546, and R01 AG019241 from the National Institute on Aging, as well as a grant to the University of Kentucky’s Center for Clinical and Translational Science, UL1TR000117, from the National Center for Advancing Translational Sciences. The Einstein Aging Study is supported by P01 AG003949.

Publisher Copyright:
© 2018, © 2018 Taylor & Francis Group, LLC.


  • Longitudinal data
  • MAR
  • MCAR
  • missing covariate
  • multi-state model

ASJC Scopus subject areas

  • Statistics and Probability


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