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 journalArticle

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 languageEnglish (US)
Pages (from-to)5733-5747
Number of pages15
JournalCommunications in Statistics - Theory and Methods
Volume48
Issue number23
DOIs
StatePublished - Dec 2 2019

Keywords

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

ASJC Scopus subject areas

  • Statistics and Probability

Fingerprint Dive into the research topics of 'Estimation of multi-state models with missing covariate values based on observed data likelihood'. Together they form a unique fingerprint.

  • Cite this