The analysis of correlated panel data using a continuous-time Markov model

Eric W. Lee, Mimi Y. Kim

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


We consider the analysis of correlated panel data in which two or more correlated multistate processes are periodically observed on each individual and the exact transition times between states are unknown. We describe a procedure that models each process marginally under a time-homogeneous Markov model allowing for covariates. The resulting estimators are shown to be asymptotically jointly normal with a covariance matrix that can be consistently estimated. Simultaneous inference procedures are also proposed. Methods are illustrated using data from an AIDS clinical trial to compare the toxic effects of two treatments on two hematologic variables, hemoglobin and absolute neutrophil count.

Original languageEnglish (US)
Pages (from-to)1638-1644
Number of pages7
Issue number4
StatePublished - 1998
Externally publishedYes


  • Markov model
  • Panel data
  • Simultaneous inference

ASJC Scopus subject areas

  • Statistics and Probability
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences
  • Applied Mathematics


Dive into the research topics of 'The analysis of correlated panel data using a continuous-time Markov model'. Together they form a unique fingerprint.

Cite this