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

Eric W. Lee, Mimi Kim

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

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
JournalBiometrics
Volume54
Issue number4
DOIs
StatePublished - 1998
Externally publishedYes

Fingerprint

Correlated Data
Continuous-time Model
Panel Data
Markov Model
Simultaneous Inference
Neutrophils
Hemoglobin
Multi-state
Poisons
Covariance matrix
Clinical Trials
Process Model
neutrophils
Covariates
clinical trials
hemoglobin
Count
Hemoglobins
Estimator
Unknown

Keywords

  • Markov model
  • Panel data
  • Simultaneous inference

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Public Health, Environmental and Occupational Health
  • Agricultural and Biological Sciences (miscellaneous)
  • Applied Mathematics
  • Statistics and Probability

Cite this

The analysis of correlated panel data using a continuous-time Markov model. / Lee, Eric W.; Kim, Mimi.

In: Biometrics, Vol. 54, No. 4, 1998, p. 1638-1644.

Research output: Contribution to journalArticle

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