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Estimation in a Markov chain regression model with missing covariates

In: Probability, Statistics and Modelling in Public Health

Author

Listed:
  • Dorota M. Dabrowska

    (University of California, Department of Biostatistics)

  • Robert M. Elashoff

    (University of California, Department of Biostatistics)

  • Donald L. Morton

    (John Wayne Cancer Institute)

Abstract

Summary Markov chain proportional hazard regression model provides a powerful tool for analysis of multiple event times. We discuss estimation in absorbing Markov chains with missing covariates. We consider a MAR model assuming that the missing data mechanism depends on the observed covariates, as well as the number of events observed in a given time period, their types and times of their occurrence. For estimation purposes we use a piecewise constant intensity regression model.

Suggested Citation

  • Dorota M. Dabrowska & Robert M. Elashoff & Donald L. Morton, 2006. "Estimation in a Markov chain regression model with missing covariates," Springer Books, in: Mikhail Nikulin & Daniel Commenges & Catherine Huber (ed.), Probability, Statistics and Modelling in Public Health, pages 90-118, Springer.
    • Handle: RePEc:spr:sprchp:978-0-387-26023-5_7
    DOI: 10.1007/0-387-26023-4_7
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