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Estimation in a Markov chain regression model with missing covariates

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Probability, Statistics and Modelling in Public Health

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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.

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Author information

Authors and Affiliations

  1. Department of Biostatistics, University of California, Los Angeles, CA, 90095-1772

    Dorota M. Dabrowska & Robert M. Elashoff

  2. John Wayne Cancer Institute, Santa Monica, CA, 90404

    Donald L. Morton

Authors
  1. Dorota M. Dabrowska
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  2. Robert M. Elashoff
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  3. Donald L. Morton
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Editor information

Editors and Affiliations

  1. Université Victor Segalin Bordeaux 2, France

    Mikhail Nikulin  & Daniel Commenges  & 

  2. V. Steklov Mathematical Institute, Saint Petersburg, Russia

    Mikhail Nikulin

  3. Université René Descartes, Paris, France

    Catherine Huber

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© 2006 Springer Science+Business Media, Inc.

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Dabrowska, D.M., Elashoff, R.M., Morton, D.L. (2006). Estimation in a Markov chain regression model with missing covariates. In: Nikulin, M., Commenges, D., Huber, C. (eds) Probability, Statistics and Modelling in Public Health. Springer, Boston, MA. https://doi.org/10.1007/0-387-26023-4_7

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