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Conditional Rate Derivation in the Presence of Intervening Variables Using a Markov Chain

Author

Listed:
  • Richard H. Shachtman

    (University of North Carolina, Chapel Hill, North Carolina)

  • John R. Schoenfelder

    (Burroughs Wellcome Company, Research Triangle Park, North Carolina)

  • Carol J. Hogue

    (Centers for Disease Control, Atlanta, Georgia)

Abstract

When conducting inferential and epidemiologic studies, researchers are often interested in the distribution of time until the occurrence of some specified event, a form of incidence calculation. Furthermore, this interest often extends to the effects of intervening factors on this distribution. In this paper we impose the assumption that the phenomena being investigated are governed by a stationary Markov chain and review how one may estimate the above distribution. We then introduce and relate two different methods of investigating the effects of intervening factors. In particular, we show how an investigator may evaluate the effect of potential intervention programs. Finally, we demonstrate the proposed methodology using data from a population study.

Suggested Citation

  • Richard H. Shachtman & John R. Schoenfelder & Carol J. Hogue, 1982. "Conditional Rate Derivation in the Presence of Intervening Variables Using a Markov Chain," Operations Research, INFORMS, vol. 30(6), pages 1070-1081, December.
    • Handle: RePEc:inm:oropre:v:30:y:1982:i:6:p:1070-1081
    DOI: 10.1287/opre.30.6.1070
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