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A continuous-time Markov chain approach with the analytic likelihood in studies of behavioral changes

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  • Ho-Lan Peng
  • Andrew Aschenbrenner
  • Kirk von Sternberg
  • Patricia D. Mullen
  • Wenyaw Chan

Abstract

We develop an analytic likelihood approach for a four-state CTMC by solving the backwards Kolmogorov differential equations, reducing this bias in transition rate estimates. A simulation study is performed to assess the performance of this new method and confirms that it achieves good coverage probabilities with low bias and standard errors. Finally, we analyzed data from Project SUCCESS to estimate the study each participant’s transitions among behavioral stage, consisting of risky drinking and possible ineffective using of contraception, which comprise the primary endpoint of risk of an alcohol-exposed pregnancy.

Suggested Citation

  • Ho-Lan Peng & Andrew Aschenbrenner & Kirk von Sternberg & Patricia D. Mullen & Wenyaw Chan, 2019. "A continuous-time Markov chain approach with the analytic likelihood in studies of behavioral changes," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(23), pages 5756-5765, December.
  • Handle: RePEc:taf:lstaxx:v:48:y:2019:i:23:p:5756-5765
    DOI: 10.1080/03610926.2018.1520886
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