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A partial likelihood-based two-dimensional multistate markov model with application to myocardial infarction and stroke recurrence

Author

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  • Chu-Chih Chen

    (Institute of Population Health Sciences, National Health Research Institutes
    Kaohsiung Medical University)

  • , Chuan-Pin Lee

    (Institute of Population Health Sciences, National Health Research Institutes)

  • Yuan-Horng Yan

    (Kuang Tien General Hospital)

  • Tsun-Jen Cheng

    (National Taiwan University)

  • Pranab K. Sen

    (University of North Carolina
    University of North Carolina)

Abstract

Myocardial infarction (MI) and stroke are the most common acute life-threatening cardiovascular disease (CVD) events and are triggered by the rupture of lipid-rich plaques in the arterial wall during the progression of atherosclerosis. Thus, they share a common pathology and preventive interventions. However, the association between the recurrent events of MI and stroke among CVD patients as the disease progresses remains unclear. In this study, we propose a bivariate Markov model to describe the multistate of recurrent events of MI and stroke. A partial likelihood approach was adopted by using the Sarkar's absolutely continuous bivariate exponential distribution (ACBVE) separately for the transitions among different states. The parametric model estimates the hazard function at each state and thus takes more information than an alternative semiparametric approach. As an illustrative example, we analyzed recurrent events of MI and stroke in individuals from the Taiwan National Health Insurance Research Database. Comparisons with the nonparametric Aalen-Johansen estimator for each state showed that the parametric ACBVE explained the data well. The correlation coefficients between the first recurrent MI and stroke tended to increase as the state of disease status progresses. The proposed two-dimensional multistate Markov model may be employed to describe the progressive comorbidity of two associated diseases.

Suggested Citation

  • Chu-Chih Chen & , Chuan-Pin Lee & Yuan-Horng Yan & Tsun-Jen Cheng & Pranab K. Sen, 2021. "A partial likelihood-based two-dimensional multistate markov model with application to myocardial infarction and stroke recurrence," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 282-303, November.
    • Handle: RePEc:spr:sankhb:v:83:y:2021:i:2:d:10.1007_s13571-019-00212-y
    DOI: 10.1007/s13571-019-00212-y
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    References listed on IDEAS

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    1. D. Y. Lin & L. J. Wei & I. Yang & Z. Ying, 2000. "Semiparametric regression for the mean and rate functions of recurrent events," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 711-730.
    2. Murphy, S. A. & Sen, P. K., 1991. "Time-dependent coefficients in a Cox-type regression model," Stochastic Processes and their Applications, Elsevier, vol. 39(1), pages 153-180, October.
    3. Partha Dutta & Gabriel Courties & Ying Wei & Florian Leuschner & Rostic Gorbatov & Clinton S. Robbins & Yoshiko Iwamoto & Brian Thompson & Alicia L. Carlson & Timo Heidt & Maulik D. Majmudar & Felix L, 2012. "Myocardial infarction accelerates atherosclerosis," Nature, Nature, vol. 487(7407), pages 325-329, July.
    4. Andrew C. Titman & Linda D. Sharples, 2010. "Semi-Markov Models with Phase-Type Sojourn Distributions," Biometrics, The International Biometric Society, vol. 66(3), pages 742-752, September.
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