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Alternating event processes during lifetimes: population dynamics and statistical inference

Author

Listed:
  • Russell T. Shinohara

    (University of Pennsylvania)

  • Yifei Sun

    (Johns Hopkins University)

  • Mei-Cheng Wang

    (Johns Hopkins University)

Abstract

In the literature studying recurrent event data, a large amount of work has been focused on univariate recurrent event processes where the occurrence of each event is treated as a single point in time. There are many applications, however, in which univariate recurrent events are insufficient to characterize the feature of the process because patients experience nontrivial durations associated with each event. This results in an alternating event process where the disease status of a patient alternates between exacerbations and remissions. In this paper, we consider the dynamics of a chronic disease and its associated exacerbation-remission process over two time scales: calendar time and time-since-onset. In particular, over calendar time, we explore population dynamics and the relationship between incidence, prevalence and duration for such alternating event processes. We provide nonparametric estimation techniques for characteristic quantities of the process. In some settings, exacerbation processes are observed from an onset time until death; to account for the relationship between the survival and alternating event processes, nonparametric approaches are developed for estimating exacerbation process over lifetime. By understanding the population dynamics and within-process structure, the paper provide a new and general way to study alternating event processes.

Suggested Citation

  • Russell T. Shinohara & Yifei Sun & Mei-Cheng Wang, 2018. "Alternating event processes during lifetimes: population dynamics and statistical inference," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(1), pages 110-125, January.
    • Handle: RePEc:spr:lifeda:v:24:y:2018:i:1:d:10.1007_s10985-017-9404-5
    DOI: 10.1007/s10985-017-9404-5
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    References listed on IDEAS

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    1. Yining Ye & John D. Kalbfleisch & Douglas E. Schaubel, 2007. "Semiparametric Analysis of Correlated Recurrent and Terminal Events," Biometrics, The International Biometric Society, vol. 63(1), pages 78-87, March.
    2. Chiung-Yu Huang & Mei-Cheng Wang, 2004. "Joint Modeling and Estimation for Recurrent Event Processes and Failure Time Data," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1153-1165, December.
    3. 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.
    4. Pena E.A. & Strawderman R.L. & Hollander M., 2001. "Nonparametric Estimation With Recurrent Event Data," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1299-1315, December.
    5. Yan, Jun & Fine, Jason P., 2008. "Analysis of Episodic Data With Application to Recurrent Pulmonary Exacerbations in Cystic Fibrosis Patients," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 498-510, June.
    6. Wang M-C. & Qin J. & Chiang C-T., 2001. "Analyzing Recurrent Event Data With Informative Censoring," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1057-1065, September.
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    Cited by:

    1. Douglas E. Schaubel & Bin Nan, 2018. "Special issue dedicated to Jack Kalbfleisch," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(1), pages 1-2, January.
    2. Benny Ren & Ian Barnett, 2023. "Combining mixed effects hidden Markov models with latent alternating recurrent event processes to model diurnal active–rest cycles," Biometrics, The International Biometric Society, vol. 79(4), pages 3402-3417, December.

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