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Regression analysis of general mixed recurrent event data

Author

Listed:
  • Ryan Sun

    (University of Texas MD Anderson Cancer Center)

  • Dayu Sun

    (Emory University Rollins School of Public Health)

  • Liang Zhu

    (Eisai US)

  • Jianguo Sun

    (University of Missouri)

Abstract

In modern biomedical datasets, it is common for recurrent outcomes data to be collected in an incomplete manner. More specifically, information on recurrent events is routinely recorded as a mixture of recurrent event data, panel count data, and panel binary data; we refer to this structure as general mixed recurrent event data. Although the aforementioned data types are individually well-studied, there does not appear to exist an established approach for regression analysis of the three component combination. Often, ad-hoc measures such as imputation or discarding of data are used to homogenize records prior to the analysis, but such measures lead to obvious concerns regarding robustness, loss of efficiency, and other issues. This work proposes a maximum likelihood regression estimation procedure for the combination of general mixed recurrent event data and establishes the asymptotic properties of the proposed estimators. In addition, we generalize the approach to allow for the existence of terminal events, a common complicating feature in recurrent event analysis. Numerical studies and application to the Childhood Cancer Survivor Study suggest that the proposed procedures work well in practical situations.

Suggested Citation

  • Ryan Sun & Dayu Sun & Liang Zhu & Jianguo Sun, 2023. "Regression analysis of general mixed recurrent event data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(4), pages 807-822, October.
    • Handle: RePEc:spr:lifeda:v:29:y:2023:i:4:d:10.1007_s10985-023-09604-9
    DOI: 10.1007/s10985-023-09604-9
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    References listed on IDEAS

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    1. Clare Bycroft & Colin Freeman & Desislava Petkova & Gavin Band & Lloyd T. Elliott & Kevin Sharp & Allan Motyer & Damjan Vukcevic & Olivier Delaneau & Jared O’Connell & Adrian Cortes & Samantha Welsh &, 2018. "The UK Biobank resource with deep phenotyping and genomic data," Nature, Nature, vol. 562(7726), pages 203-209, October.
    2. Liang Zhu & Ying Zhang & Yimei Li & Jianguo Sun & Leslie L. Robison, 2018. "A semiparametric likelihood†based method for regression analysis of mixed panel†count data," Biometrics, The International Biometric Society, vol. 74(2), pages 488-497, June.
    3. Liang Zhu & Sangbum Choi & Yimei Li & Xuelin Huang & Jianguo Sun & Leslie L. Robison, 2020. "Statistical analysis of clustered mixed recurrent-event data with application to a cancer survivor study," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 820-832, October.
    4. Lei Liu & Xuelin Huang & Alex Yaroshinsky & Janice N. Cormier, 2016. "Joint frailty models for zero-inflated recurrent events in the presence of a terminal event," Biometrics, The International Biometric Society, vol. 72(1), pages 204-214, March.
    5. Guanglei Yu & Yang Li & Liang Zhu & Hui Zhao & Jianguo Sun & Leslie L. Robison, 2019. "An additive–multiplicative mean model for panel count data with dependent observation and dropout processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 46(2), pages 414-431, June.
    6. Liang Zhu & Hui Zhao & Jianguo Sun & Wendy Leisenring & Leslie L. Robison, 2015. "Regression analysis of mixed recurrent-event and panel-count data with additive rate models," Biometrics, The International Biometric Society, vol. 71(1), pages 71-79, March.
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