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Regression Analysis of Mixed Panel-Count Data with Application to Cancer Studies

Author

Listed:
  • Yimei Li

    (St. Jude Children’s Research Hospital)

  • Liang Zhu

    (The University of Texas Health Science Center at Houston)

  • Lei Liu

    (Washington University in St. Louis)

  • Leslie L. Robison

    (St. Jude Children’s Research Hospital)

Abstract

Both panel-count data and panel-binary data are common data types in recurrent event studies. Because of inconsistent questionnaires or missing data during the follow-ups, mixed data types need to be addressed frequently. A recently proposed semiparametric approach uses a proportional means model to facilitate regression analyses of mixed panel-count and panel-binary data. This method can use all available information regardless of the record type and provide unbiased estimates. However, the large number of nuisance parameters in the nonparametric baseline hazard function makes the estimating procedure very complicated and time-consuming. We approximated the baseline hazard function to simplify the estimating procedure. Simulation studies showed that our method performed similarly to that of the previous semiparametric likelihood-based method, but with much faster speed. Approximating the baseline hazard not only reduced the computational burden but also made it possible to implement the estimating procedure in a standard software, such as SAS.

Suggested Citation

  • Yimei Li & Liang Zhu & Lei Liu & Leslie L. Robison, 2021. "Regression Analysis of Mixed Panel-Count Data with Application to Cancer Studies," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(1), pages 178-195, April.
    • Handle: RePEc:spr:stabio:v:13:y:2021:i:1:d:10.1007_s12561-020-09291-2
    DOI: 10.1007/s12561-020-09291-2
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    References listed on IDEAS

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