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Regression analysis of mixed recurrent-event and panel-count data.

Liang Zhu1, Xinwei Tong2, Jianguo Sun3

  • 1Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA.

Biostatistics (Oxford, England)
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This study introduces regression analysis for mixed recurrent-event and panel-count data, offering new estimation procedures for complex event history studies. The methods provide reliable parameter estimation for continuous and periodic monitoring scenarios.

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Estimating equation-based approachMaximum likelihood approachRegression analysis

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Recurrent event data analysis typically involves continuous monitoring (recurrent-event data) or periodic monitoring (panel-count data).
  • Existing methods do not adequately address scenarios with mixed monitoring schemes within a single study.
  • This gap limits comprehensive analysis in event history studies with heterogeneous data collection.

Purpose of the Study:

  • To develop regression analysis methods for mixed recurrent-event and panel-count data.
  • To present and evaluate two novel estimation procedures: maximum likelihood and estimating equations.
  • To establish the asymptotic properties of the proposed estimators for regression parameters.

Main Methods:

  • Developed maximum likelihood estimation (MLE) procedure for mixed data.
  • Developed an estimating equation (EE) procedure for mixed data.
  • Established asymptotic properties for both MLE and EE estimators of regression parameters.

Main Results:

  • The study presents two viable statistical procedures for analyzing mixed recurrent-event and panel-count data.
  • Asymptotic properties of the regression parameter estimators were theoretically established.
  • The methods were successfully applied to real-world data from a Childhood Cancer Survivor Study.

Conclusions:

  • Regression analysis for mixed recurrent-event and panel-count data is feasible and necessary.
  • The proposed maximum likelihood and estimating equation procedures offer robust methods for such data.
  • These methods enhance the analysis of event history data with combined continuous and periodic monitoring.