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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Panel count data are common in recurrent event processes observed at discrete time points.
  • Limited software implementations exist for analyzing such data, especially concerning covariate effects.
  • Recurrent event data analysis is crucial in fields like clinical trials and reliability engineering.

Purpose of the Study:

  • To review semiparametric regression modeling approaches for panel count data.
  • To highlight practical implementations available in the R package spef.
  • To focus on analyzing the effects of time-independent covariates on recurrent events.

Main Methods:

  • Review of semiparametric regression models for panel count data.
  • Categorization of methods based on the association of examination times with the event process.
  • Illustration using data from a skin cancer clinical trial.

Main Results:

  • The R package spef provides practical software implementations for reviewed methods.
  • Methods are grouped based on covariate-event process association, aiding practical application.
  • The utility of these methods is demonstrated through a real-world clinical trial example.

Conclusions:

  • Semiparametric regression models offer robust approaches for panel count data analysis.
  • The R package spef enhances the accessibility and practical application of these statistical methods.
  • Understanding covariate effects in recurrent event processes is vital for various research areas.