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Summary
This summary is machine-generated.

This study introduces a flexible semiparametric model for recurrent event analysis using single-index models. The novel framework enhances interpretability and offers robust estimation methods for time-to-event data.

Keywords:
Dimension reductionInformative censoringKernel smoothingRate functionRecurrent event

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Single-index models are increasingly popular in time-to-event analysis for their flexibility and dimension reduction capabilities.
  • Recurrent event data analysis presents unique challenges in modeling event rates over time.
  • Existing models may lack interpretability or struggle with complex covariate effects.

Purpose of the Study:

  • To propose a novel semiparametric framework for the rate function of recurrent event counting processes.
  • To model the size and shape components of the rate function using single-index models.
  • To ensure directional interpretability of covariate effects and encompass existing models.

Main Methods:

  • Utilizing single-index models for both size and shape components of the rate function.
  • Imposing monotone constraints on link functions for enhanced interpretability.
  • Developing a two-step, rank-based estimation procedure for regression parameters, accommodating informative censoring.
  • Introducing hypothesis testing for shape and size independence to guide model selection.

Main Results:

  • The proposed semiparametric model offers enhanced interpretability for recurrent event data.
  • The two-step estimation procedure yields asymptotically normal estimators with a root-n convergence rate.
  • The methodology is validated through simulation studies and a real-world application in hematopoietic stem cell transplantation.

Conclusions:

  • The proposed framework provides a flexible and interpretable approach to modeling recurrent event data.
  • The developed estimation and testing procedures are statistically sound and practically applicable.
  • This methodology advances time-to-event analysis, particularly for complex recurrent event processes.