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Statistical Inference for Counting Processes Under Shape Heterogeneity.

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Summary
This summary is machine-generated.

This study introduces new methods to analyze recurrent event data when the proportional rate assumption is violated. The approach effectively estimates both shape and size parameters for covariate effects, improving statistical modeling.

Keywords:
dimension reductionkernel smoothingrecurrent event processsingle index model

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Proportional rate models are widely used for recurrent event data analysis.
  • The proportional rate assumption restricts covariate effects to magnitude changes, not shape modifications.
  • Violation of this assumption necessitates alternative modeling strategies.

Purpose of the Study:

  • To propose a novel statistical framework for analyzing recurrent event data when the proportional rate assumption fails.
  • To characterize covariate effects on both the shape and magnitude of the rate function.
  • To develop robust estimation methods for these complex covariate effects.

Main Methods:

  • Introduced shape and size parameters to model flexible covariate effects on the rate function.
  • Proposed a conditional pseudolikelihood approach to estimate shape parameters by eliminating size parameters.
  • Utilized an event count projection approach for estimating size parameters.

Main Results:

  • The proposed estimators for shape and size parameters are asymptotically normal with a root-n convergence rate.
  • Simulation studies demonstrated the effectiveness of the new methods.
  • Application to SEER-Medicare data on recurrent hospitalizations showcased practical utility.

Conclusions:

  • The developed methods provide a flexible and interpretable way to analyze recurrent event data beyond the proportional rates assumption.
  • This framework enhances the understanding of covariate impacts on event rates over time.
  • The approach is validated through simulations and real-world healthcare data analysis.