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Methods for Estimating Center Effects on Recurrent Events.

Dandan Liu1, John D Kalbfleisch2, Douglas E Schaubel2

  • 1Department of Biostatistics, Vanderbilt University School of Medicine 1161 21st Avenue South, Nashville, TN 37232, USA dandan.liu@vanderbilt.edu.

Statistics in Biosciences
|July 1, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to quantify center effects in recurrent event data, improving analysis for large numbers of centers and events. The approach estimates center effects using observed-to-expected event ratios, applicable even with terminating events.

Keywords:
Center effectsProportional rates modelRecurrent event dataTerminating event

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Area of Science:

  • Biostatistics
  • Health Services Research
  • Epidemiology

Background:

  • Recurrent event data analysis is crucial in healthcare.
  • Quantifying center effects is challenging with traditional methods, especially with many centers.
  • Existing models often struggle with large datasets and numerous event times.

Purpose of the Study:

  • To develop a novel, feasible method for quantifying center effects in recurrent event data.
  • To address limitations of traditional parametric models with many centers.
  • To provide a robust estimation technique applicable to complex event data.

Main Methods:

  • Proposed a new estimation method avoiding indicator variables for centers.
  • Utilized the ratio of observed to expected cumulative events for consistent estimation.
  • Extended the methodology to accommodate terminating events that permanently stop recurrent events.

Main Results:

  • Demonstrated consistent estimation of center effects using the proposed ratio method.
  • Developed large sample results for the novel estimators.
  • Validated the finite-sample properties through simulation studies.

Conclusions:

  • The new method offers a feasible and consistent approach to quantifying center effects in recurrent event data.
  • The method is effective even when dealing with a large number of centers and complex event sequences.
  • Applied successfully to real-world data, such as end-stage renal disease patient hospital admissions.