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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Related Experiment Video

Updated: Sep 7, 2025

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
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Analysis of cyclic recurrent event data with multiple event types.

Chien-Lin Su1, Feng-Chang Lin2

  • 1Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada.

Japanese Journal of Statistics and Data Science
|June 17, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical model for analyzing recurrent event data with periodic patterns. Incorporating cyclic components significantly improves the predictability of event occurrences compared to traditional models.

Keywords:
Cyclic baseline rate functionFire serviceGeneralized estimating equationsMarginal rate modelPrediction

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Area of Science:

  • Statistics
  • Survival Analysis
  • Time Series Analysis

Background:

  • Recurrent event data are common in various fields.
  • Some event processes exhibit cyclic or periodic patterns.
  • Existing models may not adequately capture these temporal features.

Purpose of the Study:

  • To propose a semiparametric rate model for multiple recurrent event types with cyclic components.
  • To estimate regression coefficients and assess model predictability.
  • To compare the performance of the proposed model against a conventional model.

Main Methods:

  • Utilized generalized estimating equations for coefficient estimation.
  • Employed a fully nonparametric estimator to profile the baseline rate function.
  • Conducted simulation experiments to evaluate finite-sample behavior.
  • Applied the model to recurrent fire alarm data.

Main Results:

  • The proposed estimators are consistent and asymptotically Gaussian.
  • The model with cyclic components demonstrates improved predictability.
  • The semiparametric rate model effectively handles multiple event types with periodic features.

Conclusions:

  • The developed semiparametric model is a valuable tool for analyzing recurrent events with cyclic patterns.
  • Incorporating cyclic components enhances the predictive power for such data.
  • The model provides a robust framework for understanding and forecasting events with temporal periodicity.