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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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Published on: December 9, 2015

Accelerated Recurrence Time Models.

Yijian Huang1, Limin Peng

  • 1Department of Biostatistics and Bioinformatics, Emory University.

Scandinavian Journal of Statistics, Theory and Applications
|February 18, 2010
PubMed
Summary
This summary is machine-generated.

We introduce accelerated recurrence time models to analyze recurrent events with changing covariate effects. This method, akin to quantile regression, offers a novel approach for frequency-dependent analyses in survival data.

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Published on: October 23, 2020

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Recurrent events are common in medical research, such as disease relapse.
  • Traditional models may not adequately capture evolving risk factors over time.
  • Accurate modeling of recurrent events is crucial for treatment evaluation and prognosis.

Purpose of the Study:

  • To propose a novel statistical model for recurrent event data that accounts for time-varying covariate effects.
  • To develop a robust estimation and inference procedure for the proposed model.
  • To demonstrate the utility and performance of the new methodology.

Main Methods:

  • Generalization of the accelerated failure time (AFT) model to create accelerated recurrence time (ART) models.
  • Incorporation of frequency-dependent coefficients to model evolving covariate effects.
  • Adaptation of Powell's censored quantile regression estimator for estimation and inference.

Main Results:

  • The proposed ART models allow for covariate effects that change with recurrence frequency.
  • Consistency and asymptotic normality of the developed estimator are theoretically established.
  • Simulations confirm the model's good performance in practical scenarios.

Conclusions:

  • Accelerated recurrence time models provide a flexible framework for analyzing recurrent events with evolving covariate effects.
  • The developed methodology offers a statistically sound and computationally efficient approach.
  • The model is effectively demonstrated through an application to bladder cancer recurrence data.