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RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
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Published on: July 17, 2021

A dynamic Mover-Stayer model for recurrent event processes subject to resolution.

Hua Shen1, Richard J Cook

  • 1Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, N2L 3G1, Canada, hshen@uwaterloo.ca.

Lifetime Data Analysis
|June 21, 2013
PubMed
Summary

This study introduces a dynamic Mover-Stayer model to analyze recurrent affective disorder exacerbations. The model helps identify risk factors and understand disease resolution patterns over time.

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Area of Science:

  • Psychiatry and Biostatistics
  • Mathematical Modeling of Health Events

Background:

  • Recurrent symptomatic exacerbations requiring hospitalization are common in affective disorders.
  • Understanding the temporal patterns and risk factors of these events is crucial for patient management.
  • Some patients exhibit temporally clustered exacerbations that eventually cease, suggesting disease resolution.

Purpose of the Study:

  • To develop a novel dynamic Mover-Stayer model for analyzing recurrent exacerbations in affective disorders.
  • To incorporate a latent variable indicating disease resolution, stopping further events.
  • To identify risk factors associated with the occurrence and clustering of exacerbations.

Main Methods:

  • Development of a discrete-time dynamic Mover-Stayer model.
  • Utilizing a latent intensity-based point process to model event occurrences.
  • Application of an expectation-maximization algorithm for parametric and semiparametric model fitting.

Main Results:

  • The model effectively captures the cessation of events after a period, indicating disease resolution.
  • It allows for the identification of factors influencing the onset and resolution of affective disorder exacerbations.
  • Demonstrates a framework for analyzing time-to-event data with a resolution component.

Conclusions:

  • The dynamic Mover-Stayer model provides a robust framework for studying recurrent events in affective disorders.
  • This approach enhances understanding of disease trajectories and the concept of resolution.
  • Facilitates identification of prognostic markers and personalized treatment strategies.