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Additive mixed effect model for recurrent gap time data.

Jieli Ding1, Liuquan Sun2

  • 1School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, Hubei, People's Republic of China.

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Summary
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This study introduces a new additive mixed effects model to analyze recurrent event gap times, accounting for individual patient differences. This approach improves statistical modeling for time-to-event data in medical research.

Keywords:
Additive mixed effect modelEstimating equationGap timesRandom effectsRecurrent event data

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Area of Science:

  • Biostatistics
  • Medical Statistics
  • Survival Analysis

Background:

  • Recurrent event gap times are crucial in medical and observational studies.
  • The marginal additive hazards model is common but ignores dependence among gap times.
  • Existing models may not fully capture the complexity of repeated event data.

Purpose of the Study:

  • To propose an additive mixed effects model for analyzing gap time data.
  • To incorporate subject-specific random effects to account for dependence among gap times.
  • To provide a statistically robust method for recurrent event analysis.

Main Methods:

  • Development of an additive mixed effects model for gap time analysis.
  • Utilizing estimating equation approaches for parameter estimation.
  • Establishing asymptotic properties of the estimators and presenting model-checking procedures.

Main Results:

  • The proposed model effectively accounts for the dependence among gap times using random effects.
  • Estimating equations provide reliable parameter estimation with established asymptotic properties.
  • Simulation studies demonstrate the finite sample performance of the proposed methods.

Conclusions:

  • The additive mixed effects model offers an improved approach for analyzing recurrent event gap times.
  • The method is validated through simulations and a real-world clinical study application.
  • This model enhances the analysis of time-to-event data where events recur within subjects.