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Dynamic analysis of recurrent event data using the additive hazard model.

Johan Fosen1, Ornulf Borgan, Harald Weedon-Fekjaer

  • 1Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, N-0317 Oslo. johan.fosen@medisin.uio.no

Biometrical Journal. Biometrische Zeitschrift
|July 19, 2006
PubMed
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This study introduces a new method for analyzing recurrent event data, incorporating past events as dynamic predictors. This approach ensures unbiased estimation of treatment effects while analyzing the impact of previous occurrences.

Area of Science:

  • Biostatistics
  • Epidemiology
  • Survival Analysis

Background:

  • Recurrent event data analysis is complex.
  • Incorporating prior event occurrences as covariates is challenging.
  • Unbiased estimation of treatment effects alongside dynamic covariates is crucial.

Purpose of the Study:

  • To develop a method for analyzing recurrent event data.
  • To effectively utilize information on previous event occurrences as time-dependent covariates.
  • To ensure unbiased estimation of treatment and fixed covariate effects in the presence of dynamic covariates.

Main Methods:

  • Utilized an additive regression model for the intensity of recurrent events.
  • Defined direct, indirect, and total effects analogous to path analysis.

Related Experiment Videos

  • Employed theoretical considerations and simulation studies.

    • Main Results:

    • The proposed method allows for the analysis of dynamic covariates in recurrent event data.
    • Demonstrated unbiased estimation of fixed covariate effects.
    • Illustrated the methodology with a real-world data set.

    Conclusions:

    • The developed method provides a robust framework for recurrent event data analysis.
    • Enables nuanced understanding of covariate effects, including dynamic influences.
    • Applicable to various fields dealing with repeated events, such as medical research.