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Recurrent event data analysis with intermittently observed time-varying covariates.

Shanshan Li1, Yifei Sun2, Chiung-Yu Huang2,3

  • 1Department of Biostatistics, Indiana University Fairbanks School of Public Health, Indianapolis, 46202, IN, U.S.A.

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This study introduces a new statistical method for analyzing recurrent event data when covariates are intermittently observed. The proposed estimator accurately assesses the risk of recurrent events, improving upon existing methods.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Survival Analysis

Background:

  • Recurrent event data analysis is crucial in many fields, but methods struggle with intermittently observed time-varying covariates.
  • Current techniques often assume continuous covariate observation, which is unrealistic in many research settings.
  • There's a need for robust methods to estimate covariate effects on recurrent events when observations are periodic.

Purpose of the Study:

  • To develop a novel semiparametric estimator for regression parameters in the proportional rate model.
  • To address the challenge of intermittently observed time-varying covariates in recurrent event data analysis.
  • To provide a statistically rigorous method for estimating the effects of such covariates on recurrent event risk.

Main Methods:

  • Proposed a novel semiparametric estimator for regression parameters within the proportional rate model.
  • Utilized kernel smoothing to estimate the mean covariate process.
  • Derived asymptotic properties, including unbiasedness and normality, and calculated asymptotic variance.

Main Results:

  • The proposed semiparametric estimator demonstrated asymptotic unbiasedness and normal distribution.
  • Simulation studies showed the estimator's performance compared to methods using last-observation-carried-forward.
  • The methods were applied to a real-world study on streptococcus and pharyngitis in Indian school children.

Conclusions:

  • The novel semiparametric estimator effectively handles intermittently observed time-varying covariates in recurrent event analysis.
  • The proposed method offers a statistically sound approach for risk estimation in such scenarios.
  • This work advances the analysis of recurrent event data, particularly in observational studies with periodic covariate monitoring.