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Ascertainment correction in frailty models for recurrent events data.

Theodor A Balan1, Marianne A Jonker2, Paul C Johannesma3

  • 1Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, The Netherlands.

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Ignoring selection bias in recurrent event studies can bias results. This paper proposes a simple likelihood adjustment to obtain unbiased estimators for covariate effects and incidence in Andersen-Gill and shared frailty models.

Keywords:
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Area of Science:

  • Biostatistics
  • Epidemiology
  • Survival Analysis

Background:

  • Retrospective studies with recurrent events often select individuals based on past event history.
  • This ascertainment can lead to non-representative samples, introducing selection bias.
  • Ignoring this bias can distort statistical inference.

Purpose of the Study:

  • To investigate the impact of unadjusted data analysis in the presence of selection bias.
  • To propose a corrected statistical method for analyzing recurrent event data with ascertainment.
  • To provide unbiased and consistent estimators for regression models.

Main Methods:

  • Utilized Andersen-Gill and shared frailty regression models.
  • Investigated the consequences of ignoring selection mechanisms in data analysis.
  • Developed a simple likelihood adjustment for corrected analysis.
  • Assessed the proposed method through simulation studies.

Main Results:

  • Failure to adjust for ascertainment introduces bias in estimators of covariate effects, incidence, and frailty variance.
  • The proposed likelihood adjustment yields unbiased and consistent estimators.
  • Simulation results confirm the effectiveness of the corrected method.

Conclusions:

  • Selection bias is a critical issue in retrospective recurrent event studies.
  • A simple likelihood adjustment effectively corrects for ascertainment bias.
  • The proposed method ensures reliable statistical inference in such studies.