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Quantifying Direct and Indirect Effects through Joint Modeling of Terminal Events and Gap Times between Recurrent

Fang Niu1, Cheng Zheng1, Lei Liu2

  • 1Department of Biostatistics, University of Nebraska Medical Center, Omaha, Nebraska, U.S.A.

Journal of Data Science : JDS
|April 20, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new joint modeling approach for recurrent events and survival, using gap times to better capture varying covariate effects. The method accurately estimates mediation effects and is applied to AIDS patient data.

Keywords:
Causal inferenceFrailty modelMediation analysisSurvival data

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Joint models typically use a total time scale for recurrent events, assuming constant covariate effects.
  • Modeling gap times between recurrent events offers greater flexibility and accuracy, especially when early events differ from later ones.
  • Limited research exists on mediation analysis using joint modeling of gap times and survival outcomes.

Purpose of the Study:

  • To propose a novel joint modeling approach for mediation analysis of recurrent events on survival outcomes by modeling recurrent events via gap times.
  • To accommodate situations where the first recurrent event has different characteristics than subsequent events.
  • To address unmeasured confounding using a relaxed "sequential ignorability" assumption.

Main Methods:

  • Developed a joint modeling framework that models recurrent events using gap times.
  • Incorporated a relaxed "sequential ignorability" assumption to handle unmeasured confounding.
  • Conducted simulation studies to assess model performance.

Main Results:

  • The proposed model demonstrates good performance in estimating both model parameters and mediation effects.
  • Simulations confirm the model's ability to handle varying covariate effects in recurrent events.
  • The method successfully applied to an AIDS study.

Conclusions:

  • The novel joint modeling approach using gap times provides a flexible and accurate method for mediation analysis in the presence of recurrent events.
  • The approach is valuable for understanding complex relationships between recurrent events, survival, and covariates, as demonstrated in the AIDS study.
  • The relaxed "sequential ignorability" assumption offers a practical way to address unobserved confounding in such models.