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Inverse Probability Weighting for Recurrent Event Models.

Jiren Sun1, Tobias Mütze2, Tianmeng Lyu3

  • 1Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, USA.

Statistics in Medicine
|April 6, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces new methods for analyzing recurrent events in clinical trials, accounting for other health events. The approach improves estimation of treatment effects, offering better bias and power in simulations.

Keywords:
Lin‐Wei‐Yang‐Ying (LWYY)hypothetical estimandsintercurrent eventsinverse probability weighting (IPW)negative binomialrecurrent events

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Epidemiology

Background:

  • Recurrent events are key clinical trial endpoints across many diseases.
  • Intercurrent events complicate the interpretation of treatment effects on recurrent outcomes.
  • Estimating hypothetical treatment effects, assuming no intercurrent events, is clinically relevant.

Purpose of the Study:

  • To propose novel statistical estimators for hypothetical treatment effects in recurrent event data.
  • To address confounding from baseline and time-varying covariates influenced by intercurrent events.

Main Methods:

  • Utilized inverse probability weighting (IPW) for adjustment.
  • Applied IPW to established Lin-Wei-Yang-Ying (LWYY) and negative binomial (NB) models.
  • Incorporated adjustments for baseline and internal time-varying covariates.

Main Results:

  • Proposed IPW estimators demonstrated superior performance in simulation studies.
  • The new methods showed reduced bias and increased power compared to alternatives.
  • Accurate estimation of hypothetical treatment effects was achieved.

Conclusions:

  • The developed estimators provide a robust method for analyzing recurrent events in the presence of intercurrent events.
  • This approach enhances the validity and precision of treatment effect estimation in clinical trials.
  • The findings support the use of these methods for more reliable clinical trial data interpretation.