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A two-part mixed-effects model for analyzing clustered time-to-event data with clumping at zero.

Jian Zhao1, Yun Zhao2, Liming Xiang3

  • 1Department of Epidemiology and Biostatistics, School of Public Health, Curtin University, Perth, Australia; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.

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This study introduces a novel two-part mixed-effects model to analyze clustered time-to-event data with zero-inflated outcomes. The proposed method effectively utilizes all data, outperforming traditional survival analysis for epidemiological studies.

Keywords:
Clumping at zeroFrailty modelMixed-effectsTime-to-event dataTwo-part model

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Longitudinal studies often have zero-inflated time-to-event outcomes, losing valuable data in standard survival analysis.
  • Clustered observations within groups (e.g., hospitals) introduce correlation, further complicating analysis.
  • Existing two-part models are rarely applied to clustered time-to-event data with clumping at zero.

Purpose of the Study:

  • To propose and validate a two-part mixed-effects modeling approach for clustered time-to-event data with excess zeros.
  • To effectively utilize all available data, including baseline negative responses and follow-up events.
  • To address the challenge of correlated observations within clusters in time-to-event analyses.

Main Methods:

  • A two-part mixed-effects model combining logistic regression for baseline prevalence and parametric frailty models for time-to-event outcomes.
  • Incorporation of correlated random effects to account for within-cluster correlation and correlation between the two model parts.
  • Application to exclusive breastfeeding data from a community-based prospective cohort study in Nepal.

Main Results:

  • A significant positive correlation (ρ = 0.67, P < 0.001) was found between baseline exclusive breastfeeding prevalence and duration.
  • The proposed correlated two-part model demonstrated superior performance compared to an independent two-part model (LR test = 8.6, P = 0.003).

Conclusions:

  • The novel approach fully leverages all data, offering an advantage over conventional survival analysis for zero-inflated clustered time-to-event data.
  • This methodology is applicable beyond breastfeeding studies to diverse research areas facing similar data structures.