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Related Experiment Video

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Joint Model for Interval-Censored Semicompeting Events and Longitudinal Data With Subject-Specific Within- and

Léonie Courcoul1, Catherine Helmer1, Antoine Barbieri1

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|March 27, 2026
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Summary
This summary is machine-generated.

High blood pressure variability is a dementia risk factor. This study introduces a novel statistical model to better assess intra- and intervisit blood pressure variations and their impact on dementia and death risk.

Keywords:
blood pressuredementiaheteroskedasticityillness‐death modelinterval censoring

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Area of Science:

  • Neurology
  • Biostatistics
  • Epidemiology

Background:

  • Dementia affects 50 million globally, with numbers increasing.
  • No cure exists; prevention focuses on modifiable cardiovascular risk factors.
  • High blood pressure and its variability are implicated as dementia risk factors, but studies have methodological limitations.

Purpose of the Study:

  • To propose a novel joint statistical model for analyzing blood pressure variability.
  • To differentiate between short-term (intravisit) and long-term (intervisit) blood pressure variability.
  • To assess the impact of these variabilities on dementia and death risk.

Main Methods:

  • A joint model combining mixed-effects and illness-death models was developed.
  • The model incorporates subject-specific random effects for both intervisit and intravisit variances.
  • It allows risks to depend on marker value, slope, and residual variance components, handling interval censoring and semicompeting risks.

Main Results:

  • A simulation study validated the proposed estimation procedure.
  • The model was implemented in an R package for practical application.
  • Analysis of the Three-City (3C) cohort data provided insights into blood pressure variability and dementia/death risk.

Conclusions:

  • The developed joint model offers a robust framework for analyzing blood pressure variability in relation to dementia and mortality.
  • Distinguishing between intravisit and intervisit variability is crucial for accurate risk assessment.
  • This approach can inform dementia prevention strategies by better identifying cardiovascular risk.