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Competing Risks Analysis of the Finnish Diabetes Prevention Study.

Moustafa M A Ibrahim1,2,3, Matti Uusitupa4, Jaakko Tuomilehto5,6,7

  • 1Department of Pharmacy, Uppsala University, Uppsala, Sweden.

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|June 30, 2025
PubMed
Summary
This summary is machine-generated.

Lifestyle changes significantly reduced diabetes and death risk in a study of interval-censored data. Key predictors for type 2 diabetes (T2DM) include BMI, HbA1c, and insulin sensitivity.

Keywords:
Markov modelNONMEMcompeting risksdiabetes mellitusinterval‐censored observationsmodel‐based drug developmentmulti‐state modelpharmacokinetics‐pharmacodynamicssurvival analysis

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Area of Science:

  • Clinical Trials
  • Epidemiology
  • Biostatistics

Background:

  • Clinical studies frequently encounter competing risks, where multiple events can occur.
  • Interval-censored data, observed only at discrete visits, poses challenges for standard survival models, potentially causing bias.
  • Accurate analysis of competing risks in interval-censored data is crucial for reliable clinical outcome assessment.

Purpose of the Study:

  • To develop and apply a multi-state model for analyzing competing risks in interval-censored data.
  • To assess the impact of lifestyle changes on diabetes incidence and mortality.
  • To identify key predictors for diabetes development, dropout, and death.

Main Methods:

  • Developed a multi-state model tailored for competing risks with interval-censored data.
  • Applied the model to data from the Finnish Diabetes Prevention Study.
  • Analyzed associations between covariates and clinical outcomes, including diabetes onset, dropout, and death.

Main Results:

  • Lifestyle interventions significantly reduced the risk of both type 2 diabetes (T2DM) and death.
  • Individuals who dropped out showed a lower risk of T2DM, indicating potential non-independent censoring.
  • Identified baseline BMI, HbA1c, and QUICKI insulin sensitivity as key predictors for T2DM onset; baseline BMI for dropout; and sex and age for death.

Conclusions:

  • The developed multi-state model effectively analyzes competing risks in interval-censored data.
  • Lifestyle changes are effective in mitigating T2DM and mortality risks.
  • Specific covariates (BMI, HbA1c, QUICKI, sex, age) can predict clinical outcomes and guide therapeutic interventions.