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Updated: Feb 28, 2026

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Outcome Risk Modeling for Disability-Free Longevity: Comparison of Random Forest and Random Survival Forest Methods.

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Random survival forests (RSF) and random forests (RF) showed similar performance in predicting time-to-event outcomes in the ASPREE trial. RSF did not consistently outperform RF in risk prediction models for elderly participants.

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AspirinClinical TrialDisability-free LongevityPredictive ModelingRandom ForestRandom Survival Forest

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Area of Science:

  • Gerontology
  • Biostatistics
  • Machine Learning in Healthcare

Background:

  • Time-to-event data analysis often employs methods incorporating time.
  • Random survival forests (RSF) are an extension of random forests (RF) designed for such data.
  • The ASPirin in Reducing Events in the Elderly (ASPREE) trial provided a cohort for evaluating these models.

Purpose of the Study:

  • To compare the predictive performance of RSF and RF models.
  • To determine if RSF offers superior discrimination and calibration over RF for time-to-event outcomes.
  • To assess the value of incorporating time into risk prediction models.

Main Methods:

  • Utilized data from the ASPREE randomized controlled trial.
  • Excluded participants from outside the US or with missing data.
  • Trained Random Forest (RF) and Random Survival Forest (RSF) models on 2,291 participants using 115 candidate predictors.
  • The primary outcome was the earliest occurrence of incident dementia, physical disability, or death.

Main Results:

  • The primary endpoint occurred in 10.5% of participants.
  • Both RF and RSF models demonstrated similar discrimination metrics (sensitivity, specificity, PPV, time-dependent AUC, Harrell's concordance).
  • Calibration, assessed by Brier score, was also comparable between the two models.

Conclusions:

  • Random survival forests (RSF) and random forests (RF) exhibited comparable discrimination and calibration in this cohort.
  • RSF may not consistently provide more accurate outcome predictions than RF.
  • Further research across diverse clinical trial cohorts is necessary to define the specific contexts where time-based risk modeling offers added value.