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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Related Experiment Video

Updated: Jan 17, 2026

Assessing the Coherence of Parents' Short Narratives Regarding their Child Using the Five-Minute Speech Sample Procedure
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Exploring father-adolescent closeness: A random forest approach.

Garrett T Pace1, Joyce Y Lee2, Kaitlin P Ward3

  • 1School of Social Work, University of Nevada, Las Vegas, Las Vegas, NV.

Family Relations
|September 25, 2025
PubMed
Summary

Machine learning, specifically random forest, enhances family science research on father-child relationships by overcoming data challenges. Fathers' residential status was the key predictor of adolescent closeness.

Keywords:
adolescenceartificial intelligencechildren’s perspectivesfather–child relationshipsmachine learningrandom forest

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Area of Science:

  • Family Science
  • Computational Social Science
  • Artificial Intelligence in Social Research

Background:

  • Fatherhood research faces recruitment, retention, and data complexity challenges.
  • Machine learning (ML) offers solutions for analyzing large, complex datasets and handling missing data.
  • ML can mitigate methodological hurdles in studying father-child relationships.

Purpose of the Study:

  • To demonstrate the utility of random forest, a machine learning algorithm, in family science.
  • To identify key predictors of father-adolescent closeness.
  • To enhance regression models using ML-informed variable selection.

Main Methods:

  • Applied random forest algorithm to predict father-adolescent closeness.
  • Utilized data from the Future of Families and Child Wellbeing Study (n=2,927).
  • Included 131 predictors measured in the first decade of childhood.

Main Results:

  • Fathers' residential status with the child was the strongest predictor of closeness.
  • Random forest improved variable selection for enhanced regression models.
  • Demonstrated ML's capability in identifying complex family context predictors.

Conclusions:

  • Random forest is a valuable tool for family scientists studying complex relationships.
  • Incorporating artificial intelligence, like random forest, advances family science research.
  • This approach offers new directions for understanding father-child dynamics.