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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Random Survival Forest With Multiple Imputation Analysis for Case-Cohort and Generalized Case-Cohort Studies.

Haolin Li1, Haibo Zhou1, David Couper1

  • 1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

Statistics in Medicine
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We developed new methods for survival prediction in case-cohort studies using random survival forests with multiple imputation. These approaches improve accuracy for epidemiological research and disease prediction.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Machine Learning

Background:

  • Case-cohort and generalized case-cohort designs offer efficient alternatives to full cohort studies in epidemiology.
  • Existing research primarily addresses estimation and inference in semiparametric survival models.
  • Nonparametric survival prediction methods are under-explored for these efficient study designs.

Purpose of the Study:

  • To introduce novel methods for nonparametric survival prediction in case-cohort and generalized case-cohort studies.
  • To evaluate the performance of proposed random survival forest approaches with different imputation techniques.
  • To enhance the utility of case-cohort designs for predictive modeling in epidemiology.

Main Methods:

  • Proposed random survival forest with multiple imputation by chained equation (RSF-MICE).
  • Proposed random survival forest with substantive model compatible imputation (RSF-SMCI).
  • Conducted simulation studies to assess finite-sample performance.

Main Results:

  • Both RSF-MICE and RSF-SMCI demonstrated superior performance in simulations across various scenarios.
  • The proposed methods provide effective survival predictions within case-cohort frameworks.
  • Successfully applied the approach to predict incident diabetes in a real-world study.

Conclusions:

  • RSF-MICE and RSF-SMCI are effective and efficient for survival prediction in case-cohort studies.
  • These methods advance nonparametric survival analysis for epidemiological research.
  • The developed approach has practical applications in predicting disease incidence.