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Fitting high-dimensional mixture cure models using the hdcuremodelsR package.

Kellie J Archer1, Han Fu2

  • 1The Ohio State University, Division of Biostatistics, College of Public Health, Columbus, 43210, OH, United States.

Computer Methods and Programs in Biomedicine
|January 3, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces the hdcuremodels R package for analyzing time-to-event data with a cured fraction in high-dimensional settings. The package effectively fits penalized mixture cure models, aiding in identifying molecular features for risk stratification.

Keywords:
LASSOPenalizedRRegularizedSurvival

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Area of Science:

  • Biostatistics
  • Computational Biology
  • Genomics

Background:

  • Time-to-event outcomes are crucial in biomedical research, especially with long-term survivors or uncured subjects.
  • Mixture cure models (MCMs) are essential for such datasets.
  • Identifying molecular features linked to time-to-event outcomes is vital for pathway elucidation and therapeutic targeting.

Purpose of the Study:

  • Introduce the hdcuremodels R package for high-dimensional mixture cure modeling.
  • Enable modeling of right-censored time-to-event data with a cured fraction and numerous predictors.
  • Facilitate the identification of molecular features associated with survival outcomes.

Main Methods:

  • Implemented expectation-maximization and generalized monotone incremental forward stagewise algorithms for model fitting.
  • Developed cross-validation functions, with and without false discovery rate control.
  • Included flexible modeling functions without predictor requirements for incidence and latency components.

Main Results:

  • Demonstrated the fitting of a high-dimensional penalized mixture cure model.
  • Applied the model to an acute myeloid leukemia dataset.
  • Achieved strong predictive performance on an independent test set.

Conclusions:

  • The hdcuremodels package effectively fits penalized mixture cure models.
  • It accommodates datasets where the number of predictors exceeds the sample size.
  • Provides tools for analyzing complex time-to-event data in high-dimensional biological studies.