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An R-Based Landscape Validation of a Competing Risk Model
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Fitting the Cox proportional hazards model to big data.

Jianqiao Wang1, Donglin Zeng1, Dan-Yu Lin1

  • 1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.

Biometrics
|March 18, 2024
PubMed
Summary

We developed an efficient Cox model fitting method for big data. This approach significantly reduces computation time while maintaining statistical accuracy for survival analysis.

Keywords:
censoringefficient scoreone-step estimationpartial likelihoodtime complexitytime-dependent covariates

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Computational Statistics

Background:

  • The Cox proportional hazards model is standard for analyzing time-to-event data with covariates.
  • Analyzing large datasets (big data) with traditional Cox model fitting methods is computationally intensive.
  • Handling time-dependent covariates and censored data are key challenges in survival analysis.

Purpose of the Study:

  • To propose a computationally efficient method for fitting the Cox proportional hazards model to big data.
  • To reduce the computational burden of Cox model estimation for large-scale studies.
  • To ensure the proposed method maintains the statistical properties of the conventional estimator.

Main Methods:

  • Maximum partial likelihood estimation on a data subset.
  • One-step estimation using efficient score functions to incorporate remaining data.
  • Validation through extensive simulation studies and real-world data application (UK Biobank).

Main Results:

  • The proposed method achieves the same asymptotic distribution as the full dataset estimator.
  • Significant reduction in computation time compared to conventional methods.
  • Demonstrated effectiveness and efficiency on large-scale UK Biobank data.

Conclusions:

  • The proposed method offers a computationally efficient alternative for Cox model fitting in big data settings.
  • This approach enables accurate survival analysis on massive datasets, previously limited by computational resources.
  • The method is practical for large cohort studies and real-world data analysis.