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Efficient Algorithms and Implementation of a Semiparametric Joint Model for Longitudinal and Competing Risk Data:

Shanpeng Li1, Ning Li2,3, Hong Wang4

  • 1Department of Biostatistics, University of California at Los Angeles, Los Angeles, CA, USA.

Computational and Mathematical Methods in Medicine
|February 18, 2022
PubMed
Summary

This study introduces efficient linear scan algorithms to significantly speed up semiparametric joint models for longitudinal and competing risk data. These computational improvements make complex survival analysis feasible for large biobank datasets.

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Area of Science:

  • Biostatistics
  • Computational Biology
  • Survival Analysis

Background:

  • Semiparametric joint models for longitudinal and competing risk data are computationally intensive.
  • Existing methods struggle to scale for massive biobank-scale datasets.

Purpose of the Study:

  • To address computational barriers in semiparametric joint models.
  • To develop scalable algorithms for analyzing longitudinal and competing risk survival data.

Main Methods:

  • Developed customized linear scan algorithms.
  • Reduced computational complexity from O(n^2) or O(n^3) to O(n).
  • Applied algorithms to numerical integration, risk set calculation, and standard error estimation.

Main Results:

  • Achieved significant speedups (up to hundreds of thousands-fold) for n > 10^4.
  • Reduced runtime from days to minutes for large datasets.
  • Validated algorithms using simulated and real-world biobank data.

Conclusions:

  • Linear scan algorithms offer substantial computational efficiency for joint models.
  • The FastJM R package provides a scalable solution for biobank data analysis.
  • Enables advanced survival analysis on large-scale biomedical datasets.