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Establishing a Competing Risk Regression Nomogram Model for Survival Data

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Published on: October 23, 2020

A fast divide-and-conquer sparse Cox regression.

Yan Wang^1,2, Chuan Hong³, Nathan Palmer³

¹Department of Environmental Health, Harvard T. H. Chan School of Public Health, 401 Park Drive West, Boston, MA, 02215, USA.

Biostatistics (Oxford, England)

|September 24, 2019

Summary

This summary is machine-generated.

A new divide-and-conquer algorithm efficiently fits sparse Cox regression to large datasets. This method speeds up analysis while maintaining statistical accuracy for survival data prediction.

Keywords:

Cox proportional hazards model Distributed learning Divide-and-conquer Least square approximation Shrinkage estimation Variable selection

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Area of Science:

Biostatistics
Computational Biology
Health Informatics

Background:

Sparse Cox regression is crucial for analyzing large-scale survival data.
Existing methods struggle with computational efficiency on massive datasets where sample size far exceeds covariate dimension ($n_0 \gg p$).

Purpose of the Study:

To develop a computationally and statistically efficient divide-and-conquer (DAC) algorithm for sparse Cox regression on massive datasets.
To enable accurate survival data analysis and prediction in resource-intensive scenarios.

Main Methods:

The proposed DAC algorithm uses a one-step linear approximation and a least squares approximation to the partial likelihood (PL).
It maximizes PL using a small data subset and performs penalized estimation via a fast PL approximation.
Applicable to both time-independent and time-dependent survival data.

Main Results:

The DAC algorithm significantly outperforms existing methods in computational speed.
It achieves statistical efficiency comparable to full sample-based estimators.
Demonstrates substantial computational gains over traditional and existing DAC algorithms.

Conclusions:

The proposed DAC algorithm offers an efficient solution for fitting sparse Cox regression to massive survival datasets.
It provides a computationally feasible and statistically sound approach for large-scale health data analysis, such as predicting heart failure readmission.