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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Comparison of variable selection methods for high-dimensional survival data with competing events.

Julia Gilhodes1, Christophe Zemmour2, Soufiane Ajana1

  • 1Department of Biostatistics, Institut Claudius Regaud, IUCT-O, Toulouse, France.

Computers in Biology and Medicine
|October 28, 2017
PubMed
Summary

Identifying gene signatures for competing risks is crucial for personalized medicine. While Random Survival Forest and boosting methods show promise, the resulting gene signatures lack stability, especially with smaller training datasets.

Keywords:
BoostingCompeting risksHigh-dimensional dataRandom survival forestStabilityVariable selection

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Personalized medicine requires identifying gene signatures for competing risks to improve patient stratification and treatment.
  • High-dimensional data selection methods for molecular signatures in competing risks contexts have received limited attention.
  • This study evaluates two high-dimensional selection methods: Random Survival Forest and a boosting approach for proportional subdistribution hazards models.

Purpose of the Study:

  • To investigate the performance of Random Survival Forest and a boosting approach for identifying gene signatures in the context of competing risks.
  • To assess the stability and prognostic performance of molecular signatures derived from high-dimensional data.
  • To compare the effectiveness of these methods in risk stratification for personalized medicine.

Main Methods:

  • Utilized bladder cancer patient data (GSE5479) and simulated datasets.
  • Employed a resampling strategy, splitting data into 100 training and validation sets for each sample.
  • Developed molecular signatures using Random Survival Forest and boosting in training sets, then applied them to validation sets.

Main Results:

  • Both Random Survival Forest and boosting methods demonstrated comparable predictive performance for survival data.
  • Few selected genes were common between the two methods, indicating distinct signature compositions.
  • Signature stability decreased with smaller training sample sizes, with a low frequency of gene occurrence across signatures.

Conclusions:

  • Random Survival Forest and boosting approaches offer good predictive performance but yield highly unstable gene signatures.
  • Further research is necessary to develop robust strategies for analyzing high-dimensional data in competing risks scenarios.
  • The instability of gene signatures highlights the need for improved methods in personalized medicine for complex risk prediction.