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Related Experiment Video

Updated: Jun 26, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Improved AIC Selection Strategy for Survival Analysis.

Hua Liang1, Guohua Zou

  • 1Department of Biostatistics and Computational Biology University of Rochester Medical Center Rochester, NY 14642, USA hliang@bst.rochester.edu.

Computational Statistics & Data Analysis
|January 23, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces an improved predictor selection method for survival analysis, offering better performance than traditional AIC in small sample sizes. The enhanced AIC(C) procedure demonstrates effectiveness in both simulations and real-world data analysis.

Related Experiment Videos

Last Updated: Jun 26, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • Selecting significant predictors is crucial in survival analysis.
  • Traditional Akaike Information Criterion (AIC) can be suboptimal for small sample sizes.

Purpose of the Study:

  • To extend the AIC(C) selection procedure for improved predictor selection in survival models, particularly for small sample sizes.
  • To evaluate the performance of the proposed AIC(C) method against traditional AIC.

Main Methods:

  • Theoretical verification using a special case of the exponential distribution.
  • Simulation studies comparing the proposed AIC(C) with AIC.
  • Analysis of two real-world survival data sets.

Main Results:

  • The proposed AIC(C) method substantially outperforms traditional AIC in small samples.
  • The AIC(C) method competes with AIC in moderate and large sample sizes.
  • The method's utility is demonstrated on real data.

Conclusions:

  • The extended AIC(C) procedure offers a valuable improvement for predictor selection in survival analysis, especially when dealing with limited data.
  • This enhanced method provides a more reliable approach compared to traditional AIC for small sample scenarios.