A DETECTION OF INFLUENTIAL INDIVIDUALS FOR THE RISK PREDICTION OF ENDOLEAK FORMATION AFTER TEVAR BASED ON A NEW FRAMEWORK OF STATISTICAL SENSITIVITY ANALYSIS

DOI Web Site
  • Hayashi Kuniyoshi
    CREST, JST:Graduate School of Environmental and Life Science, Okayama University
  • Ishioka Fumio
    CREST, JST:School of Law, Okayama University
  • Ueda Takuya
    CREST, JST:Department of Radiology, St. Luke's International Hospital
  • Suito Hiroshi
    CREST, JST:Graduate School of Environmental and Life Science, Okayama University
  • Kurihara Koji
    CREST, JST:Graduate School of Environmental and Life Science, Okayama University

Bibliographic Information

Other Title
  • 胸部大動脈瘤ステントグラフト治療に関わる予後予測 : 線形判別分析に基づくエンドリークの予測と影響標本集合の探索
  • キョウブ ダイ ドウミャクリュウ ステントグラフト チリョウ ニ カカワル ヨゴ ヨソク : センケイ ハンベツ ブンセキ ニ モトズク エンドリーク ノ ヨソク ト エイキョウ ヒョウホン シュウゴウ ノ タンサク

Search this article

Abstract

In this paper, we propose a new framework of statistical sensitivity analysis for linear discriminant analysis based on the sign of the influence function. In general, the average of discriminant scores and the misclassification rate based on cross-validation are important measures in discriminant analysis. However, when we perform statistical sensitivity analysis for a discriminant method, we often get different influential subsets for prediction accuracy and analysis result in the cases of the evaluation of individuals for the average of discriminant scores and the misclassification rate based on cross-validation. To deal with the above situation, we search a subset based on the common influential individuals in both cases, and regard the subset as a confidential influential subset. By investigating the characteristics of the individuals belonging to the common subset, we robustly perform statistical sensitivity analysis for the prediction accuracy in linear discriminant analysis. We apply our proposed approach to a two-discrimination problem in terms of a clinical side effect after thoracic endovascular aortic repair (TEVAR), and show the usefulness of our approach.

Journal

Keywords

Details 詳細情報について

Report a problem

Back to top