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Developing new statistical methods and designs for clinical research involving categorical variables

Research Project

Project/Area Number 21K11790
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeMulti-year Fund
Section一般
Review Section Basic Section 60030:Statistical science-related
Research InstitutionYokohama City University

Principal Investigator

YAMAMOTO Kouji  横浜市立大学, 医学研究科, 教授 (10548176)

Project Period (FY) 2021-04-01 – 2024-03-31
Project Status Completed (Fiscal Year 2023)
Budget Amount *help
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2023: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2022: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2021: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Keywords臨床試験 / F1スコア / 選択デザイン / カテゴリカル変数 / co-primary / micro-averaged / macro-averaged / co-primary endpoints / 機械学習 / 統計的推測 / 臨床研究 / カテゴリカルデータ / デザイン
Outline of Research at the Start

本研究では臨床研究における次の2つのテーマを選定し,新たな手法開発に取り組む:
①3値以上のカテゴリ変数をアウトカムとする場合の複数の判別法間の性能比較に対する解析手法
②2値変数を含む複数の主要評価変数をもとにしたフレキシブルな治療候補選択デザイン
これらの課題解決に際して,実際の臨床研究に応用した場合の性能評価も行う.また,本研究課題は実際の臨床研究で直面しているものであり,本研究での成果は,より侵襲の少ない方法による疾患の鑑別診断や,最終的に患者さんへ適切な治療を還元するための一助となることが期待される.

Outline of Final Research Achievements

In clinical research,categorical variables are often selected as the primary endpoint.In this study,we studied two problems that were derived from real-world problems: (1) the proposal of an F1 score-based analysis method for comparisons of methods with three or more categories of results, and (2) the development of a flexible treatment candidate selection design with multiple binary variables as the co-primary endpoints.
For (1), we proposed the F1 score, which is a commonly used performance measure in the machine learning field, and clarified the statistical properties of F1 score measures used in the case of three or more categories. For the second, we proposed a new design that simultaneously evaluates two binary variables, efficacy and safety, and selects the best treatment.

Academic Significance and Societal Importance of the Research Achievements

本研究は実際の医学研究における課題から着想を得たものであり,これらに対して新たな解析手法等を提案した.これは新たな統計的手法開発にとどまらない.課題(1)に対してはより科学的に新たに開発された検査法や診断法の有効性を述べることができ,課題(2)に対してはより多面的な角度から最善の治療法を選択できる可能性が高まる.本研究手法を今後の医学分野へ応用することにより,より効率的な研究遂行が可能となり,最終的には疾患で苦しむ患者さんへのよりよい医療の提供につながるものと期待される.

Report

(4 results)
  • 2023 Annual Research Report   Final Research Report ( PDF )
  • 2022 Research-status Report
  • 2021 Research-status Report
  • Research Products

    (4 results)

All 2024 2023 Other

All Int'l Joint Research (1 results) Journal Article (1 results) (of which Int'l Joint Research: 1 results,  Peer Reviewed: 1 results) Presentation (2 results) (of which Int'l Joint Research: 1 results)

  • [Int'l Joint Research] Vanderbilt University(米国)

    • Related Report
      2023 Annual Research Report
  • [Journal Article] Hypothesis testing procedure for binary and multi-class F<sub>1</sub>-scores in the paired design2023

    • Author(s)
      Takahashi Kanae、Yamamoto Kouji、Kuchiba Aya、Shintani Ayumi、Koyama Tatsuki
    • Journal Title

      Statistics in Medicine

      Volume: 42 Issue: 23 Pages: 4177

    • DOI

      10.1002/sim.9853

    • Related Report
      2023 Annual Research Report
    • Peer Reviewed / Int'l Joint Research
  • [Presentation] 多値分類臨床検査データにおける感度,陽性的中率,F1 スコアの仮説検定2024

    • Author(s)
      高橋 佳苗,山本 紘司
    • Organizer
      2024年度応用統計学会年会
    • Related Report
      2023 Annual Research Report
  • [Presentation] A superiority test for comparing sensitivity, specificity, and predictive values of two diagnostic tests2023

    • Author(s)
      Kanae Takahashi and Kouji Yamamoto
    • Organizer
      The 37th International Workshop on Statistical Modelling
    • Related Report
      2023 Annual Research Report
    • Int'l Joint Research

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Published: 2021-04-28   Modified: 2025-01-30  

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