Hayashi, Kenichi

写真a

Affiliation

Faculty of Science and Technology, Department of Mathematics (Yagami)

Position

Associate Professor
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Career 【 Display / hide 】

  • 2008.04
    -
    2009.02

    同志社大学 , 文化情報学部, 実習助手

  • 2009.04
    -
    2011.03

    日本学術振興会, 特別研究員（DC2）

  • 2010.02
    -
    2010.11

    University of California, Berkeley, Department of Statistics, Visiting Scholar

  • 2011.04
    -
    2011.09

    大阪大学, 大学院医学系研究科, 特任研究員

  • 2011.10
    -
    2015.03

    大阪大学, 大学院医学系研究科, 助教

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Academic Background 【 Display / hide 】

  • 2000.04
    -
    2004.03

    Osaka University, Department of Human Sciences

    University, Graduated

  • 2005.04
    -
    2007.03

    Osaka University, Graduate School of Engineering Science, Department of Systems Science

    Graduate School, Completed, Master's course

  • 2007.04
    -
    2011.03

    Osaka University, Graduate School of Engineering Science, Department of Systems Science

    Graduate School, Withdrawal after completion of doctoral course requirements, Doctoral course

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Academic Degrees 【 Display / hide 】

  • 博士（工学）, 大阪大学, Coursework, 2011.09

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Research Areas 【 Display / hide 】

  • Informatics / Statistical science (Biostatistics)

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Books 【 Display / hide 】

  • Rで学ぶ統計的データ解析

    林 賢一, 講談社サイエンティフィク, 2020.11,  Page： 352

  • Machine Learning: The Art and Science of Algorithms that Make Sense of Data

    竹村 彰通（監訳），田中 研太郎，小林 景，兵頭 昌，片山 翔太，山本 倫生，吉田 拓真，林 賢一，松井 秀俊，小泉 和之，永井 勇, 朝倉書店, 2017.04

    Scope: 7章

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Papers 【 Display / hide 】

  • Variable selection methods based on pseudo-observations in competing risks analysis

    Tajima, F., Hayashi, K.

    International Journal of Statistics and Probabilit 14 ( 2 ) 37 - 54 2025.07

    Research paper (scientific journal), Joint Work, Last author, Corresponding author, Accepted

  • Asymptotic Properties of Matthews Correlation Coefficient

    Itaya, Y., Tamura, J., Hayashi, K., Yamamoto, K.

    Statistics in Medicine 44 ( 1-2 )  2025.01

    Research paper (scientific journal), Joint Work, Accepted,  ISSN  02776715

    　View Summary

    Evaluating classifications is crucial in statistics and machine learning, as it influences decision-making across various fields, such as patient prognosis and therapy in critical conditions. The Matthews correlation coefficient (MCC), also known as the phi coefficient, is recognized as a performance metric with high reliability, offering a balanced measurement even in the presence of class imbalances. Despite its importance, there remains a notable lack of comprehensive research on the statistical inference of MCC. This deficiency often leads to studies merely validating and comparing MCC point estimates—a practice that, while common, overlooks the statistical significance and reliability of results. Addressing this research gap, our paper introduces and evaluates several methods to construct asymptotic confidence intervals for the single MCC and the differences between MCCs in paired designs. Through simulations across various scenarios, we evaluate the finite-sample behavior of these methods and compare their performances. Furthermore, through real data analysis, we illustrate the potential utility of our findings in comparing binary classifiers, highlighting the possible contributions of our research in this field.

  • Partial areas under the curve of the cumulative distribution function as a new composite estimand for randomized clinical trials

    Taguri, M., Hayashi, K.

    Statistical Methods in Medical Research  2025

    Last author,  ISSN  09622802

    　View Summary

    Clinical trials often face the challenge of post-randomization events, such as the initiation of rescue therapy or the premature discontinuation of randomized treatment. Such events, called “intercurrent events” (ICEs) in ICH E9(R1), may influence the estimation and interpretation of treatment effects. According to ICH E9(R1), there are five strategies for handling ICEs. This study focuses on the composite strategy, which incorporates ICEs in the outcome of interest and defines the treatment effects using composite endpoints that combine the measured continuous variables and ICEs. An advantage of this strategy is that it avoids the occurrence of missing data because they are defined as part of the outcome of interest. In this study, we propose a new composite estimand: the difference in the partial areas under the curves (pAUCs) of the cumulative distribution function. While the pAUC is closely related to the trimmed mean approach proposed by Permutt and Li, it offers the advantage of allowing pre-specification of the cutoff value for a “good” response based on clinical considerations. This ensures that the pAUC can be calculated irrespective of the proportion of ICEs. We describe the causal interpretation of our method and its relationship with two other strategies (treatment policy and hypothetical strategies) using a potential outcome framework. We present simulation results in which our method performs reasonably well compared to several existing approaches in terms of type I error, power, and the proportion of undefined test statistics.

  • Optimal machine learning models for developing prognostic predictions in patients with advanced cancer

    Hamano, J., Takeuchi, A., Keyaki, T., Nose, H., Hayashi, K.

    Cereus 16 ( 12 )  2024.12

    Research paper (scientific journal), Joint Work, Last author, Accepted

  • A new integrated discrimination improvement index via odds

    Hayashi, K., Eguchi, S.

    Statistical Papers 65 ( 8 ) 4971 - 4990 2024.10

    Lead author, Corresponding author,  ISSN  09325026

    　View Summary

    Consider adding new covariates to an established binary regression model to improve prediction performance. Although difference in the area under the ROC curve (delta AUC) is typically used to evaluate the degree of improvement in such situations, its power is not high due to being a rank-based statistic. As an alternative to delta AUC, integrated discrimination improvement (IDI) has been proposed by Pencina et al. (2008). However, several papers have pointed out that IDI erroneously detects meaningless improvement. In the present study, we propose a novel index for prediction improvement having Fisher consistency, implying that it overcomes the problems in both delta AUC and IDI. Furthermore, our proposed index also has an advantage that the index we proposed in our previous study (Hayashi and Eguchi 2019) lacked: it does not require any hyperparameters or complicated transformations that would make interpretation difficult.

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Papers, etc., Registered in KOARA 【 Display / hide 】

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Reviews, Commentaries, etc. 【 Display / hide 】

  • 医学のための標的学習の基礎 II：無作為化比較試験における共変量調整

    田栗 正隆, 高橋 邦彦, 小向 翔, 伊藤 ゆり, 服部 聡, 船渡川 伊久子, 篠崎 智大, 山本 倫生, 林 賢一

    計量生物学 44 ( 2 )  2024

    Article, review, commentary, editorial, etc. (scientific journal), Joint Work

  • Statistics is not alchemy

    林 賢一

    Japanese Psychological Review 61 ( 1 ) 147 - 155 2018.07

    Article, review, commentary, editorial, etc. (scientific journal), Single Work, Lead author, Corresponding author

  • 労働時間と飲酒行動

    吉田 恵子，林 賢一

    桃山学院大学経済経営論集 56 ( 1 ) 153 - 164 2014.11

    Rapid communication, short report, research note, etc. (bulletin of university, research institution), Joint Work, Last author

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Presentations 【 Display / hide 】

  • Contradictory conclusions in classification metrics: a study of paired design

    Okada, K., Hayashi, K.

    The 8th International Conference on Econometrics and Statistics (EcoSta 2025), 

    2025.08
    , 

    Oral presentation (general)

  • 診断検査における評価指標の過大評価に関する定量化法

    伊藤 健太， 竹内 文乃，林 賢一

    日本産業衛生学会第2回関東地方会学会, 

    2025.07
    , 

    Oral presentation (general)

  • Co-primary endpointを設定する国際同時医薬品開発計画における一貫性を考慮した対象地域集団の症例数設定

    Issiki,S.,Hayashi,K.

    2025年度応用統計学会年会, 

    2025.05
    , 

    Oral presentation (general)

  • メディアンの差のメタアナリシスにおける統合効果の分散推定

    奥田 忠久， 田栗 正隆，林 賢一

    2025年度応用統計学会年会, 

    2025.05
    , 

    Oral presentation (general)

  • ペアデザインにおいて分類性能指標が逆転する現象について

    岡田 和也，林 賢一

    2025年度応用統計学会年会, 

    2025.05
    , 

    Oral presentation (general)

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Research Projects of Competitive Funds, etc. 【 Display / hide 】

  • Integrated understanding of statistical analysis for imbalanced data and its advanced application to survival analysis

    2023.04
    -
    2027.03

    MEXT,JSPS, Grant-in-Aid for Scientific Research, Grant-in-Aid for Scientific Research (C), Principal investigator

  • Developments and applications of statistical prediction method

    2018.04
    -
    2023.03

    MEXT,JSPS, Grant-in-Aid for Scientific Research, Grant-in-Aid for Scientific Research (B), Coinvestigator(s)

  • Development of statistical models for data containing heterogeneous subgroups using statistical learning theory and its application to clinical medicine

    2018.04
    -
    2022.03

    MEXT,JSPS, Grant-in-Aid for Scientific Research, Grant-in-Aid for Scientific Research (C), Principal investigator

  • A study on exploratory identification of responsive subgroups in complex survival event data

    2015.04
    -
    2018.03

    MEXT,JSPS, Grant-in-Aid for Scientific Research, Grant-in-Aid for Young Scientists (B), Principal investigator

  • Development of C-index-based survival tree and its application to biomedical research

    2012.04
    -
    2015.03

    MEXT,JSPS, Grant-in-Aid for Scientific Research, Grant-in-Aid for Young Scientists (B), Principal investigator

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Awards 【 Display / hide 】

  • 最優秀賞（ポスター部門）

    岡田和也，林賢一, 2024.01,  2023年度スポーツデータサイエンスコンペティション事務局, 2023年度スポーツデータサイエンスコンペティション

    Type of Award： Award from Japanese society, conference, symposium, etc.

    　View Description

    岡田和也（理工学部数理科学科，筆頭著者）との共同研究

  • 若手優秀発表賞

    2021.05, 日本計量生物学会

    Type of Award： Award from Japanese society, conference, symposium, etc.

    　View Description

    2021年度計量生物学会年会 若手優秀発表賞（正会員の部）

  • 最優秀賞

    奥富航，林賢一, 2018.01, 第7回スポーツデータ解析コンペティション審査会, 第7回スポーツデータ解析コンペティション

    Type of Award： Award from Japanese society, conference, symposium, etc.

    　View Description

    奥富航（理工学部数理科学科，筆頭著者）との共同研究

  • 論文賞

    2017.12, 日本分類学会, 日本分類学会

    Type of Award： Award from Japanese society, conference, symposium, etc.

  • 論文賞

    2015.05, 日本計算機統計学会, 日本計算機統計学会

    Type of Award： Award from Japanese society, conference, symposium, etc.

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Courses Taught 【 Display / hide 】

  • TOPICS IN STATISTICAL SCIENCES C

    2025, Autumn Semester

  • STATISTICAL SCIENCE AND ITS EXERCISE

    2025, Spring Semester

  • SEMINAR IN STATISTICAL SCIENCES

    2025, Autumn Semester

  • MATHEMATICS 2B

    2025, Autumn Semester

  • MATHEMATICS 1A

    2025, Spring Semester

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Courses Previously Taught 【 Display / hide 】

  • 経営工学特別講義・情報工学特別講義

    Tokyo University of Science

    2021.10
    , 

    Autumn Semester, Postgraduate

  • 理科演習

    慶應女子高等学校

    2017.01
    , 

    Autumn Semester, Other, 1h

  • 疫学総論

    Osaka University

    2016.05
    , 

    Spring Semester, Postgraduate, 1h

  • 数理科学講演会

    慶應義塾湘南藤沢高等部

    2016.03
    , 

    Autumn Semester, Other

  • 疫学総論

    Osaka University

    2015.06
    , 

    Spring Semester, Postgraduate, 1h

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Memberships in Academic Societies 【 Display / hide 】

  • 日本分類学会, 

    2017.10
    -
    Present

  • International Biometric Society, 

    2014.04
    -
    Present

  • 日本疫学会, 

    2014.04
    -
    Present

  • 日本計量生物学会, 

    2014.04
    -
    Present

  • 日本計算機統計学会, 

    2013.04
    -
    Present

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Committee Experiences 【 Display / hide 】

  • 2021.04
    -
    2022.02

    NEDO先導研究プログラム外部有識者

  • 2021
    -
    2021.11

    副実行委員長（第35回シンポジウム）, 日本計算機統計学会

  • 2020
    -
    2021.03

    副実行委員長（2021年度年会）, 日本数学会

  • 2018
    -
    Present

    Associate Editor, Japanese Journal of Statistics and Data Science

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