Research

Published and accepted papers

Statistical Methodology

1. Ohigashi T, Maruo K, Sozu T, Sawamoto R, Gosho M. Potential bias models with Bayesian shrinkage priors for dynamic borrowing of multiple historical control data. Pharmaceutical Statistics  2025; 24(2): e2453. [publication] 

2. Minewaki S, Ohigashi T, Sozu T. Evaluating marginal likelihood approximations of dose–response relationship models in Bayesian benchmark dose methods for risk assessment. Computational Toxicology 2025; 100347. [publication]

3. Ishii R, Ohigashi T, Maruo K, Gosho M. geessbin: an R package for analyzing small-sample binary data using modified generalized estimating equations with bias-adjusted covariance estimators. BMC Medical Research Methodology 2024; 24: 277. [publication]

4. Maruo K, Ishii R, Yamaguchi Y, Ohigashi T, Gosho M. Small sample adjustment for inference without assuming orthogonality in a mixed model for repeated measures analysis. Journal of Biopharmaceutical Statistics 2024+; doi: 10.1080/10543406.2024.2420632. [publication]

5. Gosho M, Ishii R, Ohigashi T, Maruo K. Multivariate generalized mixed-effects models for screening multiple adverse drug reactions in spontaneous reporting systems. Frontiers in Pharmacology 2024; 15. [publication] 

6. Gosho M, Ohigashi T, Nagashima K, Ito Y, Maruo K. Bias in odds ratios from logistic regression methods with sparse data sets. Journal of Epidemiology 2023; 33(6): 265–275. [publication]

7. Ohigashi T, Maruo K, Sozu T, Gosho M. Using horseshoe prior for incorporating multiple historical control data in randomized controlled trials. Statistical Methods in Medical Research 2022; 31(7): 1392–1404. [publication]

8. Gosho M, Ohigashi T, Maruo K. SignalDetDDI: An SAS macro for detecting adverse drug-drug interactions in spontaneous reporting systems. PLOS ONE 2018; 13(11): e0207487. [publication]

Review of Statistical Methodology (in Japanese)

9. 野村尚吾, 大東智洋,  澤本涼. Hybrid controlアプローチを用いるランダム化比較試験の計画と解析：外部データが要約統計量の場合. 計量生物学 2022; 43(1): 63–96. [publication]
   (Nomura S, Ohigashi T, Sawamoto R. Design and analysis of randomized controlled trials using a hybrid control approach: a case when external data is derived as summary statistics. Japanese Journal of Biometrics 2022; 43(1): 63–96.)

Medical Researches

10. Okubo R, Ohigashi T, Kondo M, Tsunoda R, Kai H, Saito C, Hoshino J, Okada H, Narita I, Maruyama S, Wada T, Yamagada K. Associations of anaemia and iron deficiency with health-related quality of life in patients with chronic kidney disease stage G3b-5 in Japan: sub analysis of the Reach-J CKD cohort study. BMC Nephrology 2024; 25: 414.

11. Hoshino J, Ohigashi T, Tsunoda R, Ito Y, Kai H, Saito C, Okada H, Narita I, Wada T, Maruyama S, Pisoni R, Pecoits-Filho R, Yamagata K. Physical activity and renal outcome in diabetic and non-diabetic patients with chronic kidney disease stage G3b to G5. Scientific Reports 2024; 14: 26378.

12. Shimada K, Gosho M, Ohigashi T, Kume K, Yano T, Ishii R, Maruo K, Inokuchi R, Iwagami M, Ueda H, Tanaka M, Sanuki M, Tamiya  N. Risk of postoperative pneumonia after extubation with the positive pressure versus normal pressure technique: a single-center retrospective observational study. Journal of Anesthesia 2025; 39: 5–14.

13. Shimada K, Inokuchi R, Ohigashi T, Iwagami M, Tanaka M, Gosho M, Tamiya N. Artificial intelligence-assisted interventions for perioperative anesthetic management: a systematic review and meta-analysis. BMC Anesthesiology 2024; 24: 306.

14. 中川理子, 村田雄哉, 丸尾和司, 大東智洋, 成田聖門, 山下創一郎, 田中誠. ロクロニウム臭化物静注液「マルイシ」の作用発現時間：エスラックス®治験データとの比較. 麻酔 2024; 73: 224–228.
    (Nakagawa R, Murata Y, Maruo K, Ohigashi T, Narita S, Yamashita S, Tanaka M. Onset Time of Rocuronium Bromide Intravenous Solution Maruishi: Comparison with Eslax Intravenous Clinical Trial Data. Japanese Journal of Anesthesiology 2024; 73: 224–228.)

15. Shimoda T, Liu C, Mathis BJ, Goto Y, Ageyama D, Kato H, Matsubara M, Ohigashi T, Gosho M, Suzuki Y, Hiramatsu Y. Effect of cardiopulmonary bypass on coagulation factors II, VII, and X in a primate model. Interactive CardioVascular and Thoracic Surgery 2023; 37: ivad194.

16. Kawamura T, Sekine Y, Sugai K, Yanagihara T, Saeki Y, Kitazawa S, Kobayashi N, Goto Y, Ichimura H, Ohigashi T, Maruo K, Sato Y. Three-dimensional analysis reveals a high incidence of lung adenocarcinoma in the upper region. Surgery Today 2024; 54: 634–641.

17. Kurita N, Nishikii H, Maruyama Y, Suehara Y, Hattori K, Sakamoto T, Kato T, Yokoyama Y, Obara N, Maruo K, Ohigashi T, Yamaguchi H, Iwamoto T, Minohara H,  Matsuoka R, Hashimoto K, Sakata-Yanagimoto M, Chiba S. Safety of romiplostim administered immediately after cord-blood transplantation: a phase 1 trial. Annals of Hematology 2023; 102: 2895–2902.

18. Mizuno M, Chiba I, Mukohara T, Kondo M, Maruo K, Ohigashi T, Naruo M, Asano Y, Onishi T, Tanabe H, Muta R, Mishima S, Okano S, Yuda M, Hosono A, Ueda Y, Bando H, Itagaki H, Ferrans C, Akimoto T. Effectiveness of an online support program to help female cancer patients manage their health and illness: Protocol for a randomized controlled trial. Contemporary Clinical Trials Communications 2022; 30: 101035.

19. Nakata Y, Sasai H, Gosho M, Kobayashi H, Shi Y, Ohigashi T, Mizuno S, Murayama C, Kobayashi S, Sasaki Y. A Smartphone Healthcare Application, CALO mama Plus, to Promote Weight Loss: A Randomized Controlled Trial. Nutrients 2022; 14: 4608.

20. Izawa J, Matsuzaki K, Raita Y, Uehara G, Nishioka N, Yano H, Sudo, K, Katsuren M, Ohigashi T, Sozu T, Kawamura T, Miyasato H, The TRANEPSY Trial Group. Intravenous Tranexamic Acid in Percutaneous Kidney Biopsy: A Randomized Controlled Trial. Nephron 2022; 147(3-4): 144–151.

21. Matsuzaki K, Ohigashi T, Sozu T, Ishida M, Kobayashi D, Suzuki H, Suzuki Y, Kawamura T. Identification of High-Risk Groups in Urinalysis: Lessons from the Longitudinal Analysis of Annual Check-Ups. Healthcare 2022; 10(9): 1704.

22. Kimata A, Nogami A, Yamasaki H, Ohigashi T, Gosho M, Igarashi M, Sekiguchi Y, Ieda M, Calkins H, Aonuma K. Optimal interruption time of dabigatran oral administration to ablation (O-A time) in patients with atrial fibrillation: Integrated analysis of 2 randomized controlled clinical trials. Journal of Cardiology 2021; 77(6): 652–659.

23. Murata Y, Yamada K, Hamaguchi Y, Ohigashi T, Maruo K, Yamashita S, Tanaka M. Patient-controlled epidural analgesia, patient-controlled intravenous analgesia, and conventional intravenous opioids for gynecologic interstitial brachytherapy: A singlecenter retrospective study. Brachytherapy 2021; 20(4): 765–770.

24. Hoshi T, Sato A, Hiraya D, Watabe H, Takeyasu N, Nogami A, Ohigashi T, Gosho M, Ieda M, Aonuma K. Short-duration triple antithrombotic therapy for atrial fibrillation patients who require coronary stenting: Results of the SAFE-A study. EuroIntervention 2020; 16(2): e164–e172.

25. Yamamoto K, Koretsune Y, Kinugasa Y, Ohigashi T, Sozu T, Masuyama T. The preventive approach to degenerative aortic stenosis should depart from the approach to atherosclerotic diseases: A Japanese perspective. European Journal of Preventive Cardiology 2020; 27(19): 2170–2172.

Working papers

1. Ohigashi T, Maruo K, Sozu T, Gosho M. Nonparametric Bayesian approach for dynamic borrowing of historical control data. R&R. [arXiv]

2. Orihara S, Sugasawa S, Ohigashi T, Nakagawa T, Taguri M. Nonparametric Bayesian Adjustment of Unmeasured Confounders in Cox Proportional Hazards Models. [arXiv]

3. Kojima M, Orihara S, Hanada K, Ohigashi T. Sample size re-estimation in blinded hybrid-control design using inverse probability weighting. [arXiv]

Talks (International Conference)

1. Orihara S, Sugasawa S, Ohigashi T, Nakagawa T, Taguri M. Nonparametric Bayesian adjustment of unmeasured confounders in Cox proportional hazards models. IASC-ARS Interim Conference 2024, Taipei City, Taiwan. 13–14 December 2024. [Invited]

2. Minewaki S, Ohigashi T, Sozu T. Evaluation of methods for approximating posterior probability of dose-response relationship models in Bayesian benchmark dose methods for risk assessment. IASC-ARS Interim Conference 2024, Taipei City, Taiwan. 13–14 December 2024. [Oral]

3. Ohigashi T, Maruo K, Sozu T, Gosho M. A dependent Dirichlet process mixture model for borrowing historical controls with survival outcome. 2024 Joint Statistical Meetings, Portland, Oregon, USA, 3–8 August 2024. [Poster]

4. Orihara S, Sugasawa S, Ohigashi T, Nakagawa T, Taguri M. Nonparametric Bayesian adjustment of unmeasured confounders in Cox proportional hazards models. 2024 Joint Statistical Meetings, Portland, Oregon, USA, 3–8 August 2024. [Contributed speed session]

5. Ohigashi T, Maruo K, Sozu T, Sawamoto R, Gosho M. Dynamic borrowing from multiple historical control data by shrinkage priors. 2022 WNAR/IMS/JR Annual Meeting, Virtual Conference, 10–15 June 2022. [Invited]

6. Ohigashi T, Sozu T, Tsuchida J, Maruo K, Gosho M. Bayesian hierarchical model with historical patient-level covariates for considering between-trial heterogeneity. 2019 WNAR/IMS/JR Annual Meeting, Portland, Oregon, USA, 23–26 June 2019. [Poster]

7. Ohigashi T, Sozu T, Tsuchida J. Covariate adjustment for considering between-trial heterogeneity in clinical trials using historical data for evaluating the treatment efficacy. ENAR 2019 Spring Meeting, Philadelphia, Pennsylvania, USA, 24–27 March 2019. [Poster]

8. Ohigashi T, Sozu T. Bayesian methods for evaluating the efficacy of a new treatment considering between-trial heterogeneity in clinical trials using historical data. XXIX International Biometric Conference, Barcelona, Spain, 8–13 July 2018. [Poster]

Talks (国内学会)

口頭発表

1. 折原隼一郎, 大東智洋. ノンパラメトリックベイズを利用した潜在アウトカムの同時分布の推定可能性. 応用統計学会2025年年会, 富山県富山市・富山国際会議場, 2025年5月17日.

2. 大東智洋, 菅澤翔之助. MCMCの再実行を必要としない事前分布の感度解析とtipping-point解析. 2025年度日本計量生物学会年会, 富山県富山市・富山国際会議場, 2025年5月15日–16日.

3. 村崎亘, 大東智洋, 石井亮太, 丸尾和司, 五所正彦. ヒストリカルデータを利用する単群臨床試験のベイズ流症例数設定法の改良. 2025年度日本計量生物学会年会, 富山県富山市・富山国際会議場, 2025年5月15日–16日.

4. 地引涼真, 大東智洋, 寒水孝司. 主要変数が生存時間変数の場合のBayesian additive regression treesを用いた既存データの利用法. 2025年度日本計量生物学会年会, 富山県富山市・富山国際会議場, 2025年5月15日–16日.

5. 折原隼一郎, 大東智洋, 小向翔. ノンパラメトリックベイズを利用した解釈可能なハザード比の推定. 2025年度日本計量生物学会年会, 富山県富山市・富山国際会議場, 2025年5月15日–16日.

6. 松井孝太, 大東智洋, 金森敬文, 土田潤, 坂巻顕太郎. 臨床試験における過去データ適応的利用のための重要度重み付き推定法. 第27回情報論的学習理論ワークショップ (IBIS2024), ソニックシティ（さいたま）, 2024年11月4日–7日.

7. 松井孝太, 大東智洋, 土田潤, 坂巻顕太郎. 転移学習を用いたヒストリカルコントロールデータの動的利用. 2024年度統計関連学会連合大会, 東京理科大学 神楽坂キャンパス, 2024年9月2日–5日.

8. 大東智洋. 階層Bayesian bootstrapを用いた異質因果効果の推定. 2024年度日本計量生物学会年会 特別セッション「異質因果効果の推定：個別化医療選択への理論と実践」, 福岡県福岡市, 2024年5月10–11日. [招待講演]

9. 大東智洋, 丸尾和司, 寒水孝司, 五所正彦. Dirichlet過程混合モデルを用いたクラスタリングによる既存データ利用法の提案. 2023年度日本計量生物学会年会, 北海道札幌市, 2023年4月20–21日. [若手優秀発表賞（正会員部門）]

10. 大東智洋, 丸尾和司, 寒水孝司, 澤本涼, 五所正彦. 既存試験データを利用するためのBayesian shrinkage priorに基づく方法の提案. 2022年度日本計量生物学会年会, 東京都葛飾区, 2022年5月13–14日.

11. 大東智洋, 丸尾和司, 寒水孝司, 五所正彦. 既存試験データを用いたhorseshoe priorに基づく二値応答の群間比較法. 2021年度日本計量生物学会年会, オンライン開催, 2021年5月13–14日.

12. 大東智洋, 丸尾和司, 五所正彦. 背景因子と応答変数に対する新規・既存データの類似度を考慮した併合解析法の提案. 2019年度統計関連学会連合大会, 滋賀県彦根市, 2019年9月8–12日.

Talks (国内セミナー・シンポジウム)

1. 臨床試験におけるdependent Dirichlet過程を用いた既存データの利用法. 科研費シンポジウム 「ベイズ統計学の最前線: 理論から実践まで」. 明治大学駿河台キャンパス, 2024年1月19日. 

2. Hybrid controlのデザインと解析 ② 外部データが要約データの場合の解析手法.  2023年度計量生物セミナー「ベイズ推測と臨床研究への応用」. 中央大学後楽園キャンパス, 2023年12月14日. [PDF]

Softwares

1. Ishii R, Ohigashi T, Maruo K, Gosho M (2023). geessbin: Modified Generalized Estimating Equations for Binary Outcome. [link]

Journal Reviewer

Statistics Journal

Japanese Journal of Biometrics

Medical Journal

BMJ Open