Paper

・Nishikimi M, Matsuda N, Matsui K, Takahashi K, Ejima T, Liu K, Ogura T, Higashi M, Umino H, Makishi G, Numaguchi A, Matsushima S, Tokuyama H, Nakamura M, Matsui S. CAST: a new score for early prediction of neurological outcomes after cardiac arrest before therapeutic hypothermia with high accuracy. Intensive Care Med. 2016; 42(12):　2106-2107.

・Ishikawa T, Uetake H, Murotani K, Kobunai T, Ishiguro M, Matsui S, Sugihara K. Genome-wide DNA copy-number analysis in ACTS-CC trial of adjuvant chemotherapy for stage III colonic cancer. Anticancer Res. 2016; 36(3): 853-860.

・Sharma A, Shigemizu D, Boroevich KA, López Y, Kamatani Y, Kubo M, Tsunoda T. Stepwise iterative maximum likelihood clustering approach. BMC Bioinformatics. 2016; 17(1): 319.

・Sharma R, Kumar S, Tsunoda T, Patil A, Sharma A. Predicting MoRFs in protein sequences using HMM profiles. BMC Bioinformatics. 2016; 17(Suppl 19): 504

・Saini H, Lal SP, Naidu VV, Pickering VW, Singh G, Tsunoda T, Sharma A. Gene masking – a technique to improve accuracy for cancer classification with high dimensionality in microarray data. BMC Med Genomics. 2016; 9(Suppl 3): 74.

・Hattori S, Zhou XH. Evaluation of predictive capacities of biomarkers based on research synthesis. Stat Med. 2016; 35(25): 4559-4572.

・Hattori S, Zhou XH. Time-dependent summary receiver operating characteristics for meta-analysis of prognostic studies. Stat Med. 2016; 35(26):4746-4763.

・Hattori S, Zhou XH. Evaluation of predictive capacities of biomarkers based on research synthesis. Stat Med. 2016; 35(25): 4559-4572.

・Sadashima E, Hattori S, Takahashi K. Meta-analysis of prognostic studies for a biomarker with a study-specific cutoff value. Res Synth Methods. 2016; 7(4): 402-419.

Symposium/Workshop

・シンポジウム「統計科学が切り拓く個別化医療：方法論・実践のフロンティア」（2017年3月27-28日、アクロス福岡）

・統計学・機械学習若手シンポジウム「大規模複雑データに対する統計・機械学習のアプローチ」が開催されます（2017年9月15-17日、名古屋工業大学）

・Second Pacific Rim Cancer Biostatistics Workshop, Oct.12-13, 2017, Kanazawa, Japan.

Book

・Matsui S, Crowley J (eds.). Frontiers of Biostatistical Methods and Applications in Clinical Oncology. Springer, 2017.

Book Chapter

・Matsui S. Phase III clinical trial designs incorporating predictive biomarkers: an overview. In Frontiers of Biostatistical Methods and Applications in Clinical Oncology (eds: Matsui S, Crowley J), pp. 85-103, Springer, 2017.

・Hattori S, Zhou XH. Meta-analysis of prognostic studies evaluating time-dependent diagnostic and predictive capacities of biomarkers. In Frontiers of Biostatistical Methods and Applications in Clinical Oncology (eds: Matsui S, Crowley J), pp. 257-273. Springer, 2017.

・Noma, H. Efficient study designs and semiparametric inference methods for developing genomic biomarkers in cancer clinical research. In Frontiers of Biostatistical Methods and Applications in Clinical Oncology (eds: Matsui S, Crowley J), pp. 381-400. Springer, 2017.

・Kawaguchi A. Supervised dimension-reduction methods for brain tumor image data analysis. In Frontiers of Biostatistical Methods and Applications in Clinical Oncology (eds: Matsui S, Crowley J), pp. 401-411. Springer, 2017.

Paper

・Toyoizumi K, Matsui S. Correcting estimation bias in randomized clinical trials with a test of treatment-by-biomarker interaction. Statistics in Biopharmaceutical Research 2017; 9(2), 172-179.

・Nishikimi M, Matsuda N, Matsui K, Takahashi K, Ejima T, Liu K, Ogura T, Higashi M, Umino H, Makishi G, Numaguchi A, Matsushima S, Tokuyama H, Nakamura M, Matsui S. A novel scoring system for predicting the neurologic prognosis prior to the initiation of induced hypothermia in cases of post-cardiac arrest syndrome: the CAST score. Scand J Trauma Resusc Emerg. 2017; 25(1): 49.

・Sugasawa S, Noma H, Otani T, Nishino J, Matsui S. An efficient and flexible test for rare variant effects. Eur J Hum Genet. 2017; 25(6): 752-757.

・Okumura Y, Sakata N, Takahashi K, Nishi D, Tachimori H. Epidemiology of overdose episodes from the period prior to hospitalization for drug poisoning until discharge in Japan: An exploratory descriptive study using a nationwide claims database. J Epidemiol. 2017; 27(8): 373-380.

・Takahashi K, Tachimori H, Kan C, Nishi D, Okumura Y, Kato N, Takeshima T. Spatial analysis for regional behavior of patients with mental disorders in Japan. Psychiatry Clin Neurosci. 2017; 71(4): 254-261.

・Sharma A, Boroevich KA, Shigemizu D, Kamatani Y, Kubo M, Tsunoda T. Hierarchical maximum likelihood clustering approach. IEEE Trans Biomed Eng. 2017; 64(1): 112-122.

・Sharma A, López Y, Tsunoda T. Divisive hierarchical maximum likelihood clustering. BMC Bioinformatics. 2017; 18(Suppl 16): 546.

・Kumar S, Sharma A, Tsunoda T. An improved discriminative filter bank selection approach for motor imagery EEG signal classification using mutual information. BMC Bioinformatics. 2017; 18(Suppl 16): 545.

・Sharma A, Kamola PJ, Tsunoda T. 2D-EM clustering approach for high-dimensional data through folding feature vectors. BMC Bioinformatics. 2017; 18(Suppl 16): 547.

・Shigemizu D, Iwase T, Yoshimoto M, Suzuki Y, Miya F, Boroevich KA, Katagiri T, Zembutsu H, Tsunoda T. The prediction models for postoperative overall survival and disease-free survival in patients with breast cancer. Cancer Med. 2017; 6(7): 1627-1638.

・Komukai S, Hattori S. Doubly robust estimator for net survival rate in analyses of cancer registry data. Biometrics. 2017; 73(1): 124-133.

・Tanoue Y, Yamashita S. (2017) When banks venture beyond home turf: consequences for loan performance. Journal of Credit Risk, 13, 1-19.

・Tanoue Y, Kawada A, Yamashita S (2017) Forecasting loss given default of bank loans with multi-stage model. International Journal of Forecasting, 33, 513-522.

Symposium/Workshop

・統計学・機械学習若手シンポジウム「統計・機械学習の交わりと拡がり」（2018年8月10-12日、一橋大学一橋講堂）

Book

・Hirakawa A, Sato H, Daimon T, Matsui S. Modern Dose-Finding Designs for Cancer Phase I Trials: Drug Combination and Molecularly Targeted Agents. Springer, 2018.

・丹後俊郎，松井茂之（編）．新版医学統計学ハンドブック．朝倉書店, 2018.

Paper

・Matsui S, Crowley J. Biomarker-stratified phase III clinical trials: Enhancement with a subgroup-focused sequential design. Clin Cancer Res. 2018; 24(5): 994-1001

・Matsui S, Noma H, Qu P, Sakai Y, Matsui K, Heuck C, Crowley J. Multi-subgroup gene screening using semi-parametric hierarchical mixture models and the optimal discovery procedure: Application to a randomized clinical trial in multiple myeloma. Biometrics. 2018; 74(1): 313-320.

・Shimamura F, Hamada C, Matsui S, Hirakawa A. Two-stage approach based on zone and dose findings for two-agent combination Phase I/II trials. J Biopharm Stat. 2018; 28(6):1025-1037.

・Otani T, Noma H, Nishino J, Matsui S. Re-assessment of multiple testing strategies for more efficient genome-wide association studies. Eur J Hum Genet. 2018; 26(7): 1038-1048.

・Emura T, Nakatochi M, Matsui S, Michimae H, Rondeau V. Personalized dynamic prediction of death according to tumour progression and high-dimensional genetic factors: Meta-analysis with a joint model. Statistical Methods in Medical Research 2018; 27(9): 2842-2858.

・Nishino J, Kochi Y, Shigemizu D, Kato M, Ikari K, Ochi H, Noma H, Matsui K, Morizono T, Boroevich KA, Tsunoda T, Matsui S. Empirical Bayes estimation of semi-parametric hierarchical mixture models for unbiased characterization of polygenic disease architectures. Front Genet. 2018; 9: 115.

・Nishino J, Ochi H, Kochi Y, Tsunoda T, Matsui S. Sample size for successful genome-wide association study of major depressive disorder. Front Genet. 2018; 9: 227.

・Ogura T, Nakamura Y, Takahashi K, Nishida K, Kobashi D, Matsui S. Treatment of patients with sepsis in a closed intensive care unit is associated with improved survival: a nationwide observational study in Japan. J Intensive Care. 2018; 6: 57.

・Horisaki K, Takahashi K, Ito H, Matsui S. A Dose-Response Meta-analysis of Coffee Consumption and Colorectal Cancer Risk in the Japanese Population: Application of a Cubic-Spline Model. J Epidemiol. 2018; 28(12): 503-509.

・Takahashi K, Shimadzu H. Multiple-cluster detection test for purely temporal disease clustering: Integration of scan statistics and generalized linear models. PLoS One. 2018; 13(11): e0207821.

・Kuriki S, Takahashi K, Hara H. Multiplicity adjustment for temporal and spatial scan statistics using Markov property. Japanese Journal of Statistics and Data Science. 2018; 1(1), 191-213.

・Chandra A, Sharma A, Dehzangi A, Ranganathan S, Jokhan A, Chou KC, Tsunoda T. PhoglyStruct: Prediction of phosphoglycerylated lysine residues using structural properties of amino acids. Sci Rep. 2018; 8(1): 17923.

・Dehzangi A, López Y, Taherzadeh G, Sharma A, Tsunoda T. SumSec: Accurate Prediction of Sumoylation Sites Using Predicted Secondary Structure. Molecules. 2018; 23(12). pii: E3260.

・Lysenko A, Sharma A, Boroevich KA, Tsunoda T. An integrative machine learning approach for prediction of toxicity-related drug safety. Life Sci Alliance. 2018; 1(6): e201800098.

・Shigemizu D, Miya F, Akiyama S, Okuda S, Boroevich KA, Fujimoto A, Nakagawa H, Ozaki K, Niida S, Kanemura Y, Okamoto N, Saitoh S, Kato M, Yamasaki M, Matsunaga T, Mutai H, Kosaki K, Tsunoda T. IMSindel: An accurate intermediate-size indel detection tool incorporating de novo assembly and gapped global-local alignment with split read analysis. Sci Rep. 2018; 8(1): 5608.

・Dehzangi A, López Y, Lal SP, Taherzadeh G, Sattar A, Tsunoda T, Sharma A. Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information from profile bigrams. PLoS One. 2018; 13(2): e0191900.

・López Y, Sharma A, Dehzangi A, Lal SP, Taherzadeh G, Sattar A, Tsunoda T. Success: evolutionary and structural properties of amino acids prove effective for succinylation site prediction. BMC Genomics. 2018; 19(Suppl 1):923.

・Sharma R, Raicar G, Tsunoda T, Patil A, Sharma A. OPAL: prediction of MoRF regions in intrinsically disordered protein sequences. Bioinformatics. 2018; 34(11): 1850-1858.

・Hattori S, Zhou XH. Sensitivity analysis for publication bias in meta-analysis of diagnostic studies for a continuous biomarker. Stat Med. 2018; 37(3): 327-342.

・Nomura T, Hattori S. Estimation of the average causal effect via multiple propensity score stratification Communications in Statistics – Simulation and Computation. 2018; 47(1), 48-62.

解説

・松井茂之. (2018). オミクス研究における検証的解析と探索的解析：多重検定とP 値を中心に．計量生物学 38, 127-139

Symposium/Workshop

・Third Pacific Rim Cancer Biostatistics Workshop, Jun. 27-28, 2019, Portland, USA.
http://www.wnarpacificrim.com/pacificrim/

Book

・Emura T, Chen YH, Matsui S, Rondeau V. Survival Analysis with Correlated Endpoints Subtitle: Joint Frailty-Copula Models, Springer, 2019.

・Daimon T, Hirakawa A, Matsui S. Dose-Finding Designs for Early-Phase Cancer Clinical Trials: A Brief Guidebook to Theory and Practice, Springer, 2019.

Book Chapter

・Matsui S, Igeta M, Toyoizumi K. Biomarker-based phase II and III clinical trials in oncology. In Textbook of Clinical Trials in Oncology. (eds. S. Halabi and S. Michiels), CRC Press, 2019.

・Emoto R, Kawaguchi A, Otani T, Matsui S. A model-based framework for voxel and region level inferences in neuroimaging disease-association studies. Proceeding in the 16th International Conference on Information Technology: New Generations, Springer, 2019.

・Otani T, Nishino J, Emoto R, Matsui S. Application of the multi-dimensional hierarchical mixture model to cross-disorder genome-wide association studies. Proceeding in the 16th International Conference on Information Technology: New Generations, Springer, 2019.

Paper

・Emura T, Matsui S, Chen HY. compound.Cox: Univariate feature selection and compound covariate for predicting survival. Comput Methods Programs Biomed. 2019; 168: 21-37.

・Otani T, Noma H, Sugasawa S, Kuchiba A, Goto A, Yamaji T, Kochi Y, Iwasaki M, Matsui S, Tsunoda T. Exploring predictive biomarkers from clinical genome-wide association studies via multidimensional hierarchical mixture models. Eur J Hum Genet. 2019; 27(1): 140-149.

・Igeta M, Takahashi K, Matsui S. Power and sample size calculation incorporating misspecifications of the variance function in comparative clinical trials with over-dispersed count data. Biometrics. 2019 (In press).

・Saqi M, Lysenko A, Guo YK, Tsunoda T, Auffray C. Navigating the disease landscape: knowledge representations for contextualizing molecular signatures. Brief Bioinform. 2019 (In press).

・Kawabata T, Emoto R, Nishino J, Takahashi K, Matsui S. (2019). Two-stage analysis for selecting fixed numbers of features in omics association studies. Statistics in Medicine (In press).

・Nonaka T, Igeta M, Matsui S. (2019). Statistical testing strategies for assessing treatment efficacy and marker accuracy in phase III trials. Pharmaceutical Statistics (In press).