●Taguri M, Tsukuma H. Bootstrap estimation of disease incidence proportion with measurement errors. Proceedings in Computational Statistics, 17th Symposium (COMPSTAT 2006), Physica –Verlag/Springer (Alfredo Rizzi and Maurizio Vichi, eds), 2006; 1023–1030.
●Taguri M, Matsuyama Y, Ohashi Y, Sone H, Yoshimura Y, Yamada N. A hierarchical regression model for dietary data adjusting for covariates measurement error by regression calibration: An application to a large prospective study for diabetic complications. Japanese Journal of Biometrics 2010; 31(2): 49–62.
●Chiba Y, Taguri M, Uemura Y. On the identification of the survivor average causal effect. Journal of Biometrics and Biostatistics 2011; 2: e104, 1–3.
●Taguri M, Matsuyama Y, Ohashi Y, Harahada H, Ueshima H. Doubly robust estimation of the generalized impact fraction. Biostatistics 2012; 13(3): 455–467.
●Taguri M, Chiba Y. Instruments and bounds for causal effects under the monotonic selection assumption. International Journal of Biostatistics 2012; 8(1): 24, 1–21.
●Chiba Y, Taguri M. Alternative monotonicity assumptions for improving bounds on natural direct effects. The International Journal of Biostatistics 2013; 9(2): 235–249.
●Chiba Y, Taguri M. Conditional and unconditional infectiousness effects in vaccine trials. Epidemiology 2013; 24(2): 336–337.
●Taguri M, Matsuyama Y. Comments on ‘An information criterion for marginal structural models’ by R. W. Platt, A. M. Brookhart, S. R. Cole, D. Westreich, and E. F. Schisterman. Statistics in Medicine 2013; 32(20): 3590–3591.
●Taguri M, Matsuyama Y, Ohashi Y. Model selection criterion for causal parameters in structural mean models based on a quasi-likelihood. Biometrics 2014; 70(3): 721–730.
●Taguri M, Chiba Y. A principal stratification approach for evaluating natural direct and indirect effects in the presence of treatment-induced intermediate confounding. Statistics in Medicine 2015; 34(1): 131–144.
●Taguri M. Comments on ‘A cautionary note concerning the use of stabilized weights in marginal structural models’ by D. Talbot, J. Atherton, A. M. Rossi, S. L. Bacon, and G. Lefebvre. Statistics in Medicine 2015; 34(8): 1438–1439.
●Taguri M, Izumi S. A global goodness-of-fit test for linear structural mean models. Behaviormetrika 2017; 44(1): 253–262.
●Taguri M, Featherstone J, Cheng J. Causal mediation analysis with multiple causally non-ordered mediators. Statistical Methods in Medical Research 2018; 27(1): 3–19.
●Taguri M, Kuchiba A. Decomposition of the population attributable fraction for two exposures. Annals of Epidemiology 2018; 28(5): 331–334.
●Uemura Y, Taguri M, Kawahara T, Chiba Y. Simple methods for the estimation and sensitivity analysis of principal strata effects using marginal structural models: application to a bone fracture prevention trial. Biometrical Journal 2019; 61(6):1448–1461.
●Orihara S, Hamada E. Double robust estimator in general treatment regimes based on Covariate-balancing. Communications in Statistics-Theory and Methods 2019;48(3): 462-478.
●Takeda K, Morita S, Taguri M. TITE-BOIN-ET: Time-to-event Bayesian optimal interval design to accelerate dose-finding based on both efficacy and toxicity outcomes. Pharm Stat. 2020; 19(3):335–349.
●Orihara S, Hamada E. Determination of the optimal number of strata for propensity score subclassification. Statistics & Probability Letters 2021;168: 108951.
●Harada K, Fujisawa, H. Sparse estimation of Linear Non-Gaussian Acyclic Model for Causal Discovery. Neurocomputing 2021; 459: 223-233.
●Taguri M. Discussion of “Akaike Memorial Lecture 2020: Some of the challenges of statistical applications.” Ann Inst Stat Math. 2022; 74: 643-647.
●Sakamaki K, Taguri M, Nishiuchi H, Akimoto Y, Koizumi K. Experience of distance education for project-based learning in data science. Jpn J Stat Data Sci. 2022.
●Takeda K, Morita S, Taguri M. gBOIN-ET: The generalized Bayesian optimal interval design for optimal dose-finding accounting for ordinal graded efficacy and toxicity in early clinical trials. Biom J. 2022.
●Orihara S, Kawamura T, Taguri M. Comments on ‘A weighting analogue to pair matching in propensity score analysis’ by L. Li and T. Greene. The International Journal of Biostatistics 2022.
●Orihara S, Goto A, Taguri M. Instrumental variable estimation of causal effects with applying some model selection procedures under binary outcomes. Behaviormetrika 2022.
●Harada K, Fujisawa H. Outlier-Resistant Estimators for Average Treatment Effect in Causal Inference. Statistica Sinica 2024; 34(1). (IN PRESS)
●Kojima, M., and Orihara, S. Frailty Model with Change Point for Survival Analysis, Pharmaceutical Statistics, accepted.2023. 
●Mano, H., Tanaka, Y., Orihara, S., and Moriya, J.  Application of Sample Size Re-Estimation in Clinical Trials: A Systematic Review. Contemporary Clinical Trials Communications,2023; 36, 101210.
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