各種疾患領域における臨床研究を通じて、疾病制御に関するエビデンスを提示してゆきます。

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医学医療・生物学領域での研究成果は、データ解析の結果として報告されます。それもあって、多くの人は「統計学とはデータ解析である」と思っている節がありますが、実際には、その研究の計画（デザイン）、実施、解析、報告のあらゆる過程で統計学が必要になります。

その研究仮説（上の例では、新治療が既存治療よりも優れているという仮説）を検証するためにはどのような研究デザインが最適か、研究の目的を反映した評価変数を設定できているか、その評価変数は信頼でき妥当か、既存治療としては何を設定すれば良いのか、研究期間、測定時点、サンプルサイズは必要十分となっているかなど、研究の計画段階から統計学を駆使し、きちんとデータが収集できるように事前に準備しておく必要があります。

きちんと計画していない研究から得られたデータでは、せっかく解析しても正しい結果が得られません。食材（データ）が悪ければ、腕のたつ料理人が調理（解析）しても、美味しい料理にはならないでしょう。逆に、素晴らしい食材を手に入れても、へたな料理人にかかってはせっかくの食材が台無しです。我々は、計画から報告までの過程で生じる統計学的問題を解決するための研究を行っています。

方法論的研究やそれに関連する最近の主な論文業績は以下のとおりです

2022

1. Ishii R, Maruo K, Gosho M (2022). Effect of Covariate Omission in Randomised Controlled Trials: A Review and Simulation Study. International Statistical Review 90, 100–117.
2. Tada K, Gosho M (2022). Increased risk of urinary tract infection and pyelonephritis under concomitant use of sodium dependent glucose cotransporter 2 inhibitors with antidiabetic, antidyslipidemic and antihypertensive drugs: An observational study. Fundamental & Clinical Pharmacology (doi: 10.1111/fcp.12792).
3. Ohigashi T, Maruo K, Sozu T, Gosho M (2022). Using horseshoe prior for incorporating multiple historical control data in randomized controlled trials. Statistical Methods in Medical Research (doi: 10.1177/09622802221090752).
4. Yamaguchi Y, Yoshida S, Misumi T, Maruo K (2022). Multiple imputation for longitudinal data using Bayesian lasso imputation model. Statistics in Medicine 41, 1042–1058.

2021

1. Gosho M, Ohigashi T, Nagashima K, Ito Y, Maruo K (2021). Bias in odds ratios from logistic regression methods with sparse data sets. Journal of Epidemiology (doi: 10.2188/jea.JE20210089).
2. Gosho M, Noma H, Maruo K (2021). Practical Review and Comparison of Modified Covariance Estimators for Linear Mixed Models in Small-sample Longitudinal Studies with Missing Data. International Statistical Review 89, 550–572.
3. Gosho M, Maruo K (2021). An application of the mixed-effects model and pattern mixture model to treatment groups with differential missingness suspected not-missing-at-random. Pharmaceutical Statistics 20, 93–108.
4. Maruo K, Ishii R, Yamaguchi Y, Gosho M (2021). bcmixed: A Package for Median Inference on Longitudinal Data with the Box–Cox Transformation. The R Journal 13, 253–265.
5. Ishii R, Maruo K, Noma H, Gosho M (2021). Statistical Inference Based on Accelerated Failure Time Models Under Model Misspecification and Small Samples. Statistics in Biopharmaceutical Research 13, 384–394.
6. Noma H, Matsushima Y, Ishii R (2021). Confidence interval for the AUC of SROC curve and some related methods using bootstrap for meta-analysis of diagnostic accuracy studies. Communications in Statistics: Case Studies, Data Analysis and Applications 7, 344–358.
7. Iba K, Shinozaki T, Maruo K, Noma H (2021). Re-evaluation of the comparative effectiveness of bootstrap-based optimism correction methods in the development of multivariable clinical prediction models. BMC Medical Research Methodology 21, 9.
8. Nagashima K, Noma H, Sato Y, Gosho M (2021). Sample size calculations for single‐arm survival studies using transformations of the Kaplan–Meier estimator. Pharmaceutical Statistics 20, 499–511.

2020

1. Maruo K, Ishii R, Yamaguchi Y, Doi M, Gosho M (2020). A note on the bias of standard errors when orthogonality of mean and variance parameters is not satisfied in the mixed model for repeated measures analysis. Statistics in Medicine 39, 1264–1274.
2. Tada K, Maruo K, Isogawa N, Yamaguchi Y, Gosho M (2020). Borrowing external information to improve Bayesian confidence propagation neural network. European Journal of Clinical Pharmacology 76, 1311–1319.
3. Noma H, Gosho M, Ishii R, Oba K, Furukawa TA (2020). Outlier detection and influence diagnostics in network meta-analysis. Research Synthesis Methods 11, 891–902.
4. Shimura M, Nomura S, Wakabayashi M, Maruo K, Gosho M (2020). Assessment of hazard ratios in oncology clinical trials terminated early for superiority: a systematic review. JAMA Network Open 3, e208633.
5. Uozumi R, Yada S, Maruo K, Kawaguchi A (2020). Confidence intervals for difference between two binomial proportions derived from logistic regression. Communications in Statistics - Simulation and Computation (doi: 10.1080/03610918.2019.1710195).
6. Yamaguchi Y, Ueno M, Maruo K, Gosho M (2020). Multiple imputation for longitudinal data in the presence of heteroscedasticity between treatment groups. Journal of Biopharmaceutical Statistics 30, 178–196.

2019

1. Gosho M (2019). Rhabdomyolysis risk from the use of two-drug combination of antidyslipidemic drugs with antihypertensive and antidiabetic medications: A signal detection analysis. Fundamental & Clinical Pharmacology 33, 339–346.
2. Isogawa N, Takeda K, Maruo K, Daimon T (2019). A Comparison Between a Meta-analytic Approach and Power Prior Approach to Using Historical Control Information in Clinical Trials With Binary Endpoints. Therapeutic Innovation & Regulatory Science (doi: 10.1177/2168479019862531).
3. Yamaguchi Y, Maruo K (2019). Bivariate beta‑binomial model using Gaussian copula for bivariate meta‑analysis of two binary outcomes with low incidence. Japanese Journal of Statistics and Data Science 2, 347–373.
4. Noma H, Maruo K, Gosho M, Levine SZ, Goldberg Y, Leucht S, Furukawa TA (2019). Efficient two-step multivariate random effects meta-analysis of individual participant data for longitudinal clinical trials using mixed effects models. BMC Medical Research Methodology 19, 33.
5. Ukyo Y, Noma H, Maruo K, Gosho M (2019). Improved small sample inference methods for a mixed effects models for repeated measures approach in incomplete longitudinal data analysis. Stats 2, 174–188.

2018

1. Gosho M, Sato Y, Nagashima K, Takahashi S (2018). Trends in study design and the statistical methods employed in a leading general medicine journal. Journal of Clinical Pharmacy and Therapeutics 43, 36–44.
2. Gosho M, Maruo K, Ishii R, Hirakawa A (2018). Analysis of an incomplete longitudinal composite variable using a marginalized random effects model and multiple imputation. Statistical Methods in Medical Research 27, 2200–2215.
3. Gosho M (2018). Risk of hypoglycemia after concomitant use of antidiabetic, antihypertensive, and antihyperlipidemic medications: A database study. Journal of Clinical Pharmacology 58, 1324–1331.
4. Gosho M, Maruo K (2018). Effect of heteroscedasticity between treatment groups on mixed-effects models for repeated measures. Pharmaceutical Statistics 17, 578–592.
5. Gosho M, Ohigashi T, Maruo K (2018). SignalDetDDI: An SAS macro for detecting adverse drug-drug interactions in spontaneous reporting systems. PLoS One 13(11): e0207487.
6. Maruo K, Tada K, Ishii R, Gosho M (2018). An efficient procedure for calculating sample size through statistical simulations. Statistics in Biopharmaceutical Research 10, 1–8.
7. Shimura M, Maruo K, Gosho M (2018). Conditional estimation using prior information in 2‐stage group sequential designs assuming asymptotic normality when the trial terminated early. Pharmaceutical Statistics 17, 400–413.
8. Sato A, Shimura M, Gosho M (2018). Practical characteristics of adaptive design in phase 2 and 3 clinical trials. Journal of Clinical Pharmacy and Therapeutics. 43, 170–180.
9. Yamaguchi Y, Misumi T, Maruo K (2018). A comparison of multiple imputation methods for incomplete longitudinal binary data. Journal of Biopharmaceutical Statistics 28, 645–667.
10. Noma H, Nagashima K, Maruo K, Gosho M, Furukawa TA (2018). Bartlett-type corrections and bootstrap adjustments of likelihood-based inference methods for network meta-analysis. Statistics in Medicine 37, 1178–1190.

2017

1. Gosho M, Hirakawa A, Noma H, Maruo K, Sato Y (2017). Comparison of bias-corrected covariance estimators for MMRM analysis in longitudinal data with dropouts. Statistical Methods in Medical Research. 26, 2389–2406.
2. Gosho M, Maruo K, Tada K, Hirakawa A (2017). Utilization of chi-square statistics for screening adverse drug-drug interactions in spontaneous reporting systems. European Journal of Clinical Pharmacology 73, 779–786.
3. Maruo K, Yamaguchi Y, Noma H, Gosho M (2017). Interpretable inference on the mixed effect model with the Box–Cox transformation. Statistics in Medicine 36, 2420–2434.
4. Maruo K, Yamabe T, Yamaguchi Y (2017). Statistical simulation based on right skewed distributions. Computational Statistics 32, 889–907.
5. Shimura M, Gosho M, Hirakawa A (2017). Comparison of conditional bias-adjusted estimators for interim analysis in clinical trials with survival data. Statistics in Medicine 36, 2067–2080.
6. Yamaguchi Y, Maruo K, Partlett C, Riley RD (2017). A random effects meta-analysis model with Box–Cox transformation. BMC Medical Research Methodology 17, 109.
7. Sato Y, Gosho M, Nagashima K, Takahashi S, Ware JM, Laird NM (2017). Statistical Methods in the Journal — an update. New England Journal of Medicine 376, 1086–1087.

また、当グループでは、以下の書籍を出版し、研究・教育に役立てています

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  五所正彦 監訳, 佐藤泰憲, 竹内久朗, 長島健悟, 中水流嘉臣, 平川晃弘, 松永信人, 山田雅之 (2014). 生存時間解析入門 原書第2版. 東京大学出版会. (Hosmer DW, Lemeshow S, May S. Applied Survival Analysis: Regression Modeling of Time-to-Event Data, 2nd. Wiley, 2008.)

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  佐藤泰憲, 五所正彦 (2014). ゼロから学ぶ 医薬統計教室. メジカルビュー社

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  佐久間昭 著, 五所正彦 編, 酒井弘憲 編, 佐藤泰憲 編, 竹内久朗 編 (2017). 新版 薬効評価. 東京大学出版会

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  五所正彦 (2017). イチから使う 医薬統計教室 －SPSS－. メジカルビュー社

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  平川晃弘, 五所正彦 監訳, 安藤英一, 佐藤泰憲, 高橋翔, 竹内久郎, 長島健悟, 中水流嘉臣, 野間久史, 藤井陽介, 松岡信篤, 松永信人, 丸尾和司, 山田将之 (2018). 臨床試験のためのアダプティブデザイン. 朝倉書店. (Chow SC, Chang M. Adaptive Design Methods in Clinical Trials, Second Edition. CRC Press, 2012.)

生物統計学の実践的研究も行っています。実際の医学研究（臨床試験や基礎試験）に対して、生物統計学の専門家として参画しています。適切な研究デザインを立案すること、得られたデータを解析すること、結果を誤りなく報告することを目指し、医学研究に貢献しています。実践的研究の研究成果の詳細は担当教員のページTRIOSをご参照ください。

また、筑波大学つくば臨床医学研究開発機構（Tsukuba Clinical Research & Development Organization: T-CReDO）では、試験統計家の立場から、試験計画の立案、統計解析の実施、論文作成・査読対応、統計相談等の業務を通じて、医薬品や医療機器の承認申請にも携わっています。

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