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Biostatistics Group

Applied statistics for medical and
biological research

Biostatistics is an applied statistics for medical and biological research, and aims to develop, apply, advice on statistical methodology for study design and data analysis in the research. The Department of Biostatistics at the Faculty of Medicine at the University of Tsukuba contributes to solve problems in human health using statistical approaches. The Department also provides an environment to pursue research and education in biostatistics. The biostatistical research in the department is categorized by methodology and practice.

Biostatistical Methodology

The department develops the novel statistical methods for health researches and addresses the issue as follows

Study design
  • Sample size determination
  • Non-inferiority trial
  • Interim analysis
  • Adaptive design methods
Data analysis
  • Longitudinal data analysis
  • Missing data analysis
  • Survival analysis
  • Small-sample data analysis
  • Skewed data analysis
  • Transformation of data
  • Signal detection methods
  • Using historical control data

Recent publications in methodological researches in biostatistics

  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.
  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.
  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.
  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.
  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.
  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.

Biostatistical Practice

In practice, the department applies data science to solve problems in medical and biological researches through designing studies, organizing and analyzing data, and collaborating on multidisciplinary research teams.

In the Tsukuba Clinical Research & Development Organization: T-CReDO, the faculty members are designing clinical trials, conducting data analysis, writing medical articles, and consulting on statistical issues in many medical fields as trial statisticians.