collaborators

6 papers

stat.ME2026

Covariate-Adaptive Sample Size Re-estimation for Population-Standardized Historical Control Designs in Single-Arm Trials

Keisuke Hanada, Masahiro Kojima

Externally controlled single-arm trials are increasingly considered when randomized controls are infeasible, but baseline imbalance between the active-arm trial and historical cont…

stat.ME2026

A Clustering Approach for Basket Trials Based on Treatment Response Trajectories

Masahiro Kojima, Keisuke Hanada, Atsuya Sato

Heterogeneity in efficacy is sometimes observed across baskets in basket trials. In this study, we propose a model-free clustering framework that groups baskets based on transition…

stat.ME2026

Differentially Private One-Shot Federated Inference for Linear Mixed Models via Lossless Likelihood Reconstruction

Keisuke Hanada, Toshio Shimokawa, Kazushi Maruo

One-shot federated learning enables multi-site inference with minimal communication. However, sharing summary statistics can still leak sensitive individual-level information when…

stat.ME2026

Hybrid Non-informative and Informative Prior Model-assisted Designs for Mid-trial Dose Insertion

Kana Yamada, Hisato Sunami, Kentaro Takeda +2

In oncology phase I trials, model-assisted designs have been increasingly adopted because they enable adaptive yet operationally simple dose adjustment based on accumulating safety…

stat.ME2025

Sample size re-estimation in blinded hybrid-control design using inverse probability weighting

Masahiro Kojima, Shunichiro Orihara, Keisuke Hanada +1

With the increasing availability of data from historical studies and real-world data sources, hybrid control designs that incorporate external data into the evaluation of current s…

stat.ME2025

Integrate Meta-analysis into Specific Study (InMASS) for Estimating Conditional Average Treatment Effect

Keisuke Hanada, Masahiro Kojima

Randomized controlled trials are the standard method for estimating causal effects, ensuring sufficient statistical power and confidence through adequate sample sizes. However, ach…