13 papers · 1 filter
Feedback-Aware Tuning of Recursive Q-Learning
Masahiro Kojima
Model choice in backward Q-learning is recursive because a later-stage choice changes the response supplied to an earlier regression and can alter its model-comparison statistic. S…
Finite-Boundary Reduction and Exact Verification of Strong Familywise Error in Active-Count-Coupled Multi-Arm Efficacy-Toxicity Monitoring
Masahiro Kojima, Hisato Sunami, Kentaro Takeda
Randomized dose-optimization trials may screen several candidate doses using binary efficacy and toxicity outcomes. A dose is inadmissible if efficacy is insufficient or toxicity i…
A staggered seamless dose-optimization design for co-developing monotherapy and combination therapy
Masahiro Kojima, Kentaro Takeda, Ying Yuan
Contemporary oncology drug development increasingly requires efficient dose-optimization strategies that evaluate monotherapy (Mono) and combination therapy (Combo) while balancing…
A seamless dose-optimization design for monotherapy and combination therapy
Kentaro Takeda, Masahiro Kojima
The emergence of molecular-targeted agents and immune-oncology therapies has fundamentally transformed oncology drug development, necessitating evolution beyond traditional dose-fi…
A Globally Calibrated Bayesian Optimal Phase II Design for Adaptive Enrichment Trials
Masahiro Kojima, Hisato Sunami, Masaaki Kuriki
Adaptive enrichment allows development of an experimental treatment to continue when its activity is insufficient in an all-comer population but remains promising in a prespecified…
Fast Power Evaluation under Biased-Coin Minimization: Sampling and Randomization Calibration
Masahiro Kojima
Design-stage power and sample-size evaluation under biased-coin minimization can be computationally intensive when a prespecified randomization test is reproduced within every simu…