Skip to content
Log in
Sponsored AI AM CLAUDE, A Beginner's Guide: Learn the basics of working with AI AI AM CLAUDE, A Beginner's Guide: Learn the basics of working with AI

Author

Kentaro Takeda

View on Semantic Scholar

3 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Preprint Sep 2026

A staggered seamless dose-optimization design for co-developing monotherapy and combination therapy

Contemporary oncology drug development increasingly requires efficient dose-optimization strategies that evaluate monotherapy (Mono) and combination therapy (Combo) while balancing activity, efficacy, and tolerability. We propose a staggered seamless phase I/II design for settings in which a novel agent is evaluated al...

M. Kojima, Kentaro Takeda, Ying Yuan · 0 citations
Open access Oct 2026

BOIN12-PFS: A Bayesian Optimal Interval Phase I/II Design Incorporating Progression-Free Survival Endpoint.

Modern oncology drug development is increasingly focused on identifying the optimal biological dose (OBD) rather than just the maximum tolerated dose (MTD). Early-phase oncology trials have conventionally relied on short-term endpoints, such as overall response rate (ORR), to evaluate efficacy. However, there is a grow...

Yusuke Tanaka, Kentaro Takeda, R. Kitabayashi et al. · 0 citations
Preprint Sep 2026

Optimal Biological Dose Combination Finding: A Design Roadmap and Robust Cross-Indication Bayesian Borrowing

Early-phase combination oncology trials increasingly seek the optimal biological dose combination (OBDC) rather than a maximum tolerated dose combination, in line with the FDA's Project Optimus initiative. Existing Bayesian OBDC designs span rule-based, model-assisted, and model-based paradigms, but no unified framewor...

Ayon Mukherjee, Kentaro Takeda, J. Wason · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.