Unknown

Dataset Information

An extended Bayesian semi-mechanistic dose-finding design for phase I oncology trials using pharmacokinetic and pharmacodynamic information.


ABSTRACT: We propose a model-based, semi-mechanistic dose-finding (SDF) design for phase I oncology trials that incorporates pharmacokinetic/pharmacodynamic (PK/PD) information when modeling the dose-toxicity relationship. This design is motivated by a phase Ib/II clinical trial of anti-CD20/CD3 T cell therapy in non-Hodgkin lymphoma patients; it extends a recently proposed SDF model framework by incorporating measurements of a PD biomarker relevant to the primary dose-limiting toxicity (DLT). We propose joint Bayesian modeling of the PK, PD, and DLT outcomes. Our extensive simulation studies show that on average the proposed design outperforms some common phase I trial designs, including modified toxicity probability interval (mTPI) and Bayesian optimal interval (BOIN) designs, the continual reasse

SUBMITTER: Yang C 

PROVIDER: S-EPMC11583997 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature