Sie befinden Sich nicht im Netzwerk der Universität Paderborn. Der Zugriff auf elektronische Ressourcen ist gegebenenfalls nur via VPN oder Shibboleth (DFN-AAI) möglich. mehr Informationen...
A Bayesian phase I/II biomarker-based design for identifying subgroup-specific optimal dose for immunotherapy
Ist Teil von
Statistical methods in medical research, 2022-06, Vol.31 (6), p.1104-1119
Ort / Verlag
London, England: SAGE Publications
Erscheinungsjahr
2022
Link zum Volltext
Quelle
Applied Social Sciences Index & Abstracts (ASSIA)
Beschreibungen/Notizen
Immunotherapy is an innovative treatment that enlists the patient’s immune system to battle tumors. The optimal dose for treating patients with an immunotherapeutic agent may differ according to their biomarker status. In this article, we propose a biomarker-based phase I/II dose-finding design for identifying subgroup-specific optimal dose for immunotherapy (BSOI) that jointly models the immune response, toxicity, and efficacy outcomes. We propose parsimonious yet flexible models to borrow information across different types of outcomes and subgroups. We quantify the desirability of the dose using a utility function and adopt a two-stage dose-finding algorithm to find the optimal dose for each subgroup. Simulation studies show that the BSOI design has desirable operating characteristics in selecting the subgroup-specific optimal doses and allocating patients to those optimal doses, and outperforms conventional designs.