A novel Bayesian generative approach for estimating tumor dynamics from published studies.

ABSTRACT: Tumor growth inhibition (TGI) modeling attempts to describe the time course changes in tumor size for patients undergoing cancer therapy. TGI models present several advantages over traditional exposure-response models that are based explicitly on clinical end points and have become a popular tool in the pharmacometrics community. Unfortunately, the data required to fit TGI models, namely longitudinal tumor measurements, are sparse or often not available in literature or publicly accessible databases. On the contrary, common end points such as progression-free survival (PFS) and objective response rate (ORR) are directly derived from longitudinal tumor measurements and are routinely published. To this end, a Bayesian generative model relating underlying tumor dynamics to summary PFS and ORR

SUBMITTER: Pourzanjani A 

PROVIDER: S-EPMC11330188 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature