Bayesian nonparametric estimation of targeted agent effects on biomarker change to predict clinical outcome.

ABSTRACT: The effect of a targeted agent on a cancer patient's clinical outcome putatively is mediated through the agent's effect on one or more early biological events. This is motivated by pre-clinical experiments with cells or animals that identify such events, represented by binary or quantitative biomarkers. When evaluating targeted agents in humans, central questions are whether the distribution of a targeted biomarker changes following treatment, the nature and magnitude of this change, and whether it is associated with clinical outcome. Major difficulties in estimating these effects are that a biomarker's distribution may be complex, vary substantially between patients, and have complicated relationships with clinical outcomes. We present a probabilistically coherent framework for modeling a

SUBMITTER: Graziani R 

PROVIDER: S-EPMC4383707 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature