“Improving the performance of Bayesian logistic regression model with overdose control in oncology dose‐finding studies” by Hongtao Zhang, Alan Chiang, and Jixian Wang
Sign Up to like & getrecommendations! Published in 2022 at "Statistics in Medicine"
DOI: 10.1002/sim.9494
Abstract: In their paper, Zhang et al 1 propose further extensions of the Bayesian Logistic Regression Model (BLRM) with overdose control for dose-escalation studies of a novel drug. These extensions aim to reduce the risk of… read more here.
Keywords: oncology; logistic regression; zhang; overdose control ... See more keywords
Commentary on “Improving the performance of Bayesian logistic regression model with overdose control in oncology dose‐finding studies”
Sign Up to like & getrecommendations! Published in 2022 at "Statistics in Medicine"
DOI: 10.1002/sim.9496
Abstract: The Bayesian logistic regression model (BLRM) design is a variation of the continuous reassessment method (CRM). Due to the use of an excessively tight escalation with overdose control (EWOC) rule, BLRM has high tendency to… read more here.
Keywords: oncology; logistic regression; overdose control; bayesian logistic ... See more keywords
Generalizing expectation propagation with mixtures of exponential family distributions and an application to Bayesian logistic regression
Sign Up to like & getrecommendations! Published in 2019 at "Neurocomputing"
DOI: 10.1016/j.neucom.2019.01.065
Abstract: Abstract Expectation propagation (EP) is a widely used deterministic approximate inference algorithm in Bayesian machine learning. Traditional EP approximates an intractable posterior distribution through a set of local approximations which are updated iteratively. In this… read more here.
Keywords: expectation propagation; bayesian logistic; logistic regression;