A1066
Title: Reparametrized Firth's logistic regressions for dose finding study with the biased coin design
Authors: Hyungwoo Kim - Pukyong National University, Department of Statistics and Data Science (Korea, South) [presenting]
Abstract: Finding an adequate dose of the drug by revealing the dose-response relationship is very crucial and a challenging problem in clinical development. The focus is on identifying a minimum effective dose (MED) in anesthesia studies and a maximum tolerated dose (MTD) in oncology clinical trials. For the estimation of MED and MTD, two modifications of Firth's logistic regression are proposed using reparametrization, called reparametrized Firth's logistic regression (rFLR) and Ridge-penalized reparametrized Firth's logistic regression (RrFLR). The proposed methods are designed by directly reducing the small-sample bias of the maximum likelihood estimate for the parameter of interest. In addition, a method is developed on how to construct confidence intervals for rFLR and RrFLR using profile penalized likelihood. In the up-and-down biased-coin design, numerical studies confirm the superior performance of the proposed methods in terms of the mean squared error, bias, and coverage accuracy of confidence intervals. (This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (RS-2023-00242528))
