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Updated: Jun 21, 2025

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Reparametrized Firth's Logistic Regressions for Dose-Finding Study With the Biased-Coin Design

Hyungwoo Kim1, Seungpil Jung2, Yudi Pawitan3

  • 1Department of Statistics and Data Science, Pukyong National University, Busan, Republic of Korea.

Pharmaceutical Statistics
|July 16, 2024
PubMed

Insights

This study introduces new statistical methods, reparametrized Firth's logistic regression (rFLR) and ridge-penalized reparametrized Firth's logistic regression (RrFLR), to accurately determine drug doses in clinical trials.

Area of Science:

  • Biostatistics
  • Clinical Pharmacology
  • Statistical Modeling

Background:

  • Accurate dose-finding is critical in drug development.
  • Identifying minimum effective dose (MED) and maximum tolerated dose (MTD) are key challenges.

Purpose of the Study:

  • To propose novel statistical methods for precise estimation of MED and MTD.
  • To reduce small-sample bias in dose-finding studies.

Main Methods:

  • Developed reparametrized Firth's logistic regression (rFLR).
  • Introduced ridge-penalized reparametrized Firth's logistic regression (RrFLR).
  • Constructed confidence intervals using profile penalized likelihood.

Main Results:

  • Proposed methods demonstrated superior performance in simulations.
  • rFLR and RrFLR showed reduced bias and improved mean squared error.
  • Confidence intervals exhibited enhanced coverage accuracy.

Conclusions:

  • rFLR and RrFLR offer improved accuracy for dose-finding studies.
  • These methods enhance the reliability of estimating critical dose levels.
  • The novel confidence interval construction improves statistical rigor.
Keywords:
biased‐coin designmaximum tolerated doseminimum effective doseprofile penalized likelihoodreparametrized Firth's logistic regression

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