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Pharmaceutical Statistics
Article . 2024 . Peer-reviewed
License: CC BY NC ND
Data sources: Crossref
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A Personalized Dose‐Finding Algorithm Based on Adaptive Gaussian Process Regression

Authors: Yeonhee Park; Won Chang;

A Personalized Dose‐Finding Algorithm Based on Adaptive Gaussian Process Regression

Abstract

ABSTRACTDose‐finding studies play a crucial role in drug development by identifying the optimal dose(s) for later studies while considering tolerability. This not only saves time and effort in proceeding with Phase III trials but also improves efficacy. In an era of precision medicine, it is not ideal to assume patient homogeneity in dose‐finding studies as patients may respond differently to the drug. To address this, we propose a personalized dose‐finding algorithm that assigns patients to individualized optimal biological doses. Our design follows a two‐stage approach. Initially, patients are enrolled under broad eligibility criteria. Based on the Stage 1 data, we fit a regression model of toxicity and efficacy outcomes on dose and biomarkers to characterize treatment‐sensitive patients. In the second stage, we restrict the trial population to sensitive patients, apply a personalized dose allocation algorithm, and choose the recommended dose at the end of the trial. Simulation study shows that the proposed design reliably enriches the trial population, minimizes the number of failures, and yields superior operating characteristics compared to several existing dose‐finding designs in terms of both the percentage of correct selection and the number of patients treated at target dose(s).

Keywords

Dose-Response Relationship, Drug, Normal Distribution, Drug Development, Research Design, Main Paper, Humans, Regression Analysis, Computer Simulation, Precision Medicine, Algorithms

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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