Bayesian Fractional Polynomials for Optimal Dosage Estimation with Fish Nutrition Applications

2026-05-07

AI Summary

This study addresses the challenge of estimating optimal doses in nonlinear dose–response relationships, particularly in contexts such as aquaculture, by introducing a Bayesian fractional polynomial framework for the first time. The proposed approach explicitly quantifies and integrates model uncertainty through Bayesian model averaging, thereby enhancing the robustness and accuracy of optimal dose estimation. In simulation studies, the method significantly outperforms existing benchmark approaches. Furthermore, when applied to real-world data on fish nutritional requirements, it successfully identifies the optimal nutrient dose level, demonstrating both practical applicability and statistical reliability.

Abstract

The problem of optimal dosage estimation arises in diverse scientific domains, from pharmacology and toxicology to aquaculture and environmental studies. Statistical modeling of nonlinear dose-response relationships is essential to quantify biological effects and determine response-optimal levels. This paper introduces a flexible Bayesian fractional polynomial (BFP) framework for modeling such relationships, allowing for model uncertainty quantification and robust prediction through Bayesian model averaging. Extensive simulation results demonstrate that the proposed BFP approach yields accurate estimation of optimal dose levels, outperforming benchmarks significantly. The approach is demonstrated on real data from fish nutrient requirement experiments.

Problem

• optimal dosage estimation
• dose-response relationships
• fish nutrition
• Bayesian modeling
• nonlinear modeling

Innovation

• Bayesian fractional polynomials
• optimal dosage estimation
• dose-response modeling
• Bayesian model averaging
• nonlinear regression