Frequentist and Bayesian population pharmacometrics: similarities, differences, and practical implications
Abstract
Although the frequentist versus Bayesian debate has a long-standing history, and both are well-established approaches for inference, they are often considered separately in pharmacometric literature, with their theoretical and practical differences receiving limited direct comparison. We address this gap by providing a conceptual background and comparative evaluation of frequentist and Bayesian methods for parameter inference in pharmacometrics. First, we highlight the theoretical and algorithmic differences between approaches within a general and pharmacometric context. We then apply both to classical compartmental pharmacokinetic models using simulated and real data; initially through evaluating four inference methods (two frequentist, two Bayesian) across a range of simulated data conditions, followed by investigating each inference approach in the practical setting. The simulation studies facilitate evaluation of the sensitivity of the methods to sample size, number of observations, residual noise and inter-individual variability, comparing the performance with respect to relative error, confidence/credible interval coverage and bias in the estimated variability. The real-world data analysis further highlights how each approach quantifies and presents uncertainty estimates by examining this for both the parameter estimates and system dynamics. By providing a theoretical and practical comparison that highlights the similarities and differences between approaches, this work aims to lower the barrier to understanding and implementing Bayesian and frequentist inference in pharmacometrics.
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BibTeX
@misc{cochrane2026frequentist,
title = {{Frequentist and Bayesian population pharmacometrics: similarities, differences, and practical implications}},
author = {Jodie A. Cochrane and Haini Wen and Liang Yang and Tyler Cassidy and Sara Hamis},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.17340},
eprint = {2610.17340},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.17340},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Frequentist and Bayesian population pharmacometrics: similarities, differences, and practical implications
AU - Jodie A. Cochrane
AU - Haini Wen
AU - Liang Yang
AU - Tyler Cassidy
AU - Sara Hamis
PY - 2026
DA - 2026/10/12
PB - arXiv
DO - 10.48550/arxiv.2610.17340
UR - https://arxiv.org/abs/2610.17340
AB - Although the frequentist versus Bayesian debate has a long-standing history, and both are well-established approaches for inference, they are often considered separately in pharmacometric literature, with their theoretical and practical differences receiving limited direct comparison. We address this gap by providing a conceptual background and comparative evaluation of frequentist and Bayesian methods for parameter inference in pharmacometrics. First, we highlight the theoretical and algorithmic differences between approaches within a general and pharmacometric context. We then apply both to classical compartmental pharmacokinetic models using simulated and real data; initially through evaluating four inference methods (two frequentist, two Bayesian) across a range of simulated data conditions, followed by investigating each inference approach in the practical setting. The simulation studies facilitate evaluation of the sensitivity of the methods to sample size, number of observations, residual noise and inter-individual variability, comparing the performance with respect to relative error, confidence/credible interval coverage and bias in the estimated variability. The real-world data analysis further highlights how each approach quantifies and presents uncertainty estimates by examining this for both the parameter estimates and system dynamics. By providing a theoretical and practical comparison that highlights the similarities and differences between approaches, this work aims to lower the barrier to understanding and implementing Bayesian and frequentist inference in pharmacometrics.
ER -