Population Calibration using Likelihood-Free Bayesian Inference

TL;DR

This paper introduces a likelihood-free Bayesian method for population calibration that provides uncertainty quantification and is applicable to both deterministic and stochastic models across various medical research fields.

Contribution

The paper presents a novel likelihood-free Bayesian approach for population calibration that offers uncertainty estimates and broad applicability to different model types and data conditions.

Findings

Method successfully applied to real data example

Provides uncertainty quantification in population calibration

Applicable to deterministic and stochastic models

Abstract

In this paper we develop a likelihood-free approach for population calibration, which involves finding distributions of model parameters when fed through the model produces a set of outputs that matches available population data. Unlike most other approaches to population calibration, our method produces uncertainty quantification on the estimated distribution. Furthermore, the method can be applied to any population calibration problem, regardless of whether the model of interest is deterministic or stochastic, or whether the population data is observed with or without measurement error. We demonstrate the method on several examples, including one with real data. We also discuss the computational limitations of the approach. Immediate applications for the methodology developed here exist in many areas of medical research including cancer, COVID-19, drug development and cardiology.