Bayesian Experimental Design for Oral Glucose Tolerance Tests (OGTT)

TL;DR

This paper applies Bayesian experimental design to optimize the timing of measurements in OGTTs, aiming to improve diagnostic effectiveness by selecting better test schedules.

Contribution

It introduces a Bayesian experimental design framework for OGTT, proposing a new estimator for expected utility and a method to quantify uncertainty in design comparisons.

Findings

Proposed a new optimal timing scheme for OGTT measurements.

Compared the new design favorably to traditional testing schedules.

Developed a method to quantify uncertainty in experimental design evaluation.

Abstract

OGTT is a common test, frequently used to diagnose insulin resistance or diabetes, in which a patient's blood sugar is measured at various times over the course of a few hours. Recent developments in the study of OGTT results have framed it as an inverse problem which has been the subject of Bayesian inference. This is a powerful new tool for analyzing the results of an OGTT test,and the question arises as to whether the test itself can be improved. It is of particular interest to discover whether the times at which a patient's glucose is measured can be changed to improve the effectiveness of the test. The purpose of this paper is to explore the possibility of finding a better experimental design, that is, a set of times to perform the test. We review the theory of Bayesian experimental design and propose an estimator for the expected utility of a design. We then study the properties of this estimator and propose a new method for quantifying the uncertainty in comparisons between designs. We implement this method to find a new design and the proposed design is compared favorably to the usual testing scheme.