Linking categorical models for prediction of pleasantness score using individual predictions of sweetness and creaminess: An advancement of categorical modeling

Jennifer Leohr,Maria C Kjellsson

doi:10.1007/s10928-021-09771-y

Jennifer Leohr, Maria C Kjellsson

Open Access

https://doi.org/10.1007/s10928-021-09771-y

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Abstract

The aim of this work was to develop and evaluate approaches of linked categorical models using individual predictions of probability. A model was developed using data from a study which assessed the perception of sweetness, creaminess, and pleasantness in dairy solutions containing variable concentrations of sugar and fat. Ordered categorical models were used to predict the individual sweetness and creaminess scores and these individual predictions were used as covariates in the model of pleasantness response. The model using individual predictions was compared to a previously developed model using the amount of fat and sugar as covariates driving pleasantness score. The model using the individual prediction of odds of sweetness and creaminess had a lower variability of pleasantness than the model using the content of sugar and fat in the test solutions, which indicates that the individual odds explain part of the variability in pleasantness. Additionally, simultaneous and sequential modeling approaches were compared for the linked categorical model. Parameter estimation was similar, but precision was better with sequential modeling approaches compared to the simultaneous modeling approach. The previous model characterizing the pleasantness response was improved by using individual predictions of sweetness and creaminess rather than the amount of fat and sugar in the solution. The application of this approach provides an advancement within categorical modeling showing how categorical models can be linked to enable the utilization of individual prediction. This approach is aligned with biology of taste sensory which is reflective of the individual perception of sweetness and creaminess, rather than the amount of fat and sugar in the solution.

Highlights

Overweight and obesity is a major, global health challenge [1]
Simultaneous and sequential modeling approaches were compared for the linked categorical model
Similar to PKPD modeling, we found that using the individual prediction of sweetness and creaminess reduced the estimate of variability of pleasantness compared to the model using the amount of sugar and fat to describe the pleasantness score

Summary

Introduction

Overweight and obesity is a major, global health challenge [1]. Obesity is caused by a chronic positive energy balance; whereby adipose tissue is increased as a consequence of a positive balance between energy intake and utilization. Chronic over-eating, leading to obesity, is commonly categorized as an addictive behavior, similar to drug abuse, where craving and reward play an important role [2]. Lewis Sheiner introduced, in 1994, the concept of nonlinear mixed-effects categorical data analysis in the field of pharmacokinetic-pharmacodynamic (PKPD) modeling [9]. The proportional odds model assumes that the covariate effect is the same across all categories, implying that a change in a covariate has the same effect on the log odds of all outcome categories [10]. The differential odds model relaxes this assumption and allows categories to be affected unequally by changes in a covariate [14]. The differential odds model has been shown to improve analysis of sedation data [14,15,16] and ocular itching scores [17]

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