The pitfalls of using Gaussian Process Regression for normative modeling

Normative modeling, a group of methods used to quantify an individual's deviation from some expected trajectory relative to observed variability around that trajectory, has been used to characterize subject heterogeneity. Gaussian Processes Regression includes an estimate of variable uncertaint...

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Bibliographic Details
Published in:PloS one 2021-09, Vol.16 (9), p.e0252108-e0252108
Main Authors: Xu, Bohan, Kuplicki, Rayus, Sen, Sandip, Paulus, Martin P
Format: Article
Language:English
Subjects:
Alzheimer's disease
Attention deficit hyperactivity disorder
Biology and Life Sciences
Brain research
Case-Control Studies
Computer science
Deviation
Epistemology
Evaluation
Gaussian process
Gaussian processes
Growth models
Heterogeneity
Humans
Medicine and Health Sciences
Modelling
Models, Theoretical
Noise
Normal Distribution
Physical Sciences
Regression
Regression Analysis
Research and Analysis Methods
Social Sciences
Uncertainty
Virtue epistemology
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Summary:Normative modeling, a group of methods used to quantify an individual's deviation from some expected trajectory relative to observed variability around that trajectory, has been used to characterize subject heterogeneity. Gaussian Processes Regression includes an estimate of variable uncertainty across the input domain, which at face value makes it an attractive method to normalize the cohort heterogeneity where the deviation between predicted value and true observation is divided by the derived uncertainty directly from Gaussian Processes Regression. However, we show that the uncertainty directly from Gaussian Processes Regression is irrelevant to the cohort heterogeneity in general.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0252108