A Bayesian Modelling Approach with Balancing Informative Prior for Analysing Imbalanced Data

When a dataset is imbalanced, the prediction of the scarcely-sampled subpopulation can be over-influenced by the population contributing to the majority of the data. The aim of this study was to develop a Bayesian modelling approach with balancing informative prior so that the influence of imbalance...

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Bibliographic Details
Published in:PloS one 2016-04, Vol.11 (4), p.e0152700-e0152700
Main Authors: Klein, Kerenaftali, Hennig, Stefanie, Paul, Sanjoy Ketan
Format: Article
Language:English
Subjects:
Adolescent
Adult
Antimicrobial agents
Bayes Theorem
Bayesian analysis
Bias
Biology and Life Sciences
Chemotherapy
Child
Child, Preschool
Clinical trials
Computer simulation
Cystic fibrosis
Data analysis
Data processing
Datasets
Datasets as Topic
Drug therapy
Estimates
Female
Humans
Infant
Male
Markov analysis
Mathematical models
Medical research
Medicine and Health Sciences
Methods
Modelling
Models, Theoretical
Parameter estimation
Pediatrics
People and Places
Physical Sciences
Prediction models
Research and Analysis Methods
Tobramycin
Tobramycin - administration & dosage
Young Adult
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Summary:When a dataset is imbalanced, the prediction of the scarcely-sampled subpopulation can be over-influenced by the population contributing to the majority of the data. The aim of this study was to develop a Bayesian modelling approach with balancing informative prior so that the influence of imbalance to the overall prediction could be minimised. The new approach was developed in order to weigh the data in favour of the smaller subset(s). The method was assessed in terms of bias and precision in predicting model parameter estimates of simulated datasets. Moreover, the method was evaluated in predicting optimal dose levels of tobramycin for various age groups in a motivating example. The bias estimates using the balancing informative prior approach were smaller than those generated using the conventional approach which was without the consideration for the imbalance in the datasets. The precision estimates were also superior. The method was further evaluated in a motivating example of optimal dosage prediction of tobramycin. The resulting predictions also agreed well with what had been reported in the literature. The proposed Bayesian balancing informative prior approach has shown a real potential to adequately weigh the data in favour of smaller subset(s) of data to generate robust prediction models.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0152700