title: A Bayesian Modelling Approach with Balancing Informative Prior for Analysing Imbalanced Data.
abstract: 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.
authors.affiliation: Clinical Trials and Biostatistics Unit, QIMR Berghofer Medical Research Institute, 300 Herston Road, Brisbane, Australia.
authors.forename: Kerenaftali
authors.initials: K
authors.lastname: Klein
authors.affiliation: School of Pharmacy, University of Queensland, 20 Cornwall Street, Brisbane, Australia.
authors.forename: Stefanie
authors.initials: S
authors.lastname: Hennig
authors.affiliation: Clinical Trials and Biostatistics Unit, QIMR Berghofer Medical Research Institute, 300 Herston Road, Brisbane, Australia.
authors.forename: Sanjoy Ketan
authors.initials: SK
authors.lastname: Paul
mesh_terms: D000293:Adolescent; D000328:Adult; D001499:Bayes Theorem; D002648:Child; D002675:Child, Preschool; D066264:Datasets as Topic; D005260:Female; D006801:Humans; D007223:Infant; D008297:Male; D008962:Models, Theoretical; D014031:Tobramycin / Q000008:administration & dosage; D055815:Young Adult
chemical_list: D014031:Tobramycin