Graphical and Bayesian Analysis of Unbalanced Patient Management Data Graphical and Bayesian Analysis of Unbalanced Patient Management Data
The purpose of this research was to develop innovative graphics to help describe a highly unbalanced dataset and to carry out Bayesian analyses to determine which of five devices best manages patients. An initial Bayesian analysis compared a machine-identical beta-binomial model to a machine-specific beta-binomial model. The response variable was number of in-range visits. A second Bayesian analysis compared a machine-identical lognormal model, a machine-specific lognormal model, and a machine-specific lognormal model with lower therapeutic bound as a predictor. The response variable was INR. Machines were compared using posterior predictive distributions of the absolute distance outside a patientâ€™s therapeutic range.
For the beta-binomial models, the machine-identical model had the lower DIC, meaning that POC device was not a strong predictor of success in keeping a patient in-range. The machine-specific lognormal model with a term for lower therapeutic bound had the lowest DIC of the three lognormal models, implying that the additional information of distance out of range revealed differences among the POC devices. Three of the machines had more favorable out-of-range distributions than the other two. Both Bayesian analyses provided useful information for medical practice in managing INR.
School:Brigham Young University
School Location:USA - Utah
Source Type:Master's Thesis
Keywords:bayesian unbalanced graphics
Date of Publication:10/06/2006