Espasandín Domínguez, JeniferBenítez Estévez, Alfonso JavierCadarso Suárez, Carmen MaríaKneib, ThomasBarreiro Martínez, TegraCasas Méndez, BalbinaGude, Francisco2024-01-242024-01-242018Jenifer Espasandín-Domínguez, Alfonso Javier Benítez-Estévez, Carmen Cadarso-Suárez, Thomas Kneib, Tegra Barreiro-Martínez, Balbina Casas-Méndez, Francisco Gude, Geographical differences in blood potassium detected using a structured additive distributional regression model, Spatial Statistics, Volume 24, 2018, Pages 1-132211-6753http://hdl.handle.net/10347/31971Recently, physicians in an area of northwestern Spain became concerned about the large number of patients whose serum potassium concentrations were above the normal range, as well as differences in the values recorded from one area to another. With the aim of identifying geographical differences in both mean and variability of potassium levels, analyses were performed using modern flexible regression techniques based on a structured additive distributional regression model. In this type of model, every parameter of a response distribution – rather than just the mean – is related to a structured additive predictor. After adjusting for variables such as age, sex, clot-contact time and spatial effects, differences in potassium concentrations were confirmed. The type of distributional regression model used permitted the mean and variance of the potassium concentrations to be modelled using additive predictors that allow for different types of covariate effects. A variety of complex distributions were contemplated.engAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/PotassiumDistributional regressionSpatial analysisP-splinesMatemáticas estadísticasjournal article10.1016/j.spasta.2018.03.001open access