_:n299b451a1b6c48cf87e27db12d75df1fxb52
<http://schema.org/description>
"The analysis of population-wide datasets can provide insight on the health\nstatus of large populations so that public health officials can make\ndata-driven decisions. The analysis of such datasets often requires highly\nparameterized models with different types of fixed and randoms effects to\naccount for risk factors, spatial and temporal variations, multilevel effects\nand other sources on uncertainty. To illustrate the potential of Bayesian\nhierarchical models, a dataset of about 500 000 inhabitants released by the\nPolish National Health Fund containing information about ischemic stroke\nincidence for a 2-year period is analyzed using different types of models.\nSpatial logistic regression and survival models are considered for analyzing\nthe individual probabilities of stroke and the times to the occurrence of an\nischemic stroke event. Demographic and socioeconomic variables as well as drug\nprescription information are available at an individual level. Spatial\nvariation is considered by means of region-level random effects."
<https://api.deepai.org/profile/virgilio-gomez-rubio>