title: The analysis--hierarchical models: past, present and future.
abstract: This paper discusses statistical modelling for data with a hierarchical structure, and distinguishes in this context between three different meanings of the term hierarchical model: to account for clustering, to investigate variability and separate predictive equations at different hierarchical levels (multi-level analysis), and in a Bayesian framework to involve multiple layers of data or prior information. Within each of these areas, the paper reviews both past developments and the present state, and offers indications of future directions. In a worked example, previously reported data on piglet lameness are reanalyzed with multi-level methodology for survival analysis, leading to new insights into the data structure and predictor effects. In our view, hierarchical models of all three types discussed have much to offer for data analysis in veterinary epidemiology and other disciplines.
authors.affiliation: Centre for Veterinary Epidemiological Research, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada. Electronic address: hstryhn@upei.ca.
authors.forename: Henrik
authors.initials: H
authors.lastname: Stryhn
authors.affiliation: Canadian Food Inspection Agency, Epidemiology and Surveillance Section, Atlantic Veterinary College, Department of Health Management, 550 University Avenue, Charlottetown, Prince Edward Island C1A 4P3, Canada.
authors.forename: Jette
authors.initials: J
authors.lastname: Christensen
mesh_terms: D035843:Biomedical Research / Q000379:methods* / Q000592:standards*; D015233:Models, Statistical; D014730:Veterinary Medicine / Q000379:methods*
keywords: Bayesian modelling; Hierarchical data structure; Multi-level; Non-proportional hazards; Random-effects model; Survival analysis