Comparar métodos

	Delineamento Bayesiano Caso-Crossover	Modelo Hierárquico Bayesiano
Área	Epidemiologia	Bayesiano
Família	Process / pipeline	Bayesian methods
Ano de origem	1991 (case-crossover); Bayesian extension ~2000s	2006
Autor original	Malcolm Maclure (case-crossover); Bayesian extension developed by Lumley, Sheppard, and colleagues	Gelman & Hill (2006); Bayesian multilevel tradition
Tipo	Self-matched observational study design with Bayesian inference	hierarchical probabilistic model
Fonte seminal	Maclure, M. (1991). The case-crossover design: a method for studying transient effects on the risk of acute events. American Journal of Epidemiology, 133(2), 144–153.	Gelman, A. & Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.
Outros nomes	Bayesian case-crossover, BCCO, Bayesian self-matched design, Bayesian within-person crossover	multilevel Bayes, Bayesian multilevel model, Bayesian HLM, partial pooling model
Relacionados	2	4
Resumo	The Bayesian case-crossover design is a self-matched epidemiological method that estimates the transient effect of a time-varying exposure on the risk of an acute event. Each case serves as their own control, eliminating confounding by time-stable individual characteristics. Bayesian inference replaces or supplements the classical conditional logistic regression, enabling the incorporation of prior knowledge, more stable estimation in sparse data, and full uncertainty quantification via posterior distributions.	Bayesian hierarchical modelling, popularised by Gelman and Hill (2006), is a Bayesian approach to nested data structures — such as students within schools within districts — that estimates separate parameters at each level while allowing those levels to share statistical strength through a mechanism called partial pooling. Where a classical hierarchical linear model treats group means as fixed unknown quantities, the Bayesian version places hyperprior distributions on those group means so that information flows freely across levels, producing more reliable group-level estimates whenever any individual group has few observations.