Spat Spatiotemporal Epidemiol - Spatial correlation in Bayesian logistic regression with misclassification.

Tópicos

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Resumo

Standard logistic regression assumes that the outcome is measured perfectly. In practice, this is often not the case, which could lead to biased estimates if not accounted for. This study presents Bayesian logistic regression with adjustment for misclassification of the outcome applied to data with spatial correlation. The models assessed include a fixed effects model, an independent random effects model, and models with spatially correlated random effects modelled using conditional autoregressive prior distributions (ICAR and ICAR()). Performance of these models was evaluated in a simulation study. Parameters were estimated by Markov Chain Monte Carlo methods, using slice sampling to improve convergence. The results demonstrated that adjustment for misclassification must be included to produce unbiased regression estimates. With strong correlation the ICAR model performed best. With weak or moderate correlation the ICAR() performed best. With unknown spatial correlation the recommended model would be the ICAR(), assuming convergence can be obtained.

Resumo Limpo

standard logist regress assum outcom measur perfect practic often case lead bias estim account studi present bayesian logist regress adjust misclassif outcom appli data spatial correl model assess includ fix effect model independ random effect model model spatial correl random effect model use condit autoregress prior distribut icar icar perform model evalu simul studi paramet estim markov chain mont carlo method use slice sampl improv converg result demonstr adjust misclassif must includ produc unbias regress estim strong correl icar model perform best weak moder correl icar perform best unknown spatial correl recommend model icar assum converg can obtain

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