BLUP(REMQL) estimation of a correlated random effects negative binomial hurdle model

Abstract

Bed days is a potentially useful metric of efficiency in clinical studies involving the hospital admission decision. However, this metric involves excess zeros, possible overdispersion, and possible clustering (in multi-site studies). A random effects negative binomial hurdle model can account for each of these issues. We extend this model to include site-level correlation between the two component parts and implement best linear unbiased prediction-type estimation with restricted maximum quasi-likelihood. This approach offers computational advantages over maximum likelihood in a generalized linear mixed model setting. Simulations show that the proposed approach performs well for fixed effects and variance components under a plausible range of bivariate correlation. The Emergency Department Community Acquired Pneumonia study motivates this work and illustrates the methods.