Poisson-Birnbaum-Saunders Regression Model for Clustered Count Data

TL;DR

This paper introduces a new clustered count data regression model that accounts for overdispersion and correlation within groups using a Poisson-Birnbaum-Saunders framework, with estimation via maximum likelihood and EM algorithm.

Contribution

The paper proposes the Clustered Poisson Birnbaum-Saunders (CPBS) regression model, a novel approach for correlated overdispersed count data, with explicit moment structure and estimation methods.

Findings

Model effectively captures within-cluster correlation.

Simulation studies show good finite-sample performance.

Application demonstrates practical usefulness in healthcare data.

Abstract

The premise of independence among subjects in the same cluster/group often fails in practice, and models that rely on such untenable assumption can produce misleading results. To overcome this severe deficiency, we introduce a new regression model to handle overdispersed and correlated clustered counts. To account for correlation within clusters, we propose a Poisson regression model where the observations within the same cluster are driven by the same latent random effect that follows the Birnbaum-Saunders distribution with a parameter that controls the strength of dependence among the individuals. This novel multivariate count model is called Clustered Poisson Birnbaum-Saunders (CPBS) regression. As illustrated in this paper, the CPBS model is analytically tractable, and its moment structure can be explicitly obtained. Estimation of parameters is performed through the maximum likelihood method, and an Expectation-Maximization (EM) algorithm is also developed. Simulation results to evaluate the finite-sample performance of our proposed estimators are presented. We also discuss diagnostic tools for checking model adequacy. An empirical application concerning the number of inpatient admissions by individuals to hospital emergency rooms, from the Medical Expenditure Panel Survey (MEPS) conducted by the United States Agency for Health Research and Quality, illustrates the usefulness of our proposed methodology.