Zero-Inflated Poisson Regression to Analyze Lengths of Hospital Stays Adjusting for Intra-Center Correlation

Author: SONG, JAMES

Source: Communications in Statistics: Simulation and Computation, Volume 34, Number 1, Number 1/2005 , pp. 235-241(7)

Publisher: Taylor and Francis Ltd

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Abstract:

In the anti-infective clinical trials, in addition to the efficacy and safety outcomes, health economic outcomes such as length of hospital stays (LOS) and number of hours missed from work are usually compared between treatment groups. Since excess zeros are often exhibited in the LOS data, a zero-inflated Poisson (ZIP) model is adopted to model such data. In a multi-center trial, as patients from the same center are often treated by the same doctor and have similar socioeconomic backgrounds, correlation among subjects' LOS within centers may exist, and is commonly termed the intracluster correlation (ICC). Ignoring such intracluster correlation in statistical analysis leads to erroneous parameter estimates and usually inflated Type I error. To adjust for the intracluster variations, the generalized estimating equations (GEE) method is introduced to the ZIP model. The GEEs have consistent solutions even when the dependence is misspecified. The proposed model in this study provides an extension to the regular ZIP model when analyzing correlated LOS data.

Keywords: Correlated length of hospital stays; Generalized estimating equations; Multi-center clinical trial; Zero-inflated model

Document Type: Research article

DOI: http://dx.doi.org/10.1081/SAC-200047118

Affiliations: 1: Global Biometry, Bayer Pharmaceutical Corporation, West Haven, CT, USA

Publication date: 2005-01-01