Multi-level two part random effects model, with application to an alcohol dependence study

Division of Biostatistics and Epidemiology, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908-0717, USA.
Statistics in Medicine (Impact Factor: 1.83). 08/2008; 27(18):3528-39. DOI: 10.1002/sim.3205
Source: PubMed


Two-part random effects models (J. Am. Statist. Assoc. 2001; 96:730-745; Statist. Methods Med. Res. 2002; 11:341-355) have been applied to longitudinal studies for semi-continuous outcomes, characterized by a large portion of zero values and continuous non-zero (positive) values. Examples include repeated measures of daily drinking records, monthly medical costs, and annual claims of car insurance. However, the question of how to apply such models to multi-level data settings remains. In this paper, we propose a novel multi-level two-part random effects model. Distinct random effects are used to characterize heterogeneity at different levels. Maximum likelihood estimation and inference are carried out through Gaussian quadrature technique, which can be implemented conveniently in freely available software-aML. The model is applied to the analysis of repeated measures of the daily drinking record in a randomized controlled trial of topiramate for alcohol-dependence treatment.

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    • "We used a multivariable two-part random effects model (Tooze et al., 2002; Liu et al., 2008) to determine factors affecting the mean abundance of juvenile sea lice at farm i for week t (Y i,t ). This model permitted us to account for a substantial proportion of zeros (i.e., Y i,t = 0) in the dataset (12.4%). "
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    ABSTRACT: The decline of fisheries over recent decades and a growing human population has coincided with an increase in aquaculture production. As farmed fish densities increase, so have their rates of infectious diseases, as predicted by the theory of density-dependent disease transmission. One of the pathogen that has increased with the growth of salmon farming is sea lice. Effective management of this pathogen requires an understanding of the spatial scale of transmission. We used a two-part multi-scale model to account for the zero-inflated data observed in weekly sea lice abundance levels on rainbow trout and Atlantic salmon farms in Chile, and to assess internal (farm) and external (regional) sources of sea lice infection. We observed that the level of juvenile sea lice was higher on farms that were closer to processing plants with fish holding facilities. Further, evidence for sea lice exposure from the surrounding area was supported by a strong positive correlation between the level of juvenile sea lice on a farm and the number of gravid females on neighboring farms within 30km two weeks prior. The relationship between external sources of sea lice from neighboring farms and juvenile sea lice on a farm was one of the strongest detected in our multivariable model. Our findings suggest that the management of sea lice should be coordinated between farms and should include all farms and processing plants with holding facilities within a relatively large geographic area. Understanding the contribution of pathogens on a farm from different sources is an important step in developing effective control strategies.
    Full-text · Article · Apr 2013 · Preventive Veterinary Medicine
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    • "Two-part models can account for the concentration of excessive zero-valued observations. This approach uses logistic regression to predict the probability of occurrence of a non-zero value in the first part, and linear regression to predict the amount of the non-zero values in the second part [31]. "
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    Full-text · Article · Nov 2012 · BMC Veterinary Research
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