A multi‐level two‐part random effects model, with application to an alcohol‐dependence study
- 25 January 2008
- journal article
- research article
- Published by Wiley in Statistics in Medicine
- Vol. 27 (18) , 3528-3539
- https://doi.org/10.1002/sim.3205
Abstract
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. Copyright © 2008 John Wiley & Sons, Ltd.Keywords
This publication has 30 references indexed in Scilit:
- A Hierarchical Multivariate Two-Part Model for Profiling Providers' Effects on Health Care ChargesJournal of the American Statistical Association, 2006
- Bayesian Inference for a Two-Part Hierarchical ModelJournal of the American Statistical Association, 2006
- Modelling Longitudinal Semicontinuous Emesis Volume Data with Serial Correlation in an Acupuncture Clinical TrialJournal of the Royal Statistical Society Series C: Applied Statistics, 2005
- On the Effect of the Number of Quadrature Points in a Logistic Random Effects Model: An ExampleJournal of the Royal Statistical Society Series C: Applied Statistics, 2001
- A Two-Part Random-Effects Model for Semicontinuous Longitudinal DataJournal of the American Statistical Association, 2001
- Maximum Likelihood Algorithms for Generalized Linear Mixed ModelsJournal of the American Statistical Association, 1997
- Approximate Inference in Generalized Linear Mixed ModelsJournal of the American Statistical Association, 1993
- Accurate Approximations for Posterior Moments and Marginal DensitiesJournal of the American Statistical Association, 1986
- Smearing Estimate: A Nonparametric Retransformation MethodJournal of the American Statistical Association, 1983