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Review and recommendations for zero-inflated count regression modeling of dental caries indices in epidemiological studies.

ABSTRACT: Over the past 5-10 years, zero-inflated (ZI) count regression models have been increasingly applied to the analysis of dental caries indices (e.g. DMFT, dfms). The main reason for that is linked to the broad decline in children's caries experience, such that dmf and DMF indices more frequently generate low or even zero counts. This article specifically reviews the application of ZI Poisson and ZI negative binomial regression models to dental caries, with emphasis on the description of the models and the interpretation of fitted model results given the study goals. The review finds that interpretations provided in the published caries research are often imprecise or inadvertently misleading, particularly with respect to failing to discriminate between inference for the class of susceptible persons defined by such models and inference for the sampled population in terms of overall exposure effects. Recommendations are provided to enhance the use as well as the interpretation and reporting of results of count regression models when applied to epidemiological studies of dental caries.

SUBMITTER: Preisser JS

PROVIDER: S-EPMC3424072 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Review and recommendations for zero-inflated count regression modeling of dental caries indices in epidemiological studies.

Preisser J S JS Stamm J W JW Long D L DL Kincade M E ME

Caries research 20120615 4

Over the past 5-10 years, zero-inflated (ZI) count regression models have been increasingly applied to the analysis of dental caries indices (e.g. DMFT, dfms). The main reason for that is linked to the broad decline in children's caries experience, such that dmf and DMF indices more frequently generate low or even zero counts. This article specifically reviews the application of ZI Poisson and ZI negative binomial regression models to dental caries, with emphasis on the description of the models ...[more]

PMID: 22710271

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Project description:Count data are increasingly ubiquitous in genetic association studies, where it is possible to observe excess zero counts as compared to what is expected based on standard assumptions. For instance, in rheumatology, data are usually collected in multiple joints within a person or multiple sub-regions of a joint, and it is not uncommon that the phenotypes contain enormous number of zeroes due to the presence of excessive zero counts in majority of patients. Most existing statistical methods assume that the count phenotypes follow one of these four distributions with appropriate dispersion-handling mechanisms: Poisson, Zero-inflated Poisson (ZIP), Negative Binomial, and Zero-inflated Negative Binomial (ZINB). However, little is known about their implications in genetic association studies. Also, there is a relative paucity of literature on their usefulness with respect to model misspecification and variable selection. In this article, we have investigated the performance of several state-of-the-art approaches for handling zero-inflated count data along with a novel penalized regression approach with an adaptive LASSO penalty, by simulating data under a variety of disease models and linkage disequilibrium patterns. By taking into account data-adaptive weights in the estimation procedure, the proposed method provides greater flexibility in multi-SNP modeling of zero-inflated count phenotypes. A fast coordinate descent algorithm nested within an EM (expectation-maximization) algorithm is implemented for estimating the model parameters and conducting variable selection simultaneously. Results show that the proposed method has optimal performance in the presence of multicollinearity, as measured by both prediction accuracy and empirical power, which is especially apparent as the sample size increases. Moreover, the Type I error rates become more or less uncontrollable for the competing methods when a model is misspecified, a phenomenon routinely encountered in practice.

Dataset Information

Review and recommendations for zero-inflated count regression modeling of dental caries indices in epidemiological studies.

Publications

Review and recommendations for zero-inflated count regression modeling of dental caries indices in epidemiological studies.

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