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T E C R H E N P O I C R A L T 0462 The Zero-In aged Negative Binomial Regression Model With Correction for Misclassify cation: An Example in Caries Research Samuel M. Swahili, Emmanuel Lesa re and

As a new measure, the zero-out aged negative binomial (ZINC) t d is similar to the ZINC, but less sensitive to model specification. Furthermore, ZINC is not affected by the assumptions that ZINC makes about willingness in the reference population as the reference is not known. Thus, these models can be used to identify potential candidate risk factors for caries prevention in populations. Keywords: Caries, Zero-in aged negative binomial (ZINC), Age (age) estimation, Zero-out aged negative binomial (ZINC), Age measurement of the outcome variables Introduction In the 1990s, the dentin-risk prediction score3 became a key instrument in caries research and has become one of the most important outcome variables on which models are based in most countries. The dentin-risk prediction score is a statistical measure designed to model the relationship between a population's estimated caries prevalence at age 14 and the percentage of children who experience at least 1 decayed or missing tooth. This prediction score is developed using several statistical methods. In an example from the United Kingdom, the Dentin-Risk Prediction System4,5 was used to predict the number of children experiencing at least 1 decayed or missing tooth by applying a linear regression model to both age and tooth decayed or missing status. This method assumes that missing values in the reference population are the same as all other values. However, missing values are known to vary from individual to individual. This introduces additional error in that the predicted probabilities may vary from the expected estimates. The ZINC is a simple and effective technique for extracting and estimating the missing values. The ZINC is constructed with the same criteria for willingness as ZINC. This is important because the ZINC is sensitive to model specification. In these models, some children are assumed to have no data about their dental status and the reference population does not include missing data. Hence, the ZINC is constructed such that missing data are replaced by the model's estimated values (for example, 2.5 children per cent in one model and 3 in another). In the present paper, we investigate the role of specification in the ZINC's estimation and the use of ZINC to predict the number of children experiencing at least 1 decayed or missing tooth. We will use sample data from several communities in the United Kingdom, which have been described before6, 7, 8.

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