Regression Models for Count Data: Illustrations using Longitudinal Predictors of Childhood Injury*


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Abstract

ObjectiveTo offer a practical demonstration of regression models recommended for count outcomes using longitudinal predictors of children's medically attended injuries.MethodParticipants included 708 children from the NICHD child care study. Measures of temperament, attention, parent–child relationship, and safety of physical environment were used to predict medically attended injuries.ResultsStatistical comparisons among five estimation methods revealed that a zero-inflated Poisson (ZIP) model provided the best fit with observed data. ZIP models simultaneously model dichotomous and continuous outcomes of count variables, and different constellations of predictors emerged for each aspect of the estimated model.ConclusionsThis study offers a practical demonstration of techniques designed to handle dependent count variables. The conceptual and statistical advantages of these methods are emphasized, and Stata script is provided to facilitate adoption of these techniques.

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