An application of bootstrap resampling method to obtain confidence interval for percentile fatness cutoff points in childhood and adolescence overweight diagnoses
OBJECTIVE: To present a resampling approach to obtain confidence intervals (CIs) and the empirical distributions for the studentized regression residuals percentiles when used as cutoff points for overweight and obesity diagnosis in children and adolescents.METHOD: A tutorial for the nonparametric b...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2005 |
| País: | Brasil |
| Institución: | Universidade Federal de São Paulo (UNIFESP) |
| Repositorio: | Repositório Institucional da UNIFESP |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.unifesp.br:11600/28161 |
| Acceso en línea: | http://dx.doi.org/10.1038/sj.ijo.0802866 http://repositorio.unifesp.br/handle/11600/28161 |
| Access Level: | acceso abierto |
| Palabra clave: | body composition bootstrap empirical distribution confidence intervals statistical methods |
| Sumario: | OBJECTIVE: To present a resampling approach to obtain confidence intervals (CIs) and the empirical distributions for the studentized regression residuals percentiles when used as cutoff points for overweight and obesity diagnosis in children and adolescents.METHOD: A tutorial for the nonparametric bootstrap with bias accelerating correction is presented. A classical method, the Binomial interpretation, is used as comparing criterion.SUBJECTS: A case study comprising 418 randomly selected subjects from a private secondary school (age: 10-17 y, boys: 52%).MEASUREMENTS: Body fat percentage (by), age (y) and Tanner criteria.RESULTS: the empirical distributions presented skewness suggesting that the CIs should not be symmetric. CIs obtained by the proposed approach were more realistic than the classical ones.CONCLUSIONS: We propose a simple and efficient way to obtain the interval estimates and the distribution properties of cutoff points for overweight and obese classification using a sample-based method that allows the comparison of cutoffs among many subpopulations. |
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