Jaume March, Javier Trujillano, Mercé Tor, Albert Sorribas
{"title":"Estimating conditional distributions using a method based on S-distributions reference percentile curves for body mass index in Spanish children.","authors":"Jaume March, Javier Trujillano, Mercé Tor, Albert Sorribas","doi":"","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Reference intervals are a fundamental tool for characterizing the health status in a given population and play a central role in defining diagnostic values in clinical applications. Estimation of the conditional distribution of a variable, as the body mass index (BMI), is necessary for providing reference values when there is a trend as a function of the covariate.</p><p><strong>Subjects and method: </strong>We studied 1453 boys and young between 5 and 16 years old measured in a study carried out in the schools of Lleida (Spain). BMI conditional distributions with age have been derived using a new parametric method based on the one proposed by Sorribas et al. [Stat. Med. (2000) 19:697-713]. This method is based on S-distributions as a parametric model for the distribution and uses maximum likelihood estimation of the conditional distribution.</p><p><strong>Results: </strong>The methods commonly used for estimating reference curves are based on a smoothing of sample quantiles using different techniques. However, these methods do not provide information on the conditional distribution of the target variable. Our method provides an estimation of such distribution and the corresponding reference curves for the quantiles as a function of a covariate, in our case age.</p><p><strong>Conclusions: </strong>The suggested methodology provides appropriate reference quantiles for the BMI. Our results allow characterizing the change in distribution within the age range considered. Besides describing a raise in BMI with age, we observe an increase in dispersion around puberty. This must be considered when using BMI as a diagnostic variable.</p>","PeriodicalId":55080,"journal":{"name":"Growth Development and Aging","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2003-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Growth Development and Aging","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Reference intervals are a fundamental tool for characterizing the health status in a given population and play a central role in defining diagnostic values in clinical applications. Estimation of the conditional distribution of a variable, as the body mass index (BMI), is necessary for providing reference values when there is a trend as a function of the covariate.
Subjects and method: We studied 1453 boys and young between 5 and 16 years old measured in a study carried out in the schools of Lleida (Spain). BMI conditional distributions with age have been derived using a new parametric method based on the one proposed by Sorribas et al. [Stat. Med. (2000) 19:697-713]. This method is based on S-distributions as a parametric model for the distribution and uses maximum likelihood estimation of the conditional distribution.
Results: The methods commonly used for estimating reference curves are based on a smoothing of sample quantiles using different techniques. However, these methods do not provide information on the conditional distribution of the target variable. Our method provides an estimation of such distribution and the corresponding reference curves for the quantiles as a function of a covariate, in our case age.
Conclusions: The suggested methodology provides appropriate reference quantiles for the BMI. Our results allow characterizing the change in distribution within the age range considered. Besides describing a raise in BMI with age, we observe an increase in dispersion around puberty. This must be considered when using BMI as a diagnostic variable.