Identifying biologically implausible values in big longitudinal data: an example applied to child growth data from the Brazilian food and nutrition surveillance system

Identifying biologically implausible values in big longitudinal data: an example applied to child growth data from the Brazilian food and nutrition surveillance system

Abstract Background Several strategies for identifying biologically implausible values in longitudinal anthropometric data have recently been proposed, but the suitability of these strategies for large population datasets needs to be better understood. This study evaluated the impact of removing pop...

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Bibliographic Details
Main Authors: Juliana Freitas de Mello e Silva, Natanael de Jesus Silva, Thaís Rangel Bousquet Carrilho, Elizabete de Jesus Pinto, Aline Santos Rocha, Jéssica Pedroso, Sara Araújo Silva, Ana Maria Spaniol, Rafaella da Costa Santin de Andrade, Gisele Ane Bortolini, Enny Paixão, Gilberto Kac, Rita de Cássia Ribeiro-Silva, Maurício L. Barreto
Format: Article
Language:English
Published: BMC 2024-02-01
Series:BMC Medical Research Methodology
Subjects:
Biologically implausible values
Longitudinal growth data
Outliers
Outlier identification
Anthropometry
Child’s growth
Online Access:https://doi.org/10.1186/s12874-024-02161-1

Internet

https://doi.org/10.1186/s12874-024-02161-1

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