Anomalous values and missing data in clinical and experimental studies

During analysis of scientific research data, it is customary to encounter anomalous values or missing data. Anomalous values can be the result of errors of recording, typing, measurement by instruments, or may be true outliers. This review discusses concepts, examples and methods for identifying and...

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Bibliographic Details
Published inJornal vascular brasileiro Vol. 18; p. e20190004
Main Author Miot, Hélio Amante
Format Journal Article
LanguageEnglish
Published Brazil Sociedade Brasileira de Angiologia e de Cirurgia Vascular (SBACV) 21.05.2019
Subjects
Review
outlier
multiple imputation
database
data analysis
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Summary:During analysis of scientific research data, it is customary to encounter anomalous values or missing data. Anomalous values can be the result of errors of recording, typing, measurement by instruments, or may be true outliers. This review discusses concepts, examples and methods for identifying and dealing with such contingencies. In the case of missing data, techniques for imputation of the values are discussed in, order to avoid exclusion of the research subject, if it is not possible to retrieve information from registration forms or to re-address the participant.
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Conflicts of interest: No conflicts of interest declared concerning the publication of this article.
Author information HAM - Tenured professor, Departamento de Dermatologia e Radioterapia, Faculdade de Medicina de Botucatu, Universidade Estadual Paulista (UNESP), Botucatu, SP, Brasil.
ISSN:1677-7301
1677-5449
1677-7301
DOI:10.1590/1677-5449.190004