[Imputing missing data in public health: general concepts and application to dichotomous variables].
The presence of missing data in collected variables is common in health surveys, but the subsequent imputation thereof at the time of analysis is not. Working with imputed data may have certain benefits regarding the precision of the estimators and the unbiased identification of associations between variables. The imputation process is probably still little understood by many non-statisticians, who view this process as highly complex and with an uncertain goal. To clarify these questions, this note aims to provide a straightforward, non-exhaustive overview of the imputation process to enable public health researchers ascertain its strengths. All this in the context of dichotomous variables which are commonplace in public health. To illustrate these concepts, an example in which missing data is handled by means of simple and multiple imputation is introduced.
Instituto de Investigaciones Médicas, Universidad de Antioquia, Medellín, Colombia; Programa de Doctorado en Metodología de la Investigación Biomédica y Salud Pública, Departament de Pediatria, d'Obstetricia i Ginecologia i de Medicina Preventiva, Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès, Barcelona), España.
Gilma
G
Hernández
Unitat d'Infeccions i Càncer (UNIC), Programa d'Investigació en Epidemiologia del Càncer (PREC), Institut Català d'Oncologia (ICO)-IDIBELL, L'Hospitalet de Llobregat (Barcelona), España; GRAAL-Unitat de Bioestadística, Facultat de Medicina, Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès, Barcelona), España.
David
D
Moriña
GRAAL-Unitat de Bioestadística, Facultat de Medicina, Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès, Barcelona), España. Electronic address: albert.navarro@uab.cat.
Albert
A
Navarro
D000068598:Data Accuracy; D006801:Humans; D011634:Public Health / Q000706:statistics & numerical data*
Epidemiology; Epidemiología; Imputación; Imputation; Missing data; Public health; Salud pública; Valores ausentes