Imputing missing data in public health: general concepts and application to dichotomous variables

Gilma Hernández, David Moriña, Albert Navarro

Research output: Contribution to journalArticleResearchpeer-review

Abstract

© 2017 SESPAS 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.
Original languageEnglish
Pages (from-to)342-345
JournalGaceta Sanitaria
Volume31
Issue number4
DOIs
Publication statusPublished - 1 Jul 2017

Keywords

  • Epidemiology
  • Imputation
  • Missing data
  • Public health

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