Missing data treatments in intervention studies: What was, what is, and what should be

Charlie Rioux, Todd D. Little

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Missing data are ubiquitous in studies examining preventive interventions. This missing data need to be handled appropriately for data analyses to yield unbiased results. After a brief discussion of missing data mechanisms, inappropriate missing data treatments and appropriate missing data treatments, we review the current state of missing data treatments in intervention studies as well as how they have evolved over the years. Although missing data treatments have improved over the years, antiquated missing data treatments associated with biased results are still prevalent. Furthermore, many studies do not appropriately report their rates of missing data and missing data treatments. Using appropriate missing data treatments is elemental to accurately identify effective preventive interventions and properly inform practice and policy.

Original languageEnglish
Pages (from-to)51-58
Number of pages8
JournalInternational Journal of Behavioral Development
Volume45
Issue number1
DOIs
StatePublished - Jan 2021

Keywords

  • Missing data
  • attrition
  • clinical trials
  • dropout
  • full information maximum likelihood
  • interventions
  • multiple imputation

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