Best (but oft-forgotten) practices: Missing data methods in randomized controlled nutrition trials

Research output: Contribution to journalReview articlepeer-review

Abstract

Missing data ubiquitously occur in randomized controlled trials and may compromise the causal inference if inappropriately handled. Some problematic missing data methods such as complete case (CC) analysis and last-observation-carried-forward (LOCF) are unfortunately still common in nutrition trials. This situation is partially caused by investigator confusion on missing data assumptions for different methods. In this statistical guidance, we provide a brief introduction of missing data mechanisms and the unreasonable assumptions that underlie CC and LOCF and recommend 2 appropriate missing data methods: multiple imputation and full information maximum likelihood.

Original languageEnglish (US)
Pages (from-to)504-508
Number of pages5
JournalAmerican Journal of Clinical Nutrition
Volume109
Issue number3
DOIs
StatePublished - Mar 1 2019

Keywords

  • full information maximum likelihood
  • missing data
  • missing data mechanisms
  • multiple imputation
  • randomized controlled trials

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Nutrition and Dietetics

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