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

Academic Article

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.
  • Authors

    Digital Object Identifier (doi)

    Author List

  • Li P; Stuart EA
  • Start Page

  • 504
  • End Page

  • 508
  • Volume

  • 109
  • Issue

  • 3