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

Supported by the National Center for Advancing Translational Sciences of the NIH (award no. UL1TR001417, to PL; principal investigator: R Kimberly; award no. R01HL127491, to EAS; principal investigator: J Siddique). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
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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.

Key Words

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

Abbreviations

CC
complete case
FCS
fully conditional specification
FIML
full information maximum likelihood
ITT
intent-to-treat
LOCF
last-observation-carried-forward
MAR
missing at random
MCAR
missing completely at random
MI
multiple imputation
ML
maximum likelihood
MNAR
missing not at random
MVN
multivariate normal
RCT
randomized controlled trial

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