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

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  • 1School of Nursing, University of Alabama at Birmingham, Birmingham, AL.

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|February 23, 2019
PubMed
Summary
This summary is machine-generated.

Missing data in nutrition trials can bias results. Complete case analysis and last-observation-carried-forward methods are often misused due to unclear assumptions. Multiple imputation and full information maximum likelihood are recommended for accurate causal inference.

Keywords:
full information maximum likelihoodmissing datamissing data mechanismsmultiple imputationrandomized controlled trials

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Area of Science:

  • Statistics
  • Clinical Trials
  • Nutrition Science

Background:

  • Missing data is a common challenge in randomized controlled trials, potentially compromising causal inference.
  • Inappropriate handling of missing data, such as complete case analysis and last-observation-carried-forward, remains prevalent in nutrition research.
  • Investigator confusion regarding the underlying assumptions of different missing data methods contributes to their misuse.

Purpose of the Study:

  • To clarify missing data mechanisms and the flawed assumptions of complete case analysis and last-observation-carried-forward.
  • To recommend appropriate statistical methods for handling missing data in nutrition trials.
  • To improve the validity of causal inference in nutrition research.

Main Methods:

  • Introduction to missing data mechanisms.
  • Critique of assumptions for complete case analysis and last-observation-carried-forward.
  • Recommendation of multiple imputation and full information maximum likelihood.

Main Results:

  • Complete case analysis and last-observation-carried-forward rely on strong, often unreasonable, assumptions.
  • Multiple imputation and full information maximum likelihood offer more robust approaches to handling missing data.
  • Understanding missing data assumptions is crucial for appropriate method selection.

Conclusions:

  • Misuse of complete case analysis and last-observation-carried-forward can lead to biased causal inference in nutrition trials.
  • Multiple imputation and full information maximum likelihood are statistically sound alternatives.
  • This guidance aims to reduce confusion and improve the quality of missing data analysis in nutrition research.