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Is using multiple imputation better than complete case analysis for estimating a prevalence (risk) difference in

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Complete case (CC) analysis often performs as well as multiple imputation (MI) for risk difference estimates in randomized controlled trials (RCTs) when outcomes are missing at random. Researchers should prioritize data collection and consider CC methods alongside MI.

Keywords:
Complete case analysisMissing at randomMissing binary outcomeMissing completely at randomMissing not at randomMultiple imputationRisk difference

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology

Background:

  • Missing outcomes in randomized controlled trials (RCTs) compromise valid inferences.
  • Complete case (CC) analysis reduces sample size and statistical efficiency.
  • Multiple imputation (MI) methods are preferred for preserving sample size.

Purpose of the Study:

  • Compare the performance of CC and MI methods for estimating risk differences (RD) with missing binary outcomes.
  • Evaluate the statistical efficiency and bias of CC versus MI under various missing data scenarios.

Main Methods:

  • Simulation studies with 5000 datasets and 50 imputations.
  • Assessed RCTs with one primary follow-up endpoint.
  • Varied underlying risk difference (3-25%) and missing outcome rates (5-30%).

Main Results:

  • CC estimates were generally unbiased and achieved similar or better precision than MI for missing at random (MAR) or missing completely at random (MCAR) outcomes.
  • Both CC and MI yielded invalid inferences under missing not at random (MNAR) scenarios.
  • MI showed no statistical advantage over CC for MAR/MCAR outcomes in the assessed scenarios.

Conclusions:

  • CC methods are endorsed for per-protocol risk difference analyses under MAR/MCAR conditions.
  • CC analysis can complement MI analyses, provided the missingness mechanism is valid.
  • Emphasizes the importance of maximizing data collection in clinical trials.