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Handling missing data in partially clustered randomized controlled trials.

Manshu Yang1, Darrell J Gaskin2

  • 1Department of Psychology, University of Rhode Island.

Psychological Methods
|November 6, 2023
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Summary
This summary is machine-generated.

Handling missing data in partially clustered trials is crucial for accurate psychological research. Arm-specific multiple imputation using joint modeling (MI-JM-AS) is best for fixed effects, while MI-SMC-AS is preferred for random effects.

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Area of Science:

  • Psychological Research
  • Biostatistics
  • Clinical Trials

Background:

  • Partially clustered designs are common in psychological randomized controlled trials.
  • Missing data present significant challenges in these complex trial designs.
  • Handling missing data is essential for valid research conclusions.

Purpose of the Study:

  • To compare five methods for handling auxiliary-variable-dependent missing at random data in partially clustered studies.
  • To identify the most effective imputation or estimation strategies for different data structures.
  • To provide guidance for researchers dealing with missing data in complex trial designs.

Main Methods:

  • A simulation study was conducted to compare five statistical methods.
  • Methods included various multiple imputation (MI) and sequential fully Bayesian (SFB) approaches.
  • Specific methods compared: MI-JM-SIM, MI-JM-AS, MI-SMC-AS, SFB-NON, SFB-WEAK.

Main Results:

  • The arm-specific multiple imputation using joint modeling (MI-JM-AS) method showed superior performance when missing variables involved only fixed effects.
  • The arm-specific multiple imputation using substantive-model-compatible sequential modeling (MI-SMC-AS) method was preferred when incomplete variables included random effects.
  • Performance varied based on the presence of fixed versus random effects in the incomplete variables.

Conclusions:

  • The choice of method for handling missing data in partially clustered trials depends on the nature of the incomplete variables (fixed vs. random effects).
  • MI-JM-AS and MI-SMC-AS are recommended under specific conditions for improved accuracy.
  • Empirical data examples illustrate the practical application of these methods.