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A comparison of multiple imputation methods for missing data in longitudinal studies.

Md Hamidul Huque1,2, John B Carlin3,4,5, Julie A Simpson5

  • 1Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Parkville, VIC, 3052, Australia. hamidul_b7@yahoo.com.

BMC Medical Research Methodology
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PubMed
Summary
This summary is machine-generated.

Multiple imputation (MI) methods effectively handle missing longitudinal data. Standard methods like FCS-Standard and JM-MVN perform well for regression, with complex models only needed for specific cases.

Keywords:
FCSJoint modellingLinear mixed modelLongitudinal missing dataMICEMultilevel multiple imputationMultiple imputation

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Multiple imputation (MI) is crucial for handling missing data in longitudinal studies.
  • Various MI techniques exist, but their comparative performance in longitudinal data is not well-established.
  • Standard methods (FCS-Standard, JM-MVN) and mixed-model extensions are available but lack comprehensive evaluation.

Purpose of the Study:

  • To comprehensively evaluate the performance of multiple imputation methods for longitudinal data.
  • To compare 12 different MI techniques using both simulated and empirical data.
  • To assess MI performance in analyzing the association between child BMI and quality of life.

Main Methods:

  • Utilized data from the Longitudinal Study of Australian Children (N=4661) across six waves.
  • Conducted a simulation study under missing at random (MAR) mechanisms.
  • Applied linear regression and linear mixed-effects models to analyze child BMI and quality of life.

Main Results:

  • Identified and compared 12 MI methods for longitudinal data imputation.
  • MI methods showed less bias and better coverage for linear regression models.
  • Approximately half of the MI methods performed well for linear mixed models with random intercepts.
  • An inverse association between child BMI and quality of life was observed.

Conclusions:

  • Standard Fully Conditional Specification (FCS-Standard) and Joint Multivariate Normal imputation (JM-MVN) demonstrated strong performance for regression parameter estimation.
  • Complex MI methods reflecting longitudinal structure are typically unnecessary unless dealing with irregularly spaced data.