Model checking in multiple imputation: an overview and case study.

Area of Science:

• Statistics
• Data Science
• Biostatistics

Background:

• Multiple imputation is a widely adopted technique for addressing missing data in statistical analyses.
• The accuracy of multiple imputation results depends heavily on the appropriateness of the imputation model used.
• Currently, there is a lack of comprehensive guidelines for validating these imputation models.

Purpose of the Study:

• To provide an overview of existing methods for checking the validity of imputation models.
• To highlight the importance of model checking for reliable multiple imputation analyses.
• To offer practical guidance for researchers using multiple imputation.

Main Methods:

• Review of graphical and numerical summary techniques for imputation model assessment.
• Introduction to simulation-based methods, including posterior predictive checking.
• Illustration of model checking techniques using real-world data from the Longitudinal Study of Australian Children.

Main Results:

• Multiple imputation is a powerful tool, but its effectiveness is contingent on proper model validation.
• Various techniques, from visual checks to simulations, can be employed to assess imputation models.
• The study demonstrates the application of these methods in a practical research setting.

Conclusions:

• As multiple imputation becomes a standard method, robust model checking is essential for ensuring data integrity.
• Researchers must adopt appropriate model checking strategies to validate their imputation models.
• Implementing these checks guarantees the reliability and validity of findings derived from multiple imputation.