How to deal with missing data? Multiple imputation by chained equations: recommendations and explanations for...

Area of Science:

• Medical Research Methodology
• Biostatistics
• Data Science

Background:

• Missing data is a pervasive issue in medical research, leading to reduced statistical power and potential biases.
• Understanding the types of missing data—missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR)—is crucial for appropriate analysis.
• Traditional methods like complete case analysis are often suboptimal and can introduce bias.

Purpose of the Study:

• To provide a comprehensive guide on handling missing data in medical research using multiple imputation by chained equations (MICE).
• To explain the principles behind MICE, including simulating plausible values and incorporating random effects to account for uncertainty.
• To advocate for the adoption of MICE over complete case analysis as the standard approach.

Main Methods:

• Detailed explanation of the multiple imputation by chained equations (MICE) technique.
• Simulation of multiple plausible values for each missing data point, considering relationships with other variables.
• Combination of results from individually analyzed imputed datasets to produce a robust global estimator.
• Inclusion of a random effect in the simulation process to reflect uncertainty.

Main Results:

• Multiple imputation effectively increases statistical power in analyses.
• The method corrects for biases that can arise from missing data.
• MICE is versatile and applicable to various data types.
• The approach enhances the representativeness of the analyzed sample.

Conclusions:

• Multiple imputation by chained equations is a superior method for handling missing data compared to complete case analysis.
• The technique improves the reliability and validity of medical research findings.
• This guide offers practical insights and code examples (R® package mice) for implementing MICE.