Comprehensive missing data analysis and multiple imputation using mice and ggmice packages. This function provides a complete workflow for analyzing missing data patterns, performing multiple imputation by chained equations (MICE), and evaluating imputation quality. Designed specifically for clinical research applications where missing data is common and proper handling is critical for valid statistical inference.
Details
The missing data analysis function provides three main analysis types:
Pattern Analysis: Explores missing data structure and patterns
Multiple Imputation: Performs MICE imputation with convergence diagnostics
Complete Analysis: Combines pattern analysis and imputation
Key features include:
Visual and tabular missing data pattern analysis
Multiple imputation methods (PMM, Bayesian regression, logistic regression)
Convergence diagnostics with trace plots
Quality evaluation comparing observed vs imputed data
Flexible parameter customization
Clinical research focused interpretations
Common clinical applications:
Data quality assessment for clinical trials
Missing data handling in observational studies
Regulatory compliance for pharmaceutical research
Sensitivity analysis for missing data assumptions
Examples
if (FALSE) { # \dontrun{
# Basic pattern analysis
result <- missingdata(
data = clinical_data,
analysis_vars = c("age", "bmi", "biomarker"),
analysis_type = "pattern"
)
# Multiple imputation
result <- missingdata(
data = clinical_data,
analysis_vars = c("age", "bmi", "biomarker"),
analysis_type = "imputation",
n_imputations = 10,
imputation_method = "pmm"
)
# Complete analysis
result <- missingdata(
data = clinical_data,
analysis_vars = c("age", "bmi", "biomarker"),
analysis_type = "complete",
n_imputations = 5,
max_iterations = 10
)
} # }