Comprehensive missing data pattern exploration and visualization for clinical research. Analyzes missing data mechanisms, patterns, and correlations to inform imputation strategies. Includes advanced diagnostics for missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR) assessments. Essential for understanding missingness before imputation and for regulatory documentation of missing data handling strategies.
Usage
missingdataexplorer(
data,
analysis_vars,
group_var,
time_var,
id_var,
pattern_analysis = TRUE,
mechanism_testing = TRUE,
correlation_analysis = TRUE,
temporal_analysis = FALSE,
group_comparison = FALSE,
mcar_test = "little",
min_pattern_freq = 0.05,
max_patterns_display = 20,
pattern_plot = TRUE,
correlation_plot = TRUE,
temporal_plot = FALSE,
upset_plot = TRUE,
cumulative_plot = FALSE,
monotonic_test = TRUE,
dropout_analysis = FALSE,
informative_missingness = TRUE,
completeness_threshold = 0.8,
case_completeness = TRUE,
variable_importance = TRUE,
chi_square_test = TRUE,
logistic_regression = FALSE,
survival_analysis = FALSE,
detailed_patterns = TRUE,
summary_statistics = TRUE,
clinical_interpretation = TRUE,
export_patterns = FALSE
)Arguments
- data
the data as a data frame
- analysis_vars
Variables to include in missing data pattern analysis
- group_var
Grouping variable for comparing missingness patterns (e.g., treatment arm, study site)
- time_var
Time variable for temporal missingness analysis (e.g., visit number, time since baseline)
- id_var
Subject identifier for longitudinal missingness analysis
- pattern_analysis
Analyze missing data patterns and frequencies
- mechanism_testing
Test missing data mechanisms (MCAR, MAR, MNAR)
- correlation_analysis
Analyze correlations between missingness indicators
- temporal_analysis
Analyze missingness patterns over time
- group_comparison
Compare missingness patterns between groups
- mcar_test
Method for testing Missing Completely At Random assumption
- min_pattern_freq
Minimum frequency for reporting missing data patterns (5 percent = 0.05)
- max_patterns_display
Maximum number of missing data patterns to display in detail
- pattern_plot
Generate missing data pattern plots
- correlation_plot
Generate missingness correlation heatmap
- temporal_plot
Generate temporal missingness plots
- upset_plot
Generate UpSet plot for pattern visualization
- cumulative_plot
Generate cumulative missingness over variables plot
- monotonic_test
Test whether missingness follows monotonic patterns
- dropout_analysis
Analyze dropout patterns in longitudinal data
- informative_missingness
Test whether missingness is informative (related to outcomes)
- completeness_threshold
Threshold for acceptable variable completeness (80 percent = 0.8)
- case_completeness
Analyze completeness at the case/subject level
- variable_importance
Assess which variables predict missingness in others
- chi_square_test
Chi-square tests for independence of missingness
- logistic_regression
Logistic regression to predict missingness patterns
- survival_analysis
Survival analysis for time to dropout/missingness
- detailed_patterns
Show detailed information for each missing data pattern
- summary_statistics
Include comprehensive summary statistics
- clinical_interpretation
Provide clinical interpretation and recommendations
- export_patterns
Export missing data patterns for external analysis
Value
A results object containing:
results$summary_statistics | a table | ||||
results$mcar_test_results | a table | ||||
results$missing_patterns | a table | ||||
results$correlation_matrix | a table | ||||
results$pattern_plot | an image | ||||
results$upset_plot | an image |
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$summary_statistics$asDF
as.data.frame(results$summary_statistics)
Examples
# \donttest{
data('clinical_data')
#> Warning: data set ‘clinical_data’ not found
missingdataexplorer(
data = clinical_data,
analysis_vars = c("primary_endpoint", "biomarker", "demographics"),
group_var = "treatment_arm",
pattern_analysis = TRUE,
mechanism_testing = TRUE
)
#> Error: object 'clinical_data' not found
# }