Missing Data Analysis for Clinical Variables by Outcome Status

4.2% overall missingness characterizes this clinical dataset, with no variable exceeding 15% missing data. The CSV file presents percentages of missing values for each variable across an overall cohort and stratified by one-year survival outcome. Alexy Tran-Dinh published the analysis on figshare in May 2026.

Use Cases

• Assess data completeness for clinical variables based on reported missingness percentages.
• Compare missing data patterns between survivors and non-survivors based on stratified analysis.
• Evaluate potential bias from differential missingness using chi-square test p-values mentioned in the description.
• Benchmark missing data rates against a threshold of 15% per variable.

Strengths

• Overall missingness is quantified at 4.2%, providing a clear completeness metric.
• No variable exceeds 15% missing data, indicating relatively complete records.
• Analysis includes chi-square test p-values for comparing missingness between outcome groups.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

figshare

Freshness

Last updated 2026-05-05 18:00:19; freshness should be verified.