Qualifying Missingness in Real-World Clinical Data for Secondary Use

Clinical data warehouses (CDWs) often contain missing data due to non-systematic collection. Characterizing this missingness through descriptors of rate, pattern, and mechanism helps assess dataset quality and understand real-world data. We first identified relevant qualification methods for clinical data through a scoping review. We then analyzed the behavior of the descriptors they provide, by implementing them in an automated descriptor extraction pipeline. To evaluate our approach, four missing data scenarios were simulated from the same complete dataset, reflecting clinically plausible situations. Descriptor variability across scenarios was tested using paired non-parametric tests (Friedman for numeric, Cochran's Q for binary descriptors). In total, 230 descriptors were extracted for each of 40 incomplete datasets, and 37.4% showed significant adjusted p-values after Benjamini-Hochberg correction. The most discriminant descriptors accurately reflected the scenario structures, demonstrating that descriptor-based qualification can provide interpretable insights into data quality in clinical warehouses.