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Development and Validation of a High‐Quality Composite Real‐World Mortality Endpoint
Author(s) -
Curtis Melissa D.,
Griffith Sandra D.,
Tucker Melisa,
Taylor Michael D.,
Capra William B.,
Carrigan Gillis,
Holzman Ben,
Torres Aracelis Z.,
You Paul,
Arnieri Brandon,
Abernethy Amy P.
Publication year - 2018
Publication title -
health services research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.706
H-Index - 121
eISSN - 1475-6773
pISSN - 0017-9124
DOI - 10.1111/1475-6773.12872
Subject(s) - medicine , data quality , national death index , benchmarking , data collection , data mining , statistics , oncology , computer science , hazard ratio , operations management , confidence interval , mathematics , metric (unit) , business , marketing , economics
Objective To create a high‐quality electronic health record ( EHR )–derived mortality dataset for retrospective and prospective real‐world evidence generation. Data Sources/Study Setting Oncology EHR data, supplemented with external commercial and US Social Security Death Index data, benchmarked to the National Death Index ( NDI ). Study Design We developed a recent, linkable, high‐quality mortality variable amalgamated from multiple data sources to supplement EHR data, benchmarked against the highest completeness U.S. mortality data, the NDI . Data quality of the mortality variable version 2.0 is reported here. Principal Findings For advanced non‐small‐cell lung cancer, sensitivity of mortality information improved from 66 percent in EHR structured data to 91 percent in the composite dataset, with high date agreement compared to the NDI . For advanced melanoma, metastatic colorectal cancer, and metastatic breast cancer, sensitivity of the final variable was 85 to 88 percent. Kaplan–Meier survival analyses showed that improving mortality data completeness minimized overestimation of survival relative to NDI ‐based estimates. Conclusions For EHR ‐derived data to yield reliable real‐world evidence, it needs to be of known and sufficiently high quality. Considering the impact of mortality data completeness on survival endpoints, we highlight the importance of data quality assessment and advocate benchmarking to the NDI .