Related Experiment Video
Updated: Nov 9, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Validation of a Mortality Composite Score in the Real-World Setting: Overcoming Source-Specific Disparities and
Michelle H Lerman1, Benjamin Holmes1, Daniel St Hilaire1
1Syapse, San Francisco, CA.
Purpose:
This study tested whether a composite mortality score could overcome gaps and potential biases in individual real-world mortality data sources. Complete and accurate mortality data are necessary to calculate important outcomes in oncology, including overall survival. However, in the United States, there is not a single complete and broadly applicable mortality data source. It is further likely that available data sources are biased in their coverage of sex, race, age, and socioeconomic status (SES).
Methods:
Six individual real-world data sources were combined to develop a high-quality composite mortality score. The composite score was benchmarked against the gold standard for mortality data, the National Death Index. Subgroup analyses were then conducted to evaluate the completeness and accuracy by sex, race, age, and SES.
Results:
The composite mortality score achieved a sensitivity of 94.9% and specificity of 92.8% compared with the National Death Index, with concordance within 1 day of 98.6%. Although some individual data sources show significant coverage gaps related to sex, race, age, and SES, the composite score maintains high sensitivity (84.6%-96.1%) and specificity (77.9%-99.2%) across subgroups.
Conclusion:
A composite score leveraging multiple scalable sources for mortality in the real-world setting maintained strong sensitivity, specificity, and concordance, including across sex, race, age, and SES subgroups.
Related Concept Videos
Actuarial Approach
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Strategies for Assessing and Addressing Confounding
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Bias in Epidemiological Studies
Kaplan-Meier Approach
Assumptions of Survival Analysis
Comparing the Survival Analysis of Two or More Groups

