Bias Implications of Outcome Misclassification in Observational Studies Evaluating Association Between Treatments and All‐Cause or Cardiovascular Mortality Using Administrative Claims

Background The bias implications of outcome misclassification arising from imperfect capture of mortality in claims-based studies are not well understood. Methods and Results We identified 2 cohorts of patients: (1) type 2 diabetes mellitus (n=8.6 million), and (2) heart failure (n=3.1 million), from Medicare claims (2012-2016). Within the 2 cohorts, mortality was identified from claims using the following approaches: (1) all-place all-cause mortality, (2) in-hospital all-cause mortality, (3) all-place cardiovascular mortality (based on diagnosis codes for a major cardiovascular event within 30 days of death date), or (4) in-hospital cardiovascular mortality, and compared against National Death Index identified mortality. Empirically identified sensitivity and specificity based on observed values in the 2 cohorts were used to conduct Monte Carlo simulations for treatment effect estimation under differential and nondifferential misclassification scenarios. From National Death Index, 1 544 805 deaths (549 996 35.6% cardiovascular deaths) in the type 2 diabetes mellitus cohort and 1 175 202 deaths (523 430 44.5% cardiovascular deaths) in the heart failure cohort were included. Sensitivity was 99.997% and 99.207% for the all-place all-cause mortality approach, whereas it was 27.71% and 33.71% for the in-hospital all-cause mortality approach in the type 2 diabetes mellitus and heart failure cohorts, respectively, with perfect positive predicted values. For all-place cardiovascular mortality, sensitivity was 52.01% in the type 2 diabetes mellitus cohort and 53.83% in the heart failure cohort with positive predicted values of 49.98% and 54.45%, respectively. Simulations suggested a possibility for substantial bias in treatment effects. Conclusions Approaches to identify mortality from claims had variable performance compared with the National Death Index. Investigators should anticipate the potential for bias from outcome misclassification when using administrative claims to capture mortality.