Background — Ignoring competing risks in time-to-event analyses can lead to biased risk estimates, particularly for elderly patients with multimorbidity. We aimed to demonstrate the impact of considering competing risks when estimating the cumulative incidence and risk of stroke among elderly atrial fibrillation patients.
Methods and Results — Using linked administrative databases, we identified patients with atrial fibrillation aged ≥66 years discharged from hospital in ON, Canada between January 1, 2007, and March 31, 2011. We estimated the cumulative incidence of stroke hospitalization using the complement of the Kaplan–Meier function and the cumulative incidence function. This was repeated after stratifying the cohort by presence of prespecified comorbidities: chronic kidney disease, chronic obstructive pulmonary disease, cancer, or dementia. The full cohort was used to regress components of the CHA2DS2VASc (congestive heart failure, hypertension, age, diabetes mellitus, stroke, vascular disease, sex) score on the hazard of stroke hospitalization using the Fine-Gray and Cox methods. These models were subsequently used to predict the 5-year risk of stroke hospitalization. Among 136 156 patients, the median CHA2DS2VASc score was 4 and 84 728 patients (62.2%) had ≥1 prespecified comorbidity. The 5-year cumulative incidence of stroke was 5.4% (95% confidence interval, 5.3%–5.5%), whereas that of death without stroke was 48.8% (95% confidence interval, 48.5%–49.1%). The incidence of both events was overestimated by the Kaplan–Meier method; stroke incidence was overestimated by a relative factor of 39%. The degree of overestimation was larger among patients with non-CHA2DS2VASc comorbidity because of higher incidence of death without stroke. The Fine-Gray model demonstrated better calibration than the Cox model, which consistently overpredicted stroke incidence.
Conclusions — The incidence of death without stroke was 9-fold higher than that of stroke, leading to biased estimates of stroke risk with traditional time-to-event methods. Statistical methods that appropriately account for competing risks should be used to mitigate this bias.
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