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Competing Risk of Death: An Important Consideration in Studies of Older Adults

2010/03/22 by Sarah D. Berry, Long Ngo, Elizabeth J. Samelson +1 · 596 citations
Economics, Econometrics and Finance · Medicine · #Cohort #Colorectal Cancer Screening and Detection #Cumulative incidence #Demography #Disease #Environmental health #Framingham Heart Study #Framingham Risk Score #Gerontology #Health Systems, Economic Evaluations, Quality of Life #Hip fracture #Incidence (geometry) #Internal medicine #Liver Disease Diagnosis and Treatment #Medicine #Osteoporosis #Population #Proportional hazards model #Risk assessment #Surgery #Survival analysis

paper · open access · doi:10.1111/j.1532-5415.2010.02767.x

published in Journal of the American Geriatrics Society 58(4), 783-787 (Wiley)

openalex publication_date 2010/03/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

Clinical studies often face the difficult problem of how to account for participants who die without experiencing the study outcome of interest. In a geriatric population with considerable comorbidities, the competing risk of death is especially high. Traditional approaches to describe risk of disease include Kaplan-Meier survival analysis and Cox proportional hazards regression, but these methods can overestimate risk of disease by failing to account for the competing risk of death. This report discusses traditional survival analysis and competing risk analysis as used to estimate risk of disease in geriatric studies. Furthermore, it illustrates a competing risk approach to estimate risk of second hip fracture in the Framingham Osteoporosis Study and compares the results with traditional survival analysis. In this example, survival analysis overestimated the 5-year risk of second hip fracture by 37% and the 10-year risk by 75% compared with competing risk estimates. In studies of older individuals in which a substantial number of participants die during a long follow-up, the cumulative incidence competing risk estimate and competing risk regression should be used to determine incidence and effect estimates. Use of a competing risk approach is critical to accurately determining disease risk for elderly individuals and therefore best inform clinical decision-making.

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