Competing risk of death: an important consideration in studies of older adults

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.

Figures

Figure 1. Simulated Estimates of Incidence of Second Hip Fracture According to Varying Mortality as Calculated by Cumulative Incidence Competing Risk Estimates

Figure 2

Difference in Cox Regression and Competing Risk Regression Results when Calculating the Effect Estimate of Age on the Risk of Second Hip Fracture in a Simulation Study with Varying Mortality