Competing risk regression models for epidemiologic data.
Competing events can preclude the event of interest from occurring in epidemiologic data and can be analyzed by using extensions of survival analysis methods. In this paper, the authors outline 3 regression approaches for estimating 2 key quantities in competing risks analysis: the cause-specific relative hazard ((cs)RH) and the subdistribution relative hazard ((sd)RH). They compare and contrast the structure of the risk sets and the interpretation of parameters obtained with these methods. They also demonstrate the use of these methods with data from the Women's Interagency HIV Study established in 1993, treating time to initiation of highly active antiretroviral therapy or to clinical disease progression as competing events. In our example, women with an injection drug use history were less likely than those without a history of injection drug use to initiate therapy prior to progression to acquired immunodeficiency syndrome or death by both measures of association ((cs)RH = 0.67, 95% confidence interval: 0.57, 0.80 and (sd)RH = 0.60, 95% confidence interval: 0.50, 0.71). Moreover, the relative hazards for disease progression prior to treatment were elevated ((cs)RH = 1.71, 95% confidence interval: 1.37, 2.13 and (sd)RH = 2.01, 95% confidence interval: 1.62, 2.51). Methods for competing risks should be used by epidemiologists, with the choice of method guided by the scientific question.
Lau
Bryan
B
Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland 21287, USA. blau1@jhmi.edu
Cole
Stephen R
SR
Gange
Stephen J
SJ
D000328:Adult; D016001:Confidence Intervals; D004812:Epidemiologic Methods; D005260:Female; D015658:HIV Infections / Q000453:epidemiology*; D006801:Humans; D053208:Kaplan-Meier Estimate; D008396:Maryland / Q000453:epidemiology; D015233:Models, Statistical; D015999:Multivariate Analysis; D016016:Proportional Hazards Models; D012044:Regression Analysis; D018570:Risk Assessment; D016019:Survival Analysis