Dealing with competing risks: testing covariates and calculating sample size.
It is universally agreed that Kaplan-Meier estimates overestimate the probability of the event of interest in the presence of competing risks. Kalbfleisch and Prentice recommend using the cumulative incidence as an estimate of the probability of an event of interest. However, there is no consensus on how to test the effect of a covariate in the presence of competing risks. Using simulations, this paper illustrates that the Cox proportional hazards model gives valid results when employed in testing the effect of a covariate on the hazard rate and when estimating the hazard ratio. A method to calculate the sample size for testing the effect of a covariate on outcome in the presence of competing risks is also provided.
Pintilie
Melania
M
Ontario Cancer Institute, Princess Margaret Hospital, Toronto, Canada. melania.pintilie@uhn.on.ca
D001943:Breast Neoplasms / Q000532:radiotherapy; D003198:Computer Simulation; D015986:Confounding Factors, Epidemiologic; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D009203:Myocardial Infarction / Q000209:etiology; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D012307:Risk Factors; D018401:Sample Size