Non-Parametric Inference for Cumulative Incidence Functions in Competing Risks Studies

In the competing risks problem, a useful quantity is the cumulative incidence function, which is the probability of occurrence by time t for a particular type of failure in the presence of other risks. The estimator of this function as given by Kalbfleisch and Prentice is consistent, and, properly normalized, converges weakly to a zero-mean Gaussian process with a covariance function for which a consistent estimator is provided. A resampling technique is developed to approximate the distribution of this process, which enables one to construct confidence bands for the cumulative incidence curve over the entire time span of interest and to perform Kolmogorov-Smirnov type tests for comparing two such curves. An AIDS example is provided.