Comparing k cumulative incidence functions through resampling methods

Tests for the equality of k cumulative incidence functions in a competing risks model are proposed. Test statistics are based on a vector of processes related to the cumulative incidence functions. Since their asymptotic distributions appear very complicated and depend on the underlying distribution of the data, two resampling techniques, namely the well-known bootstrap method and the so-called random symmetrization method, are used to approximate the critical values of the tests. Without making any assumptions on the nature of dependence between the risks, the tests allow one to compare k risks simultaneously for k > or = 2 under the random censorship model. Tests against ordered alternatives are also considered. Simulation studies indicate that the proposed tests perform very well with moderate sample size. A real application to cancer mortality data is given.