In a competing risks analysis, interest lies in the cause-specific cumulative incidence function (CIF) which is usually obtained in a modelling framework by either (1) transforming on all of the cause-specific hazard (CSH) or (2) through its direct relationship with the subdistribution hazard (SDH) function. We expand on current competing risks methodology from within the flexible parametric survival modelling framework (FPM) and focus on approach (2). This models all cause-specific CIFs simultaneously and is more useful when prognostic related questions are to be answered. We propose the direct FPM approach for the cause-specific CIF which models the (log-cumulative) baseline hazard without the requirement of numerical integration leading to benefits in computational time. It is also easy to make out-of-sample predictions to estimate more useful measures and alternative link functions can be incorporated, for example, the logit link. To implement the methods, a new estimation command, stpm2cr, is introduced and useful predictions from the model are demonstrated through an illustrative Melanoma dataset.
Keywords: competing risks; cumulative incidence function; flexible parametric models; st0001; stpm2cr; subdistribution hazard; survival analysis.