A regression model for the conditional probability of a competing event: application to monoclonal gammopathy of unknown significance

Arthur Allignol, Aurélien Latouche, Jun Yan and Jason Fine have a new paper in Applied Statistics (JRSS C). The paper concerns competing risks data and develops methods for regression analysis of the probability of a competing event conditional on no competing event having occurred. In terms of the cumulative incidence functions, for the case of two competing events, this can be written as . In some applications this quantity may be more useful than either the cause-specific hazards or the cumulative incidence functions themselves. One approach to regression is this scenario might be to compute pseudo-observations and perform the regression using those. The authors instead propose use of temporal process regression (Fine, Yan and Kosorok 2004), allowing estimation of time dependent regression parameters, by considering the cross-sectional data at each event time.