Methods accounting for competing risks in time-to-event problems are becoming common in mainstream statistical analyses.
Standard approaches include those based on log-rank type tests (Gray) and cumulative incidence regression (Fine and Gray). These approaches are based on weighting competing events by the censoring distribution. The usual cumulative incidence regression uses weights based on the pooled censoring distribution. However, the effects of the pattern of events and censoring in these approaches are still unclear.
We are examining two aspects of this problem: the amount of competing risk present (by using a proportional-hazards model) and the pattern of censoring between groups in the presence of competing risks.