Competing risk analyses with special reference to matched pair experiments

Several models for dependent competing risk data are considered. It is assumed that the effects of certain risk factors or treatments are of primary interest and such effects are written as regression parameters in a proportional cause-specific hazards model. Techniques based on marginally sufficient statistics and partial likelihood are used for their estimation. Efficiency comparisons are made among estimates based on the suggested models. Special attention is given to the analysis of matched pair data and the methods are illustrated in a detailed analysis of data obtained from an ongoing Swedish twin study.