  Regression analyses based on transformations of cumulative incidence
functions are often adopted when modeling and testing for treatment effects in
clinical trial settings involving competing and semi-competing risks. Common
frameworks include the Fine-Gray model and models based on direct binomial
regression. Using large sample theory we derive the limiting values of
treatment effect estimators based on such models when the data are generated
according to multiplicative intensity-based models, and show that the estimand
is sensitive to several process features. The rejection rates of hypothesis
tests based on cumulative incidence function regression models are also
examined for null hypotheses of different types, based on which a robustness
property is established. In such settings supportive secondary analyses of
treatment effects are essential to ensure a full understanding of the nature of
treatment effects. An application to a palliative study of individuals with
breast cancer metastatic to bone is provided for illustration.
