Regression modeling of the cumulative incidence function with missing causes of failure using pseudo-values
Résumé
Competing risks arise when patients may fail from several causes. Strategies for modeling cause-specific functionals
often assume that the cause of failure is known for all patients, but this is seldom the case. Several authors have
addressed the problem of modeling the cause-specific hazard rates with missing causes of failure. In contrast, direct
modeling of the cumulative incidence function has received little attention. We provide a general framework for
regression modeling of this function in the missing cause setting, encompassing key models such as the Fine and
Gray and additive models, by considering two extensions of the Andersen-Klein pseudo-value approach. The first
extension is a novel inverse probability weighting method, while the second extension is based on a previously
proposed multiple imputation procedure. The gain in using these approaches with small samples was evaluated
in an extensive simulation study. Asymptotic properties were verified and variance estimators were suggested and
evaluated. We analyzed the data from an ECOG breast cancer treatment clinical trial to illustrate the practical
value and ease of implementation of the proposed methods.