The competing risks Cox model with auxiliary case covariates under weaker missing-at-random cause of failure.

ABSTRACT: In the analysis of time-to-event data with multiple causes using a competing risks Cox model, often the cause of failure is unknown for some of the cases. The probability of a missing cause is typically assumed to be independent of the cause given the time of the event and covariates measured before the event occurred. In practice, however, the underlying missing-at-random assumption does not necessarily hold. Motivated by colorectal cancer molecular pathological epidemiology analysis, we develop a method to conduct valid analysis when additional auxiliary variables are available for cases only. We consider a weaker missing-at-random assumption, with missing pattern depending on the observed quantities, which include the auxiliary covariates. We use an informative likelihood approach that

SUBMITTER: Nevo D 

PROVIDER: S-EPMC5797530 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature