Semiparametric regression on cumulative incidence function with interval-censored competing risks data and missing event types.

ABSTRACT: Competing risk data are frequently interval-censored, that is, the exact event time is not observed but only known to lie between two examination time points such as clinic visits. In addition to interval censoring, another common complication is that the event type is missing for some study participants. In this article, we propose an augmented inverse probability weighted sieve maximum likelihood estimator for the analysis of interval-censored competing risk data in the presence of missing event types. The estimator imposes weaker than usual missing at random assumptions by allowing for the inclusion of auxiliary variables that are potentially associated with the probability of missingness. The proposed estimator is shown to be doubly robust, in the sense that it is consistent even if ei

SUBMITTER: Park J 

PROVIDER: S-EPMC9291598 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature