Marginal Analysis of Clustered Interval-Censored Competing Risks Data With Informative Cluster Size

Motivated by the National Alzheimer's Coordinating Center (NACC) study, this paper addresses the marginal analysis of clustered data under a complex setting involving interval censoring, competing risks, and informative cluster size, for which existing methods are not readily applicable. We consider a class of marginal generalized odds-rate transformation models for the cumulative incidence function in the presence of interval-censored competing risks, with the Fine-Gray model and the sub-distribution proportional odds model included as special cases. For statistical inference, a cluster-weighted sieve maximum pseudo-likelihood estimation procedure is developed, in which B-splines are used to approximate the nonparametric components of the semiparametric models. This method effectively reduces the original infinite-dimensional estimation problem to a finite-dimensional one. We establish the asymptotic properties of the resulting parameter estimators and conduct extensive simulation studies to evaluate their finite-sample performance. The proposed methodology is applied to the motivating NACC data to investigate potential risk factors for the cumulative incidence of Lewy body dementia.