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

ABSTRACT

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.