Semiparametric regression methods for temporal processes subject to multiple sources of censoring.

ABSTRACT: Process regression methodology is underdeveloped relative to the frequency with which pertinent data arise. In this article, the response is a binary indicator process representing the joint event of being alive and remaining in a specific state. The process is indexed by time (e.g., time since diagnosis) and observed continuously. Data of this sort occur frequently in the study of chronic disease. A general area of application involves a recurrent event with non-negligible duration (e.g., hospitalization and associated length of hospital stay) and subject to a terminating event (e.g., death). We propose a semiparametric multiplicative model for the process version of the probability of being alive and in the (transient) state of interest. Under the proposed methods, the regression paramet

SUBMITTER: Zhan T 

PROVIDER: S-EPMC12893071 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature