Subgroup Identification and Regression Analysis of Clustered and Heterogeneous Interval-Censored Data

Xifen Huang and Jinfeng Xu
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Xifen Huang: School of Mathematics, Yunnan Normal University, Kunming 650500, China
Jinfeng Xu: School of Mathematics, Yunnan Normal University, Kunming 650500, China

Mathematics, 2022, vol. 10, issue 6, 1-11

Abstract: Clustered and heterogeneous interval-censored data occur in many fields such as medical studies. For example, in a migraine study with the Netherlands Twin Registry, the information including time to diagnosis of migraine and gender was collected for 3975 monozygotic and dizygotic twins. Since each study subject is observed only at discrete and periodic follow-up time points, the failure times of interest (i.e., the time when the individual first had a migraine) are known only to belong to certain intervals and hence are interval-censored. Furthermore, these twins come from different genetic backgrounds and may be associated with differential risks for developing migraines. For simultaneous subgroup identification and regression analysis of such data, we propose a latent Cox model where the number of subgroups is not assumed a priori but rather data-driven estimated. The nonparametric maximum likelihood method and an EM algorithm with monotone ascent property are also developed for estimating the model parameters. Simulation studies are conducted to assess the finite sample performance of the proposed estimation procedure. We further illustrate the proposed methodologies by an empirical analysis of migraine data.

Keywords: clustered interval-censored data; EM algorithm; heterogeneous covariate effects; latent Cox model; migraine data; nonparametric maximum likelihood (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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