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Title: | Dependence modeling for recurrent event times subject to right-censoring with D-vine copulas | Authors: | Barthel, Nicole GEERDENS, Candida Czado, Claudia JANSSEN, Paul |
Issue Date: | 2019 | Publisher: | WILEY | Source: | BIOMETRICS, 75(2), p. 439-451 | Abstract: | In many time-to-event studies, the event of interest is recurrent. Here, the data for each sample unit correspond to a series of gap times between the subsequent events. Given a limited follow-up period, the last gap time might be right-censored. In contrast to classical analysis, gap times and censoring times cannot be assumed independent, i.e., the sequential nature of the data induces dependent censoring. Also, the number of recurrences typically varies among sample units leading to unbalanced data. To model the association pattern between gap times, so far only parametric margins combined with the restrictive class of Archimedean copulas have been considered. Here, taking the specific data features into account, we extend existing work in several directions: we allow for nonparametric margins and consider the flexible class of D-vine copulas. A global and sequential (one- and two-stage) likelihood approach are suggested. We discuss the computational efficiency of each estimation strategy. Extensive simulations show good finite sample performance of the proposed methodology. It is used to analyze the association of recurrent asthma attacks in children. The analysis reveals that a D-vine copula detects relevant insights, on how dependence changes in strength and type over time. | Notes: | [Barthel, Nicole; Czado, Claudia] Tech Univ Munich, Dept Math, Boltzmannstr 3, D-85748 Garching, Germany. [Geerdens, Candida; Janssen, Paul] Univ Hasselt, Ctr Stat, BioStat 1, Agoralaan 1, B-3590 Diepenbeek, Belgium. | Keywords: | dependence modeling;D-vine copulas;induced dependent right-censoring;maximum likelihood estima-tion;recurrent event time data;unbalanced gap time data | Document URI: | http://hdl.handle.net/1942/30052 | ISSN: | 0006-341X | e-ISSN: | 1541-0420 | DOI: | 10.1111/biom.13014 | ISI #: | 000483730600012 | Rights: | 2019 International Biometric Society | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2020 |
Appears in Collections: | Research publications |
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