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Dependence modeling for recurrent event times subject to right‐censoring with D‐vine copulas

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  • Nicole Barthel
  • Candida Geerdens
  • Claudia Czado
  • Paul Janssen

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.

Suggested Citation

  • Nicole Barthel & Candida Geerdens & Claudia Czado & Paul Janssen, 2019. "Dependence modeling for recurrent event times subject to right‐censoring with D‐vine copulas," Biometrics, The International Biometric Society, vol. 75(2), pages 439-451, June.
  • Handle: RePEc:bla:biomet:v:75:y:2019:i:2:p:439-451
    DOI: 10.1111/biom.13014
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    Cited by:

    1. Akim Adekpedjou & Sophie Dabo‐Niang, 2021. "Semiparametric estimation with spatially correlated recurrent events," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(4), pages 1097-1126, December.
    2. Tao Sun & Yu Cheng & Ying Ding, 2023. "An information ratio‐based goodness‐of‐fit test for copula models on censored data," Biometrics, The International Biometric Society, vol. 79(3), pages 1713-1725, September.
    3. Genest Christian & Scherer Matthias, 2019. "The world of vines: An interview with Claudia Czado," Dependence Modeling, De Gruyter, vol. 7(1), pages 169-180, January.
    4. Pan, Shenyi & Joe, Harry, 2022. "Predicting times to event based on vine copula models," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
    5. Eleanderson Campos & Roel Braekers & Devanil J. Souza & Lucas M. Chaves, 2021. "Factor copula models for right-censored clustered survival data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(3), pages 499-535, July.

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