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Copula Models and Diagnostics for Multivariate Interval-Censored Data

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Emerging Topics in Modeling Interval-Censored Survival Data

Part of the book series: ICSA Book Series in Statistics ((ICSABSS))

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Abstract

In studies concerning disease progression or patient survival, multivariate time-to-event outcomes are increasingly used as endpoints. The exact times from the non-fatal events are sometimes unobservable due to “interval-censoring” since the event status can only be determined at intermittent assessment times. In this chapter, we introduce a class of copula models to analyze multivariate interval-censored outcomes. It is a joint approach that directly connects the marginal (univariate) distributions through a copula function to construct the joint distribution. We allow flexible modeling of the covariate effects through a semiparametric transformation model. A sieve maximum likelihood estimation approach is proposed for parameter estimation. Moreover, as many copula models are available, it is essential to check if the chosen copula model fits the data well for analysis. In the second part of this chapter, we introduce a general goodness-of-fit test procedure for copula-based interval-censored data using the information ratio (IR). It can be applied to any copula family with a parametric form, such as the frequently used Archimedean and Gaussian families. Finally, we present an R package CopulaCenR, which is designed for analyzing multivariate interval-censored data through different copula families.

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Author information

Authors and Affiliations

  1. Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA

    Ying Ding

  2. Center for Applied Statistics and School of Statistics, Renmin University of China, Bei**g, China

    Tao Sun

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  1. Ying Ding
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  2. Tao Sun
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Correspondence to Ying Ding .

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Editors and Affiliations

  1. Department of Statistics, University of Missouri, Columbia, MO, USA

    Jianguo Sun

  2. College of Health Solutions, Arizona State University, Goodyear, AZ, USA

    Ding-Geng Chen

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Ding, Y., Sun, T. (2022). Copula Models and Diagnostics for Multivariate Interval-Censored Data. In: Sun, J., Chen, DG. (eds) Emerging Topics in Modeling Interval-Censored Survival Data. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-031-12366-5_8

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