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Accelerated Hazards Model and Its Extensions for 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

Semiparametric regression analysis of interval-censored data are often performed under popular models, such as the Cox proportional hazards, proportional odds and accelerated failure time models. There are cases in practice that such conventional model assumptions may be inappropriate for modeling survival outcomes of interest. In this chapter, we introduce an alternative, the accelerated hazards model, for the analysis of interval-censored data and its extension to a class of generalized accelerated hazards mixture cure models in the presence of a cure fraction. Inference for these models are obtained using the sieve maximum likelihood method, and the resulting estimators are shown to be consistent and asymptotically normal under mild regularity conditions. The finite sample performance of these models is examined through simulation studies and their practical applications are illustrated by real data examples.

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Acknowledgements

This work was supported in part by the Singapore MOE AcRF Tier 2 grant (MOE-T2EP20121-0004) and Tier 1 grant (RG98/20).

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Authors and Affiliations

  1. School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore

    Liming Xiang

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  1. Liming Xiang
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Correspondence to Liming Xiang .

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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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Xiang, L. (2022). Accelerated Hazards Model and Its Extensions for 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_5

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