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A NEW MULTILEVEL MODELING APPROACH FOR CLUSTERED SURVIVAL DATA

Published online by Cambridge University Press:  03 March 2020

Jinfeng Xu ,
Mu Yue  and
Wenyang Zhang
Jinfeng Xu
Affiliation:
The University of Hong Kong
Mu Yue
Affiliation:
University of Electronic Science and Technology of China
Wenyang Zhang*
Affiliation:
University of York
*
Address correspondence to Wenyang Zhang, Department of Mathematics, University of York, Heslington, York YO10 5DD, UK; email: Email address for correspondence: wenyang.zhang@york.ac.uk
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Abstract

In multilevel modeling of clustered survival data, to account for the differences among different clusters, a commonly used approach is to introduce cluster effects, either random or fixed, into the model. Modeling with random effects may lead to difficulties in the implementation of the estimation procedure for the unknown parameters of interest because the numerical computation of multiple integrals may become unavoidable when the cluster effects are not scalars. On the other hand, if fixed effects are used, there is a danger of having estimators with large variances because there are too many nuisance parameters involved in the model. In this article, using the idea of the homogeneity pursuit, we propose a new multilevel modeling approach for clustered survival data. The proposed modeling approach does not have the potential computational problem as modeling with random effects, and it also involves far fewer unknown parameters than modeling with fixed effects. We also establish asymptotic properties to show the advantages of the proposed model and conduct intensive simulation studies to demonstrate the performance of the proposed method. Finally, the proposed method is applied to analyze a dataset on the second-birth interval in Bangladesh. The most interesting finding is the impact of some important factors on the length of the second-birth interval variation over clusters and its homogeneous structure.

Type
ARTICLES
Information
Econometric Theory , Volume 36 , Issue 4 , August 2020 , pp. 707 - 750
Copyright
© Cambridge University Press 2020

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Footnotes

We are grateful for the vast amount of editorial input by the Editor, Peter C.B. Phillips, on the final version of the manuscript. This research was supported by the National Natural Science Foundation of China (grant number 71873085).

References

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