Semiparametric regression based on quadratic inference function for multivariate failure time data with auxiliary information

Feifei Yan, Lin Zhu, Yanyan Liu (), Jianwen Cai and Haibo Zhou
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Feifei Yan: Huazhong University of Science and Technology
Lin Zhu: East China University of Technology
Yanyan Liu: Wuhan University
Jianwen Cai: University of North Carolina at Chapel Hill
Haibo Zhou: University of North Carolina at Chapel Hill

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2021, vol. 27, issue 2, No 4, 269-299

Abstract: Abstract This paper deals with statistical inference procedure of multivariate failure time data when the primary covariate can be measured only on a subset of the full cohort but the auxiliary information is available. To improve efficiency of statistical inference, we use quadratic inference function approach to incorporate the intra-cluster correlation and use kernel smoothing technique to further utilize the auxiliary information. The proposed method is shown to be more efficient than those ignoring the intra-cluster correlation and auxiliary information and is easy to implement. In addition, we develop a chi-squared test for hypothesis testing of hazard ratio parameters. We evaluate the finite-sample performance of the proposed procedure via extensive simulation studies. The proposed approach is illustrated by analysis of a real data set from the study of left ventricular dysfunction.

Keywords: Multivariate failure time data; Validation sample; Quadratic inference function; Chi-squared test (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10985-020-09513-1

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