Proportional cross-ratio model

Tianle Hu (), Bin Nan () and Xihong Lin ()
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Tianle Hu: Eli Lilly and Company
Bin Nan: University of California - Irvine
Xihong Lin: Harvard School of Public Health

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2019, vol. 25, issue 3, No 5, 480-506

Abstract: Abstract Cross-ratio is an important local measure of the strength of dependence among correlated failure times. If a covariate is available, it may be of scientific interest to understand how the cross-ratio varies with the covariate as well as time components. Motivated by the Tremin study, where the dependence between age at a marker event reflecting early lengthening of menstrual cycles and age at menopause may be affected by age at menarche, we propose a proportional cross-ratio model through a baseline cross-ratio function and a multiplicative covariate effect. Assuming a parametric model for the baseline cross-ratio, we generalize the pseudo-partial likelihood approach of Hu et al. (Biometrika 98:341–354, 2011) to the joint estimation of the baseline cross-ratio and the covariate effect. We show that the proposed parameter estimator is consistent and asymptotically normal. The performance of the proposed technique in finite samples is examined using simulation studies. In addition, the proposed method is applied to the Tremin study for the dependence between age at a marker event and age at menopause adjusting for age at menarche. The method is also applied to the Australian twin data for the estimation of zygosity effect on cross-ratio for age at appendicitis between twin pairs.

Keywords: Bivariate survival; Cross-ratio; Empirical process theory; Local pseudo-partial likelihood; U-process (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10985-018-9451-6

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