Laplace regression with clustered censored data

Akram Yazdani, Hojjat Zeraati (), Mehdi Yaseri, Shahpar Haghighat and Ahmad Kaviani
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Akram Yazdani: Kashan University of Medical Sciences
Hojjat Zeraati: Tehran University of Medical Sciences
Mehdi Yaseri: Tehran University of Medical Sciences
Shahpar Haghighat: Motamed Cancer Institute, ACECR
Ahmad Kaviani: Tehran University of Medical Sciences

Computational Statistics, 2022, vol. 37, issue 3, No 3, 1068 pages

Abstract: Abstract In survival analysis, data may be correlated or clustered, because of some features such as shared genes and environmental background. A common approach to accommodate clustered data is the Cox frailty model that has proportional hazard assumption and complexity of interpreting hazard ratio lead to the misinterpretation of a direct effect on the time of event. In this paper, we considered Laplace quantile regression model for clustered survival data that interpret the effect of covariates on the time to event. A Bayesian approach with Markov Chain Monte Carlo method was used to fit the model. The results from a simulation study to evaluate the performance of proposed model showed that the Laplace regression model with frailty term performed well for different scenarios and the coverage rates of the pointwise 95% CIs were close to the nominal level (0.95). An application to data from breast cancer was presented to illustrate the theory and method developed in this paper.

Keywords: Accelerated failure time model; Asymmetric Laplace distribution; Clustered survival data; Quantile regression (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s00180-021-01151-x

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