Power-transformed linear regression on quantile residual life for censored competing risks data

Caiyun Fan, Feipeng Zhang and Yong Zhou

Communications in Statistics - Theory and Methods, 2016, vol. 45, issue 20, 5884-5905

Abstract: This paper proposes a power-transformed linear quantile regression model for the residual lifetime of competing risks data. The proposed model can describe the association between any quantile of a time-to-event distribution among survivors beyond a specific time point and the covariates. Under covariate-dependent censoring, we develop an estimation procedure with two steps, including an unbiased monotone estimating equation for regression parameters and cumulative sum processes for the Box–Cox transformation parameter. The asymptotic properties of the estimators are also derived. We employ an efficient bootstrap method for the estimation of the variance–covariance matrix. The finite-sample performance of the proposed approaches are evaluated through simulation studies and a real example.

Date: 2016
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DOI: 10.1080/03610926.2014.950873

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