Identifying Gene Regulatory Events using the Coefficient of Intrinsic Dependence

Abstract

In this paper, we investigate the quantile regression analysis for semi-competing risks data in which a non-terminal event may be dependently censored by a terminal event. The estimation of quatile regression parameters for the non-terminal event becomes more complicated since the non-terminal event is dependently censored by the terminal event. We can not make inference on the non-terminal event without extra assumptions. Thus, we handle this problem by assuming that the joint distribution of the terminal event and the non-terminal event follows a parametric copula model with unspecified marginal distributions. We use the stochastic property of the martingale method to estimate the quantile regression parameters under semi-competing risks data. The martingale method for quantile regression is also considered by Peng and Huang (2008) under right censoring data. We also prove the large sample properties of the proposed estimator, and introduce a model diagnostic approach to check model adequacy. From simulation results, it shows that the proposed estimator performs well. For illustration, we apply our proposed approach to analyze a real data.