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Quantile Regression for Left‐Truncated Semicompeting Risks Data
Author(s) -
Li Ruosha,
Peng Limin
Publication year - 2011
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2010.01521.x
Subject(s) - covariate , quantile regression , computer science , estimator , quantile , consistency (knowledge bases) , event (particle physics) , truncation (statistics) , inference , econometrics , regression , flexibility (engineering) , regression analysis , statistics , mathematics , machine learning , artificial intelligence , physics , quantum mechanics
Summary Semicompeting risks is often encountered in biomedical studies where a terminating event censors a nonterminating event but not vice versa. In practice, left truncation on the terminating event may arise and can greatly complicate the regression analysis on the nonterminating event. In this work, we propose a quantile regression method for left‐truncated semicompeting risks data, which provides meaningful interpretations as well as the flexibility to accommodate varying covariate effects. We develop estimation and inference procedures that can be easily implemented by existing statistical software. Asymptotic properties of the resulting estimators are established including uniform consistency and weak convergence. The finite‐sample performance of the proposed method is evaluated via simulation studies. An application to a registry dataset provides an illustration of our proposals.