title: Quantile regression for doubly censored data.
abstract: Double censoring often occurs in registry studies when left censoring is present in addition to right censoring. In this work, we propose a new analysis strategy for such doubly censored data by adopting a quantile regression model. We develop computationally simple estimation and inference procedures by appropriately using the embedded martingale structure. Asymptotic properties, including the uniform consistency and weak convergence, are established for the resulting estimators. Moreover, we propose conditional inference to address the special identifiability issues attached to the double censoring setting. We further show that the proposed method can be readily adapted to handle left truncation. Simulation studies demonstrate good finite-sample performance of the new inferential procedures. The practical utility of our method is illustrated by an analysis of the onset of the most commonly investigated respiratory infection, Pseudomonas aeruginosa, in children with cystic fibrosis through the use of the U.S. Cystic Fibrosis Registry.
authors.affiliation: Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, USA.
authors.forename: Shuang
authors.initials: S
authors.lastname: Ji
authors.forename: Limin
authors.initials: L
authors.lastname: Peng
authors.forename: Yu
authors.initials: Y
authors.lastname: Cheng
authors.forename: HuiChuan
authors.initials: H
authors.lastname: Lai
mesh_terms: D001699:Biometry / Q000379:methods*; D002648:Child; D015897:Comorbidity; D003550:Cystic Fibrosis / Q000453:epidemiology*; D003627:Data Interpretation, Statistical; D006801:Humans; D015995:Prevalence; D016016:Proportional Hazards Models; D011552:Pseudomonas Infections / Q000453:epidemiology*; D012042:Registries / Q000706:statistics & numerical data*; D012044:Regression Analysis; D018570:Risk Assessment; D012307:Risk Factors