Quantile regression analysis of censored longitudinal data with irregular outcome-dependent follow-up

• Published in: Biometrics Vol. 72; no. 1; pp. 64 - 73
• Main Authors: Sun, Xiaoyan, Peng, Limin, Manatunga, Amita, Marcus, Michele
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.03.2016; International Biometric Society
• Subjects: Asymptotic properties; at-risk population; BIOMETRIC METHODOLOGY; biometry; Censored quantile regression; Computer Simulation; computer software; Correlation analysis; Data analysis; Data Interpretation, Statistical; Data processing; detection limit; Economic models; Environmental Pollutants - blood; Follow-Up Studies; Humans; Irregular outcome-dependent follow-up; Longitudinal data; Longitudinal Studies; Models, Statistical; Outcome Assessment (Health Care) - methods; Polybrominated biphenyls; Polybrominated Biphenyls - blood; Population studies; Proportional intensity model; Quantiles; Recurrent events; Regression Analysis; Regression models; Reproducibility of Results; Robustness (mathematics); Sample Size; Sensitivity and Specificity; Skewed distributions; Irregular outcome-dependent follow-up; Longitudinal data; Censored quantile regression; Proportional intensity model; Recurrent events
• ISSN: 0006-341X; 1541-0420; 1541-0420
• DOI: 10.1111/biom.12367

In many observational longitudinal studies, the outcome of interest presents a skewed distribution, is subject to censoring due to detection limit or other reasons, and is observed at irregular times that may follow a outcome-dependent pattern. In this work, we consider quantile regression modeling of such longitudinal data, because quantile regression is generally robust in handling skewed and censored outcomes and is flexible to accommodate dynamic covariate-outcome relationships. Specifically, we study a longitudinal quantile regression model that specifies covariate effects on the marginal quantiles of the longitudinal outcome. Such a model is easy to interpret and can accommodate dynamic outcome profile changes over time. We propose estimation and inference procedures that can appropriately account for censoring and irregular outcome-dependent follow-up. Our proposals can be readily implemented based on existing software for quantile regression. We establish the asymptotic properties of the proposed estimator, including uniform consistency and weak convergence. Extensive simulations suggest good finite-sample performance of the new method. We also present an analysis of data from a long-term study of a population exposed to polybrominated biphenyls (PBB), which uncovers an inhomogeneous PBB elimination pattern that would not be detected by traditional longitudinal data analysis.