Quantile Regression Modeling of Latent Trajectory Features with Longitudinal Data

Journal of Applied Statistics
Huijuan MaHaoda Fu

Abstract

Quantile regression has demonstrated promising utility in longitudinal data analysis. Existing work is primarily focused on modeling cross-sectional outcomes, while outcome trajectories often carry more substantive information in practice. In this work, we develop a trajectory quantile regression framework that is designed to robustly and flexibly investigate how latent individual trajectory features are related to observed subject characteristics. The proposed models are built under multilevel modeling with usual parametric assumptions lifted or relaxed. We derive our estimation procedure by novelly transforming the problem at hand to quantile regression with perturbed responses and adapting the bias correction technique for handling covariate measurement errors. We establish desirable asymptotic properties of the proposed estimator, including uniform consistency and weak convergence. Extensive simulation studies confirm the validity of the proposed method as well as its robustness. An application to the DURABLE trial uncovers sensible scientific findings and illustrates the practical value of our proposals.

References

Dec 4, 2003·Statistics in Medicine·Xuming HeWing K Fung
Apr 26, 2006·Biostatistics·Marco Geraci, Matteo Bottai
Mar 23, 2010·Journal of the American Statistical Association·Ying Wei, Raymond J Carroll
Jul 6, 2015·Diabetes Therapy : Research, Treatment and Education of Diabetes and Related Disorders·Haoda FuHaya Ascher-Svanum
Dec 3, 2016·Statistical Methods in Medical Research·Maria Francesca MarinoMarco Alfò
Jul 31, 2019·Computational Statistics & Data Analysis·Marco Geraci
Aug 1, 2019·Journal of the Royal Statistical Society. Series C, Applied Statistics·Marco Geraci

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Citations

Apr 8, 2021·Statistical Methods in Medical Research·Zhiping QiuGregg E Dinse

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Methods Mentioned

BETA
amputation

Software Mentioned

R package quantreg
R package

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