Marginal quantile regression for longitudinal data analysis in the presence of time-dependent covariates

I. Chen Chen, Philip M. Westgate

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


When observations are correlated, modeling the within-subject correlation structure using quantile regression for longitudinal data can be difficult unless a working independence structure is utilized. Although this approach ensures consistent estimators of the regression coefficients, it may result in less efficient regression parameter estimation when data are highly correlated. Therefore, several marginal quantile regression methods have been proposed to improve parameter estimation. In a longitudinal study some of the covariates may change their values over time, and the topic of time-dependent covariate has not been explored in the marginal quantile literature. As a result, we propose an approach for marginal quantile regression in the presence of time-dependent covariates, which includes a strategy to select a working type of time-dependency. In this manuscript, we demonstrate that our proposed method has the potential to improve power relative to the independence estimating equations approach due to the reduction of mean squared error.

Original languageEnglish
Pages (from-to)267-282
Number of pages16
JournalInternational Journal of Biostatistics
Issue number2
StatePublished - Nov 1 2021

Bibliographical note

Publisher Copyright:
© 2020 Walter de Gruyter GmbH, Berlin/Boston.


  • empirical covariance matrix
  • mean squared error
  • modified estimating equations
  • quantile regression models
  • time-dependent covariate

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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