Using modified approaches on marginal regression analysis of longitudinal data with time-dependent covariates
Related Experiment Video
Updated: May 1, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
Published on: September 17, 2019
Yi Zhou1, John Lefante, Janet Rice
1Department of Biostatistics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, U.S.A.
Abstract:
Quadratic inference functions (QIFs) and estimating equations using the conjugate gradient method (CGM) for fitting marginal models to longitudinal data show appealing features in improving the efficiency without making assumptions on the correlation structure. However, our simulation study shows that both methods produce biased and inefficient estimates of regression parameters when time-dependent covariates are present. In this paper, we extend both the QIF and CGM methods for fitting marginal models to longitudinal data with time-dependent covariates. The idea is to restrict the moment conditions to the ones that are only valid to certain types of time-dependent covariates. Our simulations show that efficiency on estimating regression parameters is achieved using modified approaches. Furthermore, we apply the modified approach to anthropometric screening data to evaluate the association between body mass index and morbidity in children in the Philippines.
Related Concept Videos
Longitudinal Research
Comparing the Survival Analysis of Two or More Groups
Longitudinal Studies
Introduction To Survival Analysis
The primary goal of survival analysis is to estimate survival time—the time...
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Analysis of Population Pharmacokinetic Data

