Related Experiment Video
Updated: Apr 11, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
Model selection for marginal regression analysis of longitudinal data with missing observations and covariate
1Department of Mathematics, National Chung Cheng University, Chia-Yi 621, Taiwan, R.O.C. cwshen@ccu.edu.tw.
Abstract:
Missing observations and covariate measurement error commonly arise in longitudinal data. However, existing methods for model selection in marginal regression analysis of longitudinal data fail to address the potential bias resulting from these issues. To tackle this problem, we propose a new model selection criterion, the Generalized Longitudinal Information Criterion, which is based on an approximately unbiased estimator for the expected quadratic error of a considered marginal model accounting for both data missingness and covariate measurement error. The simulation results reveal that the proposed method performs quite well in the presence of missing data and covariate measurement error. On the contrary, the naive procedures without taking care of such complexity in data may perform quite poorly. The proposed method is applied to data from the Taiwan Longitudinal Study on Aging to assess the relationship of depression with health and social status in the elderly, accommodating measurement error in the covariate as well as missing observations.
Related Concept Videos
Truncation in Survival Analysis
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
Mechanistic Models: Compartment Models in Individual and Population Analysis
Longitudinal Studies
Assumptions of Survival Analysis
Censoring Survival Data
Friedman Two-way Analysis of Variance by Ranks

