A study of bias in the naive estimator in longitudinal linear mixed-effects models with measurement error and misclassification in covariates

Li, Jia (2014) A study of bias in the naive estimator in longitudinal linear mixed-effects models with measurement error and misclassification in covariates. Masters thesis, Memorial University of Newfoundland.

[English] PDF - Accepted Version Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. Download (6MB)

Abstract

This research presents a generalized least square approach to estimate the pa- rameters in a longitudinal linear mixed-effects model. In this model, we consider measurement error and misclassification in the covariates. Moreover, a classical mea- surement error for continuous covariates, and misclassification for discrete covariates up to three categories, is considered. Through simulation studies, we observe the impact of each parameter of the model on the bias of the naive estimation, when the other parameters stay unchanged.

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