Linear Mixed-Effects Models for Preliminary Analysis of Flexible Pavement Serviceability Index Data

Abstract

Multilevel data are common in many fields. Because of the hierarchical data structure, multilevel data are often analyzed with linear mixed-effects (LME) models. Exploratory analysis, statistical modeling, and examination of the model fit of LME models are more complex than those of standard multiple regressions. A systematic modeling approach that uses visual-graphical techniques and LME models is proposed and demonstrated with the original AASHO Road Test flexible pavement serviceability index data. The proposed approach includes exploring the growth patterns at both group and individual levels, identifying the important predictors and unusual subjects, choosing suitable statistical models, selecting a preliminary mean structure, selecting a random structure, selecting a residual covariance structure, model reduction, and examination of the model fit.