The detection of residual serial correlation in linear mixed models.
Diggle (1988) described how the empirical semi-variogram of ordinary least squares residuals can be used to suggest an appropriate serial correlation structure in stationary linear mixed models. In this paper, this approach is extended to non-stationary models which include random effects other than intercepts, and will be applied to prostate cancer data, taken from the Baltimore Longitudinal Study of Aging. A simulation study demonstrates the effectiveness of this extended variogram for improving the covariance structure of the linear mixed model used to describe the prostate data.
Biostatistical Centre for Clinical Trials, Catholic University of Leuven, Belgium.
G
G
Verbeke
E
E
Lesaffre
L J
LJ
Brant
D000704:Analysis of Variance; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies; D008297:Male; D017430:Prostate-Specific Antigen / Q000097:blood; D011470:Prostatic Hyperplasia / Q000453:epidemiology; D011471:Prostatic Neoplasms / Q000175:diagnosis / Q000453:epidemiology; D012984:Software; D013997:Time Factors
D017430:Prostate-Specific Antigen