Statistics in Medicine

The detection of residual serial correlation in linear mixed models

Journal Article

  • Author(s): Geert Verbeke, Emmanuel Lesaffre, Larry J. Brant
  • Article first published online: 04 Dec 1998
  • DOI: 10.1002/(SICI)1097-0258(19980630)17:12<1391::AID-SIM851>3.0.CO;2-4
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Abstract

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. © 1998 John Wiley & Sons, Ltd.

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