A test of separate hypotheses for comparing linear mixed models with non nested fixed effects

Academic Article

Abstract

  • © 2017 Taylor & Francis Group, LLC. As researchers increasingly rely on linear mixed models to characterize longitudinal data, there is a need for improved techniques for selecting among this class of models which requires specification of both fixed and random effects via a mean model and variance-covariance structure. The process is further complicated when fixed and/or random effects are non nested between models. This paper explores the development of a hypothesis test to compare non nested linear mixed models based on extensions of the work begun by Sir David Cox. We assess the robustness of this approach for comparing models containing correlated measures of body fat for predicting longitudinal cardiometabolic risk.
  • Authors

    Digital Object Identifier (doi)

    Author List

  • Smith CL; Edwards LJ
  • Start Page

  • 5487
  • End Page

  • 5500
  • Volume

  • 46
  • Issue

  • 11