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SwE - Sandwich Estimator Toolbox for Longitudinal & Repeated Measures Data

The SwE toolbox for longitudinal and repeated measures neuroimaging data uses a marginal model with the sandwich estimator of standard errors. It is implemented as a Matlab toolbox for SPM8 or SPM12. Please contact Bryan Guillaume & Tom Nichols with any questions or issues.

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Documentation

What's new?

  • As of September 2015, better small sample adjustments have been added as well as the Wild Bootstrap to make non-parametric inferences with the Sandwich Estimator.
  • (A paper covering these new developments is in preparations; please see Dr. Guillaume's thesis for full details.)

Why use SwE?

This approach has a number of advantages over traditional linear mixed effect models:
  • Easy random effects
    Only the population model is specified, meaning that no random effects (e.g. random slopes) need to be specified.
    • Despite having no explicit specification, all possible random effects are accounted for through the use of an unstructured error covariance.
    • For moderate sample sizes, the "Hom"ogeneous sandwich estimator assumes each subject in a group shares the same visit-based covariance strucutre.
    • For large sample sizes, the "Het"ergoeneous (tranditional) sandwhich estimator doesn't even assume common covariance over subjects.
    • When comparing multiple groups, each group automatically has its own covariance structure.
  • No convergence problems
    The population model is estimated with Ordinary Least Squares, meaning that the method is non-iterative and thus is immune to convergence failures not uncommon in complex mixed effects models.
  • Built for neuroimaging
    • Toolbox for widely used SPM software
    • While the traditional sandwich estimator techniques often assume large samples, SwE implements carefully evaluated (and in some cases novel) degrees of freedom estimator and small sample adjustments.
    • Familywise error-corrected voxel and cluster inferences are available with the Wild Boostrap, avoiding any parametric (e.g. random field theory) distributional assumptions.

Bugs & Feedback

Please be sure to report any problems or questions to Bryan Guillaume & Tom Nichols.