Model selection techniques for the covariance matrix for incomplete longitudinal data.

Abstract:

:In longitudinal studies with incomplete data, where the number of time points can become numerous, it is often advantageous to model the covariance matrix. We describe several covariance models (for example, mixed models, compound symmetry, AR(1)-type models, and combination models) that offer parsimonious alternatives to unstructured sigma. We evaluate each covariance model with longitudinal data concerning cholesterol as the repeated outcome measure. We discuss strategies for deciding the 'best' model and show a graphical technique for judging goodness-of-fit of covariance models.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Grady JJ,Helms RW

doi

10.1002/sim.4780141302

subject

Has Abstract

pub_date

1995-07-15 00:00:00

pages

1397-416

issue

13

eissn

0277-6715

issn

1097-0258

journal_volume

14

pub_type

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