Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
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Authors: Han-Joo Kim, Justine Shults
Title: %QLS SAS Macro: A SAS Macro for Analysis of Correlated Data Using Quasi-Least Squares
Abstract: Quasi-least squares (QLS) is an alternative computational approach for estimation of the correlation parameter in the framework of generalized estimating equations (GEE). QLS overcomes some limitations of GEE that were discussed in Crowder (1995). In addition, it allows for easier implementation of some correlation structures that are not available for GEE. We describe a user written SAS macro called %QLS, and demonstrate application of our macro using a clinical trial example for the comparison of two treatments for a common toenail infection. %QLS also computes the lower and upper boundaries of the correlation parameter for analysis of longitudinal binary data that were described by Prentice (1988). Furthermore, it displays a warning message if the Prentice constraints are violated. This warning is not provided in existing GEE software packages and other packages that were recently developed for application of QLS (in Stata, MATLAB, and R). %QLS allows for analysis of continuous, binary, or count data with one of the following working correlation structures: the first-order autoregressive, equicorrelated, Markov, or tri-diagonal structures.

Page views:: 4728. Submitted: 2008-08-01. Published: 2010-07-15.
Paper: %QLS SAS Macro: A SAS Macro for Analysis of Correlated Data Using Quasi-Least Squares     Download PDF (Downloads: 4163)
Supplements: SAS source code Download (Downloads: 898; 40KB) SAS example code from the paper Download (Downloads: 727; 876B)
toenail.txt: Example data in space-separated format Download (Downloads: 817; 20KB)
toenail2.txt: Example data in comma-separated format Download (Downloads: 760; 38KB)

DOI: 10.18637/jss.v035.i02

This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.