@article{JSSv035i02,
title={%QLS SAS Macro: A SAS Macro for Analysis of Correlated Data Using Quasi-Least Squares},
volume={35},
url={https://www.jstatsoft.org/index.php/jss/article/view/v035i02},
doi={10.18637/jss.v035.i02},
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 <code>SAS</code> macro called <code>%QLS</code>, and demonstrate application of our macro using a clinical trial example for the comparison of two treatments for a common toenail infection. <code>%QLS</code> 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 <code>Stata</code>, <code>MATLAB</code>, and <code>R</code>). <code>%QLS</code> 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.},
number={2},
journal={Journal of Statistical Software},
author={Kim, Han-Joo and Shults, Justine},
year={2010},
pages={1–22}
}