American Statistical Association
Quasi-least squares (QLS) is a computational approach for estimation of the correlation parameters that is in the framework of generalized estimating equations (GEE). This talk will give an overview of QLS that includes: why QLS is useful; how we specify a QLS model; a listing of software (and related papers) that we have developed for implementation of QLS in Stata, R, Matlab, and SAS; details regarding the QLS estimation procedure; and finally, some brief discussion of our current research in this area that concerns methods for selection of working correlation structures for GEE/QLS analysis of correlated binary data. The unifying theme of this presentation is that QLS, like GEE, is a relatively simple and straightforward approach that as such, can be very useful with respect to both methods research and statistical consultations.
Dr. Justine Shults is an Associate Professor in the Department of Biostatistics and Epidemiology in the University of Pennsylvania School of Medicine. She is also Co-Director of the Section of Biostatistics in the Department of Pediatrics at the Children’s Hospital of Philadelphia. She was the principal investigator of the completed NIH funded R01 study “Longitudinal Analysis for Diverse Populations” that had as its goal the development of improved methods for analysis of diverse populations. She is currently the principal investigator of the NIDDK funded Renal & Urologic Biostatistics Training Grant in the Department of Biostatistics and Epidemiology at the University of Pennsylvania. Dr. Shults works on the development of improved methods for the analysis of longitudinal data. She participates as a co-investigator on NIH funded projects in Psychiatry, including studies of Bipolar II disorder and alternative therapies (e.g. Chamomile) for the treatment of anxiety.
|Date:||Tuesday, April 5, 2011|
|Time:||3:30 - 4:30 P.M.|
New York State Psychiatric Institute
1051 Riverside Drive
6th Floor Multipurpose Room (6602)
New York, New York