Improved generalized estimating equation analysis via xtqls for quasi-least squares in Stata

Justine Shults (), Sarah J. Ratcliffe and Mary Leonard
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Justine Shults: Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine
Sarah J. Ratcliffe: Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine
Mary Leonard: Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine

Stata Journal, 2007, vol. 7, issue 2, 147-166

Abstract: Abstract. Quasi-least squares (QLS) is an alternative method for estimating the correlation parameters within the framework of the generalized estimating equation (GEE) approach for analyzing correlated cross-sectional and longitudinal data. This article summarizes the development of QLS that occurred in several reports and describes its use with the user-written program xtqls in Stata. Also, it demonstrates the following advantages of QLS: (1) QLS allows some correlation structures that have not yet been implemented in the framework of GEE, (2) QLS can be applied as an alternative to GEE if the GEE estimate is infeasible, and (3) QLS uses the same estimating equation for estimation of beta as GEE; as a result, QLS can involve programs already available for GEE. In particular, xtqls calls the Stata program xtgee within an iterative approach that alternates between updating estimates of the correlation parameter alpha and then using xtgee to solve the GEE for beta at the current estimate of alpha. The benefit of this approach is that after xtqls, all the usual postregression estimation commands are readily available to the user. Copyright 2007 by StataCorp LP.

Keywords: xtqls; correlated data; clustered data; longitudinal data; generalized estimating equations; quasi-least squares (search for similar items in EconPapers)
Date: 2007
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Citations: View citations in EconPapers (3)

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