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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Justine Shults & Sarah J. Ratcliffe & Mary Leonard, 2007. "Improved generalized estimating equation analysis via xtqls for quasi-least squares in Stata," Stata Journal, StataCorp LP, vol. 7(2), pages 147-166, June.
    • Handle: RePEc:tsj:stataj:v:7:y:2007:i:2:p:147-166
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    References listed on IDEAS

    as
    1. Brajendra C. Sutradhar & Kalyan Das, 2000. "On the Accuracy of Efficiency of Estimating Equation Approach," Biometrics, The International Biometric Society, vol. 56(2), pages 622-625, June.
    2. Justine Shults & Ardythe L. Morrow, 2002. "Use of Quasi–Least Squares to Adjust for Two Levels of Correlation," Biometrics, The International Biometric Society, vol. 58(3), pages 521-530, September.
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    Cited by:

    1. Mary, Sebastien & Gomez y Paloma, Sergio, 2014. "The Role of Agricultural Growth in Reducing Child Malnutrition," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 168368, Agricultural and Applied Economics Association.
    2. repec:jss:jstsof:25:i14 is not listed on IDEAS
    3. Ratcliffe, Sarah J. & Shults, Justine, 2008. "GEEQBOX: A MATLAB Toolbox for Generalized Estimating Equations and Quasi-Least Squares," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i14).
    4. repec:jss:jstsof:35:i02 is not listed on IDEAS

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