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Analysis of clustered data: A combined estimating equations approach

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  • Julie A. Stoner

Abstract

Examples of clustered data include data from longitudinal studies and data sampled within groups. This paper proposes a regression analysis method for clustered data that optimally weights and combines contrasts of the data through a combination of estimating equations. Examples of combining between-cluster, within-cluster and longitudinal data contrasts are presented. The method results in increased estimation efficiency relative to generalised estimating equations with standard working correlation structures. The proposed method also simplifies modelling decisions regarding the true correlation structure of the data and avoids correlation parameter estimation. Copyright Biometrika Trust 2002, Oxford University Press.

Suggested Citation

  • Julie A. Stoner, 2002. "Analysis of clustered data: A combined estimating equations approach," Biometrika, Biometrika Trust, vol. 89(3), pages 567-578, August.
  • Handle: RePEc:oup:biomet:v:89:y:2002:i:3:p:567-578
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    References listed on IDEAS

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    1. Hong, Han & Tamer, Elie, 2003. "A simple estimator for nonlinear error in variable models," Journal of Econometrics, Elsevier, vol. 117(1), pages 1-19, November.
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    3. Yingyao Hu & Susanne M. Schennach, 2008. "Instrumental Variable Treatment of Nonclassical Measurement Error Models," Econometrica, Econometric Society, vol. 76(1), pages 195-216, January.
    4. Schennach, Susanne M., 2008. "Quantile Regression With Mismeasured Covariates," Econometric Theory, Cambridge University Press, vol. 24(04), pages 1010-1043, August.
    5. Hua Liang & Suojin Wang & Raymond J. Carroll, 2007. "Partially linear models with missing response variables and error-prone covariates," Biometrika, Biometrika Trust, vol. 94(1), pages 185-198.
    6. Purdom Elizabeth & Holmes Susan P, 2005. "Error Distribution for Gene Expression Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-35, July.
    7. Wei, Ying & Carroll, Raymond J., 2009. "Quantile Regression With Measurement Error," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1129-1143.
    8. Delaigle, Aurore & Hall, Peter, 2008. "Using SIMEX for Smoothing-Parameter Choice in Errors-in-Variables Problems," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 280-287, March.
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    Cited by:

    1. Lan Wang & Annie Qu, 2009. "Consistent model selection and data-driven smooth tests for longitudinal data in the estimating equations approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 177-190.
    2. repec:spr:stmapp:v:15:y:2007:i:3:d:10.1007_s10260-006-0031-7 is not listed on IDEAS
    3. Fu, Liya & Wang, You-Gan, 2012. "Quantile regression for longitudinal data with a working correlation model," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2526-2538.
    4. Jaakko Nevalainen & Denis Larocque & Hannu Oja, 2007. "A weighted spatial median for clustered data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 15(3), pages 355-379, February.

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