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Adjusting for confounding by cluster using generalized linear mixed models

Author

Listed:
  • Brumback, Babette A.
  • Dailey, Amy B.
  • Brumback, Lyndia C.
  • Livingston, Melvin D.
  • He, Zhulin

Abstract

We show how to use generalized linear mixed models to adjust for confounding by cluster of the effect of a within-cluster covariate. We derive estimators for both a cluster-specific causal effect and a population-averaged causal effect.

Suggested Citation

  • Brumback, Babette A. & Dailey, Amy B. & Brumback, Lyndia C. & Livingston, Melvin D. & He, Zhulin, 2010. "Adjusting for confounding by cluster using generalized linear mixed models," Statistics & Probability Letters, Elsevier, vol. 80(21-22), pages 1650-1654, November.
    • Handle: RePEc:eee:stapro:v:80:y:2010:i:21-22:p:1650-1654
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    References listed on IDEAS

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    1. Sylvie Goetgeluk & Stijn Vansteelandt, 2008. "Conditional Generalized Estimating Equations for the Analysis of Clustered and Longitudinal Data," Biometrics, The International Biometric Society, vol. 64(3), pages 772-780, September.
    2. Verbeke G. & Spiessens B. & Lesaffre E., 2001. "Conditional Linear Mixed Models," The American Statistician, American Statistical Association, vol. 55, pages 25-34, February.
    3. John M. Neuhaus & Charles E. McCulloch, 2006. "Separating between‐ and within‐cluster covariate effects by using conditional and partitioning methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(5), pages 859-872, November.
    4. Jesse A. Berlin & Stephen E. Kimmel & Thomas R. Ten Have & Mary D. Sammel, 1999. "An Empirical Comparison of Several Clustered Data Approaches Under Confounding Due to Cluster Effects in the Analysis of Complications of Coronary Angioplasty," Biometrics, The International Biometric Society, vol. 55(2), pages 470-476, June.
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    1. Brumback, Babette A. & He, Zhulin, 2011. "The Mantel-Haenszel estimator adapted for complex survey designs is not dually consistent," Statistics & Probability Letters, Elsevier, vol. 81(9), pages 1465-1470, September.
    2. Andrew Bell & Malcolm Fairbrother & Kelvyn Jones, 2019. "Fixed and random effects models: making an informed choice," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(2), pages 1051-1074, March.
    3. Chenlu Li & Simon C Moore & Jesse Smith & Sarah Bauermeister & John Gallacher, 2019. "The costs of negative affect attributable to alcohol consumption in later life: A within-between random longitudinal econometric model using UK Biobank," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-15, February.
    4. Seonho Shin, 2021. "Were they a shock or an opportunity?: The heterogeneous impacts of the 9/11 attacks on refugees as job seekers—a nonlinear multi-level approach," Empirical Economics, Springer, vol. 61(5), pages 2827-2864, November.
    5. Melanie Prague & Rui Wang & Alisa Stephens & Eric Tchetgen Tchetgen & Victor DeGruttola, 2016. "Accounting for interactions and complex inter‐subject dependency in estimating treatment effect in cluster‐randomized trials with missing outcomes," Biometrics, The International Biometric Society, vol. 72(4), pages 1066-1077, December.
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    7. Anders Skrondal & Sophia Rabe-Hesketh, 2022. "The Role of Conditional Likelihoods in Latent Variable Modeling," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 799-834, September.

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