401
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Comparing Generalized Estimating Equation and Linear Mixed Effects Model for Estimating Marginal Association with Bivariate Continuous Outcomes

&
Pages 307-316 | Received 25 Oct 2021, Accepted 04 Jul 2022, Published online: 15 Jul 2022

References

  • Ying GS, Maguire MG, Glynn R, Rosner B. Tutorial on biostatistics: statistical analysis for correlated binary eye data. Ophthalmic Epidemiol. 2018;25(1):1–12. doi:10.1080/09286586.2017.1320413.
  • Glynn R, Rosner B. Regression methods when the eye is the unit of analysis. Ophthalmic Epidemiol. 2012;19(3):159–165. doi:10.3109/09286586.2012.674614.
  • Glynn R, Rosner B. Accounting for the correlation between fellow eyes in regression analysis. Arch Ophthalmol. 1992;110(3):381–387. doi:10.1001/archopht.1992.01080150079033.
  • Ying GS, Maguire MG, Glynn R, Rosner B. Tutorial on biostatistics: linear regression analysis of continuous correlated eye data. Ophthalmic Epidemiol. 2017;24(2):130–140. doi:10.1080/09286586.2016.1259636.
  • Zeger SL, Liang KY, Albert PS. Models for longitudinal data: a generalized estimating equation approach. Biometrics. 1988;44(4):1049–1060. doi:10.2307/2531734.
  • Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics. 1982;38(4):963. doi:10.2307/2529876.
  • Campochiaro PA, Iftikhar M, Hafiz G, et al. Oral N-acetylcysteine improves cone function in retinitis pigmentosa patients in phase I trial. J Clin Invest. 2020;130(3):1527–1541. doi:10.1172/JCI132990.
  • Rosner B, Glynn R. Estimation of rank correlation for clustered data. Stat Med. 2017;36(14):2163–2186. doi:10.1002/sim.7257.
  • Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika. 1986;73(1):13–22. doi:10.1093/biomet/73.1.13.
  • SAS Institute Inc. SAS/STAT® 15.1 User’s Guide. Cary, NC: SAS Institute Inc; 2018.
  • Patterson HD, Thompson R. Recovery of inter-block information when block sizes are unequal. Biometrika. 1971;58(3):545–554. doi:10.1093/biomet/58.3.545.
  • Stroup WW. Part II: estimation and Inference Essentials. In: Dominici FJFTZ, ed. Generalized Linear Mixed Models: Modern Concepts, Methods and Applications. Boca Raton, FL: Taylor & Francis Group, LLC; 2013:131–139.
  • Johnson RW. An Introduction to the Bootstrap. Teach Stat. 2001;23:2. doi:10.1111/1467-9639.00050.
  • Imbens GW, Kolesár M. Robust standard errors in small samples: some practical advice. Rev Econ Stat. 2016;98(4):701–712. doi:10.1162/REST_a_00552.
  • Hubbard AE, Ahern J, Fleischer NL, et al. To GEE or Not to GEE: comparing population average and mixed models for estimating the associations between neighborhood risk factors and health. Source Epidemiol 2016;21(4):467–474. Doi:10.1097/EDE.ObOl
  • Paul S, Zhang X. Small sample GEE estimation of regression parameters for longitudinal data. Stat Med. 2014;33(22):3869–3881. doi:10.1002/sim.6198.
  • Auda HA, McKean JW, Kloke JD, Sadek M, Monte A. Carlo study of REML and robust rank-based analyses for the random intercept mixed model. Commun Stat Simul Comput. 2019;48(3):837–860. doi:10.1080/03610918.2017.1400561.
  • Fellingham GW, Raghunathan TE. Sensitivity of point and interval estimates to distributional assumptions in longitudinal data analysis of small samples. Commun Stat - Simul Comput. 1995;24(3):617–630. doi:10.1080/03610919508813263.
  • Pinheiro J, Bates D, DebRoy S, Sarkar D. {R Core Team}. {nlme}: linear and Nonlinear Mixed Effects Models. R Package Version. 2021:3.1–152.
  • Halekoh U, Højsgaard S, Yan J. The R package geepack for generalized estimating equations. J Stat Softw. 2006;15(2):1–11. doi:10.18637/jss.v015.i02.
  • Zorn CJW. Generalized estimating equation models for correlated data: a review with applications. Am J Pol Sci. 2001;45(2):470. doi:10.2307/2669353.
  • Ballinger GA. Using generalized estimating equations for longitudinal data analysis. Organ Res Methods. 2004;7(2):127–150. doi:10.1177/1094428104263672.
  • Society IB. A Caveat Concerning. Independence estimating equations with multivariate binary data. Biometrics. 2010;51(1): 309–317.
  • Huang J, Huang J, Chen Y, Ying GS. Evaluation of approaches to analyzing continuous correlated eye data when sample size is small. Ophthalmic Epidemiol. 2018;25(1):45–54. doi:10.1080/09286586.2017.1339809.
  • Stroup WW. Part I: the Big Picture. In: Dominici FJFTZ, ed. Generalized Linear Mixed Models: Modern Concepts, Methods and Applications. Boca Raton, FL: Taylor & Francis Group, LLC; 2013:65–71.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.