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GEEMAEE: A SAS macro for the analysis of correlated outcomes based on GEE and finite-sample adjustments with application to cluster randomized trials.

Publication ,  Journal Article
Zhang, Y; Preisser, JS; Li, F; Turner, EL; Toles, M; Rathouz, PJ
Published in: Comput Methods Programs Biomed
March 2023

BACKGROUND AND OBJECTIVES: Generalized estimating equations (GEE) are used to analyze correlated outcomes in marginal regression models with population-averaged interpretations of exposure effects. Limitations of popular software for GEE include: (i) user choice is restricted to a small set of within-cluster pairwise correlation (intra-class correlation; ICC) structures; and (ii) inference on ICC parameters is usually not possible because the precision of their estimates is not quantified. This is important because ICC values inform the design of cluster randomized trials. Beyond the standard GEE implementation, use of paired estimating equations (Prentice 1988) provides: (i) flexible specification of models for pairwise correlations and (ii) standard errors for ICC estimates. However, most GEEs give biased estimates of standard errors and correlations when the number of clusters is small (roughly, ≤40). Consequently, there is a need for software to provide GEE analysis with finite-sample bias-corrections. METHODS: The SAS macro GEEMAEE implements paired estimating equations to simultaneously estimate parameters in marginal mean and ICC models. It provides bias-corrected standard errors and uses matrix-adjusted estimating equations (MAEE) for bias-corrected estimation of correlations. Several built-in correlation matrix options, rarely found in software, are offered for multi-period, cluster randomized trials and similarly structured longitudinal observational data structures. Additional options include user-specified correlation structures and deletion diagnostics, namely Cooks' Distance and DBETA statistics that estimate the influence of observations, cluster-periods (when applicable) and clusters. RESULTS: GEEMAEE is illustrated for a binary and a count outcome in two stepped wedge cluster randomized trials and a binary outcome in a longitudinal study of disease surveillance. Use of MAEE resulted in larger values of correlation estimates compared to uncorrected estimating equations. Use of bias-corrected variance estimators resulted in (appropriately) larger values of standard errors compared to the usual sandwich estimators. Deletion diagnostics identified the clusters and cluster-periods having the most influence. CONCLUSIONS: The SAS macro GEEMAEE provides regression analysis for clustered or longitudinal responses, and is particularly useful when the number of clusters is small. Flexible specification and bias-corrected estimation of pairwise correlation parameters and standard errors are key features of the software to provide valid inference in real-world settings.

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Published In

Comput Methods Programs Biomed

DOI

EISSN

1872-7565

Publication Date

March 2023

Volume

230

Start / End Page

107362

Location

Ireland

Related Subject Headings

  • Randomized Controlled Trials as Topic
  • Models, Statistical
  • Medical Informatics
  • Longitudinal Studies
  • Computer Simulation
  • Cluster Analysis
  • 4603 Computer vision and multimedia computation
  • 4601 Applied computing
  • 4003 Biomedical engineering
  • 0906 Electrical and Electronic Engineering
 

Citation

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Zhang, Y., Preisser, J. S., Li, F., Turner, E. L., Toles, M., & Rathouz, P. J. (2023). GEEMAEE: A SAS macro for the analysis of correlated outcomes based on GEE and finite-sample adjustments with application to cluster randomized trials. Comput Methods Programs Biomed, 230, 107362. https://doi.org/10.1016/j.cmpb.2023.107362
Zhang, Ying, John S. Preisser, Fan Li, Elizabeth L. Turner, Mark Toles, and Paul J. Rathouz. “GEEMAEE: A SAS macro for the analysis of correlated outcomes based on GEE and finite-sample adjustments with application to cluster randomized trials.Comput Methods Programs Biomed 230 (March 2023): 107362. https://doi.org/10.1016/j.cmpb.2023.107362.
Zhang Y, Preisser JS, Li F, Turner EL, Toles M, Rathouz PJ. GEEMAEE: A SAS macro for the analysis of correlated outcomes based on GEE and finite-sample adjustments with application to cluster randomized trials. Comput Methods Programs Biomed. 2023 Mar;230:107362.
Zhang, Ying, et al. “GEEMAEE: A SAS macro for the analysis of correlated outcomes based on GEE and finite-sample adjustments with application to cluster randomized trials.Comput Methods Programs Biomed, vol. 230, Mar. 2023, p. 107362. Pubmed, doi:10.1016/j.cmpb.2023.107362.
Zhang Y, Preisser JS, Li F, Turner EL, Toles M, Rathouz PJ. GEEMAEE: A SAS macro for the analysis of correlated outcomes based on GEE and finite-sample adjustments with application to cluster randomized trials. Comput Methods Programs Biomed. 2023 Mar;230:107362.
Journal cover image

Published In

Comput Methods Programs Biomed

DOI

EISSN

1872-7565

Publication Date

March 2023

Volume

230

Start / End Page

107362

Location

Ireland

Related Subject Headings

  • Randomized Controlled Trials as Topic
  • Models, Statistical
  • Medical Informatics
  • Longitudinal Studies
  • Computer Simulation
  • Cluster Analysis
  • 4603 Computer vision and multimedia computation
  • 4601 Applied computing
  • 4003 Biomedical engineering
  • 0906 Electrical and Electronic Engineering