The DOI matches its Crossref record; 80 of the 80 references it cites were found in a registry or an index.
Generalized Estimating Equations in Longitudinal Data Analysis: A Review and Recent Developments
Open accessAbstract
Generalized Estimating Equation (GEE) is a marginal model popularly applied for longitudinal/clustered data analysis in clinical trials or biomedical studies. We provide a systematic review on GEE including basic concepts as well as several recent developments due to practical challenges in real applications. The topics including the selection of “working” correlation structure, sample size and power calculation, and the issue of informative cluster size are covered because these aspects play important roles in GEE utilization and its statistical inference. A brief summary and discussion of potential research interests regarding GEE are provided in the end.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
(2005) · Statistics for Biology and Health · doi:10.1007/0-387-27255-0_9 · Cited by 20,133
Longitudinal data analysis using generalized linear models
LIANG, K.-Y., & ZEGER, S. L. (1986) · Biometrika · doi:10.1093/biomet/73.1.13 · Cited by 18,550
Information Theory and an Extension of the Maximum Likelihood Principle
Akaike, H. (1998) · Springer Series in Statistics · doi:10.1007/978-1-4612-1694-0_15 · Cited by 17,781
Statistical Analysis With Missing Data
Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,265
RUBIN, D. B. (1976) · Biometrika · doi:10.1093/biomet/63.3.581 · Cited by 9,925
(2001) · Statistics for Psychologists · doi:10.4324/9781410605542-11 · Cited by 6,877
Akaike's Information Criterion in Generalized Estimating Equations
Pan, W. (2001) · Biometrics · doi:10.1111/j.0006-341x.2001.00120.x · Cited by 2,493
Fundamentals of Clinical Trials
Friedman, L. M., Furberg, C. D., DeMets, D. L., Reboussin, D. M., & Granger, C. B. (2015) · doi:10.1007/978-3-319-18539-2 · Cited by 2,237
Quasi-likelihood functions, generalized linear models, and the Gauss—Newton method
WEDDERBURN, R. W. M. (1974) · Biometrika · doi:10.1093/biomet/61.3.439 · Cited by 2,122
Generalized Estimating Equations
Hardin, J. W., & Hilbe, J. M. (2002) · doi:10.1201/9781420035285 · Cited by 1,766
Cited by
Amini, M., Zayeri, F., & Salehi, M. (2021) · BMC Public Health · doi:10.1186/s12889-021-10429-0 · Cited by 636
Sun, Y., Li, Y., Wang, J., Chen, Q., Bazzano, A. N., & Cao, F. (2021) · Journal of Medical Internet Research · doi:10.2196/23410 · Cited by 165
“This approach has been recommended because it is able to handle missing data appropriately and remains stable in different correlation matrices [47].”
Backgroundde Melo, M. B., Daldegan‐Bueno, D., Menezes Oliveira, M. G., & de Souza, A. L. (2022) · European Journal of Neuroscience · doi:10.1111/ejn.15858 · Cited by 116
African elephants use plant odours to make foraging decisions across multiple spatial scales
Schmitt, M. H., Shuttleworth, A., Ward, D., & Shrader, A. M. (2018) · Animal Behaviour · doi:10.1016/j.anbehav.2018.04.016 · Cited by 67
Sweet tooth: Elephants detect fruit sugar levels based on scent alone
Nevo, O., Schmitt, M. H., Ayasse, M., & Valenta, K. (2020) · Ecology and Evolution · doi:10.1002/ece3.6777 · Cited by 36
Metadata
- Authors
- Wang, M.
- Venue
- Advances in Statistics
- Publisher
- Wiley
- Published
- 2014-12-01
- Volume
- 2014
- Pages
- 1-11
- ISSN
- 2314-8314 (ISSN-L)
- 2356-6892
- OpenAlex
- W2152127906
- Cited by
- 313
- References
- 80
- FWCI
- 5.16
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Generalized Estimating Equations in Longitudinal Data Analysis: A Review and Recent Developments. https://app.cochat.ai/papers/10.1155/2014/303728
- The paper
- Wang, M. (2014). Generalized Estimating Equations in Longitudinal Data Analysis: A Review and Recent Developments. Advances in Statistics. https://doi.org/10.1155/2014/303728
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
