A comparison between bootstrap methods and generalized estimating equations for correlated outcomes in generalized linear models
Abstract
We discuss and evaluate bootstrap algorithms for obtaining confidence intervals for parameters in Generalized Linear Models when the data are correlated. The methods are based on a stratified bootstrap and are suited to correlation occurring within “blocks” of data (e.g., individuals within a family, teeth within a mouth, etc.). Application of the intervals to data from a Dutch follow-up study on preterm infants shows the corroborative usefulness of the intervals, while the intervals are seen to be a powerful diagnostic in studying annual measles data. In a simulation study, we compare the coverage rates of the proposed intervals with existing methods (e.g., via Generalized Estimating Equations). In most cases, the bootstrap intervals are seen to perform better than current methods, and are produced in an automatic fashion, so that the user need not know (or have to guess) the dependence structure within a block.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The validity of the 'cluster-robust' multiplier bootstrap procedure relies on the number of clusters being 'large.'
“provided that the number of cluster is 'large.'”
From Difference-in-Differences with multiple time periods (2021) · cites this paper as Sherman and Le Cessie (2007)
Cited by
Sorted by citations
Difference-in-Differences with multiple time periods
Callaway, B., & Sant’Anna, P. H. C. (2021) · Journal of Econometrics · doi:10.1016/j.jeconom.2020.12.001 · Cited by 9,126 · see citing works
Bootstrap-Based Improvements for Inference With Clustered Errors
Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2008) · Review of Economics and Statistics · doi:10.1162/rest.90.3.414 · Cited by 4,417 · see citing works
- Authors
- Sherman, M., & Cessie, S. L.
- Affiliations
- Leiden University (NL)
- Venue
- Communications in Statistics - Simulation and Computation
- Publisher
- Informa UK Limited
- Published
- 1997-01
- Volume
- 26
- Issue
- 3
- Pages
- 901-925
- ISSN
- 0361-0918 (ISSN-L)
- 1532-4141
- OpenAlex
- W2047741946
- Cited by
- 103
- References
- 0
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 2 · 2023: 8 · 2022: 4 · 2021: 5 · 2020: 3 · 2019: 9 · 2018: 8 · 2017: 7 · 2016: 4 · 2015: 7 · 2014: 2 · 2013: 5 · 2012: 6
- FWCI
- 0.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Communications in Statistics - Simulation and Computation (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
- Keywords
- confidence intervals, correlated data, generalized linear models, generalized estimating equations, clustered data, preterm infants, measles incidence, coverage rate, dependence structure
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