Journal ArticleDOI
A covariance estimator for GEE with improved small-sample properties.
Lloyd Mancl,Timothy A. DeRouen +1 more
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TLDR
This paper proposes a bias-corrected covariance estimator for generalized estimating equations (GEE) that gives tests with sizes close to the nominal level even when the number of subjects was 10 and cluster sizes were unequal, whereas the robust and jackknife covariances estimators gave Tests with sizes that could be 2-3 times the nominallevel.Abstract:
Summary. In this paper, we propose an alternative covariance estimator to the robust covariance estimator of generalized estimating equations (GEE). Hypothesis tests using the robust covariance estimator can have inflated size when the number of independent clusters is small. Resampling methods, such as the jackknife and bootstrap, have been suggested for covariance estimation when the number of clusters is small. A drawback of the resampling methods when the response is binary is that the methods can break down when the number of subjects is small due to zero or near-zero cell counts caused by resampling. We propose a bias-corrected covariance estimator that avoids this problem. In a small simulation study, we compare the bias-corrected covariance estimator to the robust and jackknife covariance estimators for binary responses for situations involving 10–40 subjects with equal and unequal cluster sizes of 16–64 observations. The bias-corrected covariance estimator gave tests with sizes close to the nominal level even when the number of subjects was 10 and cluster sizes were unequal, whereas the robust and jackknife covariance estimators gave tests with sizes that could be 2–3 times the nominal level. The methods are illustrated using data from a randomized clinical trial on treatment for bone loss in subjects with periodontal disease.read more
Citations
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Bootstrap-Based Improvements for Inference with Clustered Errors
TL;DR: In this article, the authors investigate inference using cluster bootstrap-t procedures that provide asymptotic refinement, including the example of Bertrand, Duflo, and Mullainathan.
Journal ArticleDOI
Bootstrap-Based Improvements for Inference with Clustered Errors
TL;DR: In this paper, the authors investigate more accurate inference using cluster bootstrap-t procedures that provide asymptotic refinement for regression parameter estimates, and evaluate these procedures using Monte Carlos, including the much-cited di ¤erences-in-di¤erences example of Bertrand, Mullainathan and Duflo.
Journal ArticleDOI
Design and analysis of group-randomized trials: a review of recent methodological developments.
TL;DR: Developments in estimates of intraclass correlation, power analysis, matched designs, designs involving one group per condition, and designs in which individuals are randomized to receive treatments in groups are reviewed.
Journal ArticleDOI
Extension of the modified Poisson regression model to prospective studies with correlated binary data
Guangyong Zou,Allan Donner +1 more
TL;DR: The Poisson regression model using a sandwich variance estimator has become a viable alternative to the logistic regression model for the analysis of prospective studies with independent binary outcomes and is extended to studies with correlated binary outcomes as arise in longitudinal or cluster randomization studies.
Journal ArticleDOI
On the unnecessary ubiquity of hierarchical linear modeling.
TL;DR: This article compares and contrasts HLM with alternative methods including generalized estimating equations and cluster-robust standard errors and demonstrates the advantages of the alternative methods and also when HLM would be the preferred method.
References
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Journal ArticleDOI
A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
TL;DR: In this article, a parameter covariance matrix estimator which is consistent even when the disturbances of a linear regression model are heteroskedastic is presented, which does not depend on a formal model of the structure of the heteroSkewedness.
Journal ArticleDOI
Longitudinal data analysis using generalized linear models
Kung Yee Liang,Scott L. Zeger +1 more
TL;DR: In this article, an extension of generalized linear models to the analysis of longitudinal data is proposed, which gives consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence.
Book
The jackknife, the bootstrap, and other resampling plans
TL;DR: The Delta Method and the Influence Function Cross-Validation, Jackknife and Bootstrap Balanced Repeated Replication (half-sampling) Random Subsampling Nonparametric Confidence Intervals as mentioned in this paper.
Journal ArticleDOI
Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis
TL;DR: In this paper, a class of weighted jackknife variance estimators for the least square estimator by deleting any fixed number of observations at a time was proposed, and three bootstrap methods were considered.
Journal ArticleDOI
Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties☆
James G. MacKinnon,Halbert White +1 more
TL;DR: In this article, the authors examined several modified versions of the heteroskedasticity-consistent covariance matrix estimator of Hinkley (1977) and White (1980) and found that one estimator, based on the jackknife, performs better in small samples than the rest.
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