A comparison between bootstrap methods and generalized estimating equations for correlated outcomes in generalized linear models
Titel:
A comparison between bootstrap methods and generalized estimating equations for correlated outcomes in generalized linear models
Auteur:
Sherman, Michael Cessie, Saskia le
Verschenen in:
Communications in statistics
Paginering:
Jaargang 26 (1997) nr. 3 pagina's 901-925
Jaar:
1997
Inhoud:
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.
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