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- W1987942014 abstract "Summary Sparse clustered data arise in finely stratified genetic and epidemiologic studies and pose at least two challenges to inference. First, it is difficult to model and interpret the full joint probability of dependent discrete data, which limits the utility of full likelihood methods. Second, standard methods for clustered data, such as pairwise likelihood and the generalized estimating function approach, are unsuitable when the data are sparse owing to the presence of many nuisance parameters. We present a composite conditional likelihood for use with sparse clustered data that provides valid inferences about covariate effects on both the marginal response probabilities and the intracluster pairwise association. Our primary focus is on sparse clustered binary data, in which case the method proposed utilizes doubly discordant quadruplets drawn from each stratum to conduct inference about the intracluster pairwise odds ratios." @default.
- W1987942014 created "2016-06-24" @default.
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- W1987942014 date "2003-12-22" @default.
- W1987942014 modified "2023-10-16" @default.
- W1987942014 title "Composite Conditional Likelihood for Sparse Clustered Data" @default.
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- W1987942014 doi "https://doi.org/10.1046/j.1369-7412.2003.05300.x" @default.
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