Analysis of Clustered Binary Data With Unequal Cluster Sizes: A Semiparametric Bayesian Approach |
| |
Authors: | David J Nott Anthony Y C Kuk |
| |
Institution: | (1) Department of Economics, Universidad de Navarra, Campus Universitario, 31080 Pamplona, Spain;; |
| |
Abstract: | The analysis of clustered binary data is a common task in many areas of application. Parametric approaches to the analysis
of such data are numerous, but there has been much recent interest in nonparametric and semiparametric approaches. When cluster
sizes are unequal, an assumption is often made of compatibility of marginal distributions in order for semiparametric approaches
to be developed when there is little replication for different cluster sizes. Here, we use the marginal compatibility assumption
to extend flexible semiparametric Bayesian methods able to shrink towards a “parametric backbone” to the situation where there
are few replicated observations for distinct cluster sizes and each distinct value of a covariate. A motivating application
is the analysis of developmental toxicology data where pregnant laboratory animals are exposed to a dose of some potentially
toxic compound and interest lies in describing the distribution, as a function of the dose level, of the number of fetuses
exhibiting some characteristic abnormality. Flexible semiparametric methods are required here, as the data typically exhibit
overdispersion and complex structure. We also consider a further extension appropriate to the analysis of clustered binary
data in the situation where there is little or no replication for distinct covariate values. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|