Abstract
We propose and compare two approaches for regression analysis of multilevel binary data when clusters are not necessarily nested: a GEE method that relies on a working independence assumption coupled with a three-step method for obtaining empirical standard errors, and a likelihood-based method implemented using Bayesian computational techniques. Implications of time-varying endogenous covariates are addressed. The methods are illustrated using data from the Breast Cancer Surveillance Consortium to estimate mammography accuracy from a repeatedly screened population.
Original language | English (US) |
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Pages (from-to) | 381-398 |
Number of pages | 18 |
Journal | Biostatistics |
Volume | 5 |
Issue number | 3 |
DOIs | |
State | Published - Jul 2004 |
Externally published | Yes |
Keywords
- Conditional
- Endogeneity
- Hierarchical models
- Longitudinal data
- Marginal
- Transition models
ASJC Scopus subject areas
- Medicine(all)
- Statistics and Probability
- Statistics, Probability and Uncertainty