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) |
|---|---|
| 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
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Cite this
Miglioretti, D. L., & Heagerty, P. J. (2004). Marginal modeling of multilevel binary data with time-varying covariates. Biostatistics, 5(3), 381-398. https://doi.org/10.1093/biostatistics/kxg042