Marginal modeling of multilevel binary data with time-varying covariates

Diana L Miglioretti, Patrick J. Heagerty

Research output: Contribution to journalArticle

46 Citations (Scopus)

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 languageEnglish (US)
Pages (from-to)381-398
Number of pages18
JournalBiostatistics
Volume5
Issue number3
DOIs
StatePublished - Jul 2004
Externally publishedYes

Fingerprint

Time-varying Covariates
Binary Data
Modeling
Three-step Methods
Mammography
Computational Techniques
Standard error
Breast Cancer
Regression Analysis
Surveillance
Covariates
Likelihood
Time-varying
Breast Neoplasms
Estimate
Time-varying covariates
Population

Keywords

  • Conditional
  • Endogeneity
  • Hierarchical models
  • Longitudinal data
  • Marginal
  • Transition models

ASJC Scopus subject areas

  • Medicine(all)
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Marginal modeling of multilevel binary data with time-varying covariates. / Miglioretti, Diana L; Heagerty, Patrick J.

In: Biostatistics, Vol. 5, No. 3, 07.2004, p. 381-398.

Research output: Contribution to journalArticle

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