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Bayesian modeling of incidence and progression of disease from cross-sectional data.

Publication ,  Journal Article
Dunson, B; Baird, DD
Published in: Biometrics
December 2002

In the absence of longitudinal data, the current presence and severity of disease can be measured for a sample of individuals to investigate factors related to disease incidence and progression. In this article, Bayesian discrete-time stochastic models are developed for inference from cross-sectional data consisting of the age at first diagnosis, the current presence of disease, and one or more surrogates of disease severity. Semiparametric models are used for the age-specific hazards of onset and diagnosis, and a normal underlying variable approach is proposed for modeling of changes with latency time in disease severity. The model accommodates multiple surrogates of disease severity having different measurement scales and heterogeneity among individuals in disease progression. A Markov chain Monte Carlo algorithm is described for posterior computation, and the methods are applied to data from a study of uterine leiomyoma.

Duke Scholars

Published In

Biometrics

DOI

EISSN

1541-0420

ISSN

0006-341X

Publication Date

December 2002

Volume

58

Issue

4

Start / End Page

813 / 822

Related Subject Headings

  • White People
  • Uterine Neoplasms
  • Statistics & Probability
  • Monte Carlo Method
  • Markov Chains
  • Leiomyoma
  • Incidence
  • Humans
  • Female
  • Disease Progression
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Dunson, B., & Baird, D. D. (2002). Bayesian modeling of incidence and progression of disease from cross-sectional data. Biometrics, 58(4), 813–822. https://doi.org/10.1111/j.0006-341x.2002.00813.x
Dunson, B., and Donna D. Baird. “Bayesian modeling of incidence and progression of disease from cross-sectional data.Biometrics 58, no. 4 (December 2002): 813–22. https://doi.org/10.1111/j.0006-341x.2002.00813.x.
Dunson B, Baird DD. Bayesian modeling of incidence and progression of disease from cross-sectional data. Biometrics. 2002 Dec;58(4):813–22.
Dunson, B., and Donna D. Baird. “Bayesian modeling of incidence and progression of disease from cross-sectional data.Biometrics, vol. 58, no. 4, Dec. 2002, pp. 813–22. Epmc, doi:10.1111/j.0006-341x.2002.00813.x.
Dunson B, Baird DD. Bayesian modeling of incidence and progression of disease from cross-sectional data. Biometrics. 2002 Dec;58(4):813–822.
Journal cover image

Published In

Biometrics

DOI

EISSN

1541-0420

ISSN

0006-341X

Publication Date

December 2002

Volume

58

Issue

4

Start / End Page

813 / 822

Related Subject Headings

  • White People
  • Uterine Neoplasms
  • Statistics & Probability
  • Monte Carlo Method
  • Markov Chains
  • Leiomyoma
  • Incidence
  • Humans
  • Female
  • Disease Progression