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Handling missingness when modeling the force of infection from clustered seroprevalence data

Niel Hens, Christel Faes, Marc Aerts, Ziv Shkedy, Koen Mintiens, Hans Laevens and Frank Boelaert (2007) JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS. 12(4). p.498-513
abstract
Modeling infectious diseases data is a relatively young research area in which clustering and stratification are key features. It is not unlikely for these data to have missing values. If values are missing completely at random, the analysis on the complete cases is valid. However, in practice this assumption is usually not fulfilled. This article shows the effect of ignoring missing data in modeling the force of infection of the bovine herpesvirus-1 in Belgian cattle and proposes the use of weighted generalized estimating equations with constrained fractional polynomials as a flexible modeling tool.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
missing data, weighted generalized estimating equations, clustering, SELECTION, POPULATION, LONGITUDINAL DATA, POLYNOMIALS, BOVINE HERPESVIRUS-1, SMOOTHING PARAMETER-ESTIMATION
journal title
JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS
J. Agric. Biol. Environ. Stat.
volume
12
issue
4
pages
498 - 513
Web of Science type
Article
Web of Science id
000250990100005
JCR category
STATISTICS & PROBABILITY
JCR impact factor
0.946 (2007)
JCR rank
40/91 (2007)
JCR quartile
2 (2007)
ISSN
1085-7117
DOI
10.1198/108571107X250535
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
744261
handle
http://hdl.handle.net/1854/LU-744261
date created
2009-09-09 09:01:29
date last changed
2012-04-23 12:02:51
@article{744261,
  abstract     = {Modeling infectious diseases data is a relatively young research area in which clustering and stratification are key features. It is not unlikely for these data to have missing values. If values are missing completely at random, the analysis on the complete cases is valid. However, in practice this assumption is usually not fulfilled. This article shows the effect of ignoring missing data in modeling the force of infection of the bovine herpesvirus-1 in Belgian cattle and proposes the use of weighted generalized estimating equations with constrained fractional polynomials as a flexible modeling tool.},
  author       = {Hens, Niel and Faes, Christel and Aerts, Marc and Shkedy, Ziv and Mintiens, Koen and Laevens, Hans and Boelaert, Frank},
  issn         = {1085-7117},
  journal      = {JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS},
  keyword      = {missing data,weighted generalized estimating equations,clustering,SELECTION,POPULATION,LONGITUDINAL DATA,POLYNOMIALS,BOVINE HERPESVIRUS-1,SMOOTHING PARAMETER-ESTIMATION},
  language     = {eng},
  number       = {4},
  pages        = {498--513},
  title        = {Handling missingness when modeling the force of infection from clustered seroprevalence data},
  url          = {http://dx.doi.org/10.1198/108571107X250535},
  volume       = {12},
  year         = {2007},
}

Chicago
Hens, Niel, Christel Faes, Marc Aerts, Ziv Shkedy, Koen Mintiens, Hans Laevens, and Frank Boelaert. 2007. “Handling Missingness When Modeling the Force of Infection from Clustered Seroprevalence Data.” Journal of Agricultural Biological and Environmental Statistics 12 (4): 498–513.
APA
Hens, N., Faes, C., Aerts, M., Shkedy, Z., Mintiens, K., Laevens, H., & Boelaert, F. (2007). Handling missingness when modeling the force of infection from clustered seroprevalence data. JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS, 12(4), 498–513.
Vancouver
1.
Hens N, Faes C, Aerts M, Shkedy Z, Mintiens K, Laevens H, et al. Handling missingness when modeling the force of infection from clustered seroprevalence data. JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS. 2007;12(4):498–513.
MLA
Hens, Niel, Christel Faes, Marc Aerts, et al. “Handling Missingness When Modeling the Force of Infection from Clustered Seroprevalence Data.” JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS 12.4 (2007): 498–513. Print.