Publication
Title
Handling missingness when modeling the force of infection from clustered seroprevalence data
Author
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.
Language
English
Source (journal)
Journal of agricultural, biological, and environmental statistics / American Statistical Association; International Biometric Society. - Alexandria, Va
Publication
Alexandria, Va : 2007
ISSN
1085-7117
Volume/pages
12:4(2007), p. 498-513
ISI
000250990100005
Full text (Publishers DOI)
UAntwerpen
Faculty/Department
Publication type
Subject
External links
Web of Science
Record
Identification
Creation 26.07.2011
Last edited 22.04.2017