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

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...

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Bibliographic Details
Published in:Journal of agricultural, biological, and environmental statistics biological, and environmental statistics, 2007-12, Vol.12 (4), p.498-513
Main Authors: Hens, N, Faes, C, Aerts, M, Shkedy, Z, Mintiens, K, Laevens, H, Boelaert, F
Format: Article
Language:English
Subjects:
Agronomy. Soil science and plant productions
animal age
Animal, plant and microbial ecology
Animals
antibodies
Biological and medical sciences
Biometrics, statistics, experimental designs, modeling, agricultural computer applications
Bovine herpesvirus 1
cattle
clustering
Disease models
equations
estimation
Fundamental and applied biological sciences. Psychology
General aspects. Techniques
Generalities. Biometrics, experimentation. Remote sensing
Herds
Infections
Infectious diseases
Methods and techniques (sampling, tagging, trapping, modelling...)
Missing data
missing values
Polynomials
Seroepidemiologic studies
seroprevalence
Standard error
statistical analysis
statistical models
Veterinary medicine
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Summary: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.
ISSN:1085-7117
1537-2693
DOI:10.1198/108571107X250535