Handling missingness when modeling the force of infection from clustered seroprevalence data |
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Authors: | Niel Hens Christel Faes Marc Aerts Ziv Shkedy Koen Mintiens Hans Laevens Frank Boelaert |
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Institution: | 1.Center for Statistics,Hasselt University,Diepenbeek,Belgium;2.Veterinary and Agrochemical Research Centre,Brussels,Belgium;3.Faculty of Veterinary Medicine,Ghent University,Ghent,Belgium;4.European Food Safety Authority,Parma,Italy |
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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. |
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