Overdispersion Models for Clustered Toxicological Data in a Bioassay of Entomopathogenic Fungus

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Silvia Maria de Feitas
Lida Fallah
Clarice G. B. Demétrio
John P. Hinde

Abstract

 We consider discrete mortality data for groups of individuals observed over time. The fitting of cumulative mortality curves as a function of time involves the longitudinal modelling of the multinomial response. Typically such data exhibit overdispersion, that is greater variation than predicted by the multinomial distribution. To model the extra-multinomial variation (overdispersion) we consider a Dirichlet-multinomial model, a random intercept model and a random intercept and slope model. We construct asymptotic and robust covariance matrix estimators for the regression parameter standard errors. Applying this model to a specific insect bioassay of the fungus Beauveria bassiana, we note some simple relationships in the results and explore why these are simply a consequence of the data structure. Fitted models are used to make inferences on the effectiveness and consistency of different isolates of the fungus to provide recommendations for its use as a biological control in the field.

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How to Cite
Freitas, S. M. de, Fallah, L., Demétrio, C. G. B., & Hinde, J. P. (2023). Overdispersion Models for Clustered Toxicological Data in a Bioassay of Entomopathogenic Fungus. Brazilian Journal of Biometrics, 40(4), 490–509. https://doi.org/10.28951/bjb.v40i4.647
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