@article{Freitas_Fallah_Demétrio_Hinde_2023, title={Overdispersion Models for Clustered Toxicological Data in a Bioassay of Entomopathogenic Fungus}, volume={40}, url={https://biometria.ufla.br/index.php/BBJ/article/view/647}, DOI={10.28951/bjb.v40i4.647}, abstractNote={<p> <span class="fontstyle0">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 </span><span class="fontstyle2">Beauveria bassiana</span><span class="fontstyle0">, 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.</span> </p>}, number={4}, journal={Brazilian Journal of Biometrics}, author={Freitas, Silvia Maria de and Fallah, Lida and Demétrio, Clarice G. B. and Hinde, John P.}, year={2023}, month={Jan.}, pages={490–509} }