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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 dis-tribution. 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 recommen-dations for its use as a biological control in the field.

Keywords

Dirichlet-multinomial, Extra-multinomial variation, Generalized estimating equations, Generalized linear models, Grouped data, Random effects models

Publication Date

2022-12-31

Publication Title

Brazilian Journal of Biometrics

Volume

40

Issue

4

First Page

490

Last Page

509

Deposit Date

2026-07-17

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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