Random effects ordinal time models for grouped toxicological data from a biological control assay

Marie-José Martinez 1 John Hinde 2
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : Discrete survival times can be viewed as ordered multicategorical data. Here a continuation-ratio model is considered, which is particularly appropriate when the ordered categories represent a progression through different stages, such as survival through various times. This particular model has the advantage of being a simple decomposition of a multinomial distribution into a succession of hierarchical binomial models. In a clustered data context, random effects are incorporated into the linear predictor of the model to account for uncontrolled experimental variation. Assuming a normal distribution for the random effects, an EM algorithm with adaptive Gaussian quadrature is used to estimate the model parameters. This approach is applied to the analysis of grouped toxicological data obtained from a biological control assay. In this assay, different isolates of the fungus Beauveria bassiana were used as a microbial control for the Heterotermes tenuis termite, which causes considerable damage to sugarcane fields in Brazil. The aim is to study the pathogenicity and the virulence of the fungus in order to determine effective isolates for the control of this pest population.
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Chapitre d'ouvrage
Gilbert MacKenzie and Defen Peng. Statistical Modelling in Biostatistics and Bioinformatics: Selected papers, Springer, pp.45-58, 2014, Contributions to Statistics, 978-3-319-04578-8. 〈10.1007/978-3-319-04579-5_5〉
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https://hal.archives-ouvertes.fr/hal-00943962
Contributeur : Marie-José Martinez <>
Soumis le : dimanche 9 février 2014 - 22:54:22
Dernière modification le : jeudi 5 juin 2014 - 17:47:07

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Marie-José Martinez, John Hinde. Random effects ordinal time models for grouped toxicological data from a biological control assay. Gilbert MacKenzie and Defen Peng. Statistical Modelling in Biostatistics and Bioinformatics: Selected papers, Springer, pp.45-58, 2014, Contributions to Statistics, 978-3-319-04578-8. 〈10.1007/978-3-319-04579-5_5〉. 〈hal-00943962〉

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