[ABE-L] Seminários do Programa de Pós Graduação em Estatística/UFRGS – Discrete Survival Models for Biological Pest Control

[Márcia Barbian]

Olá,

Temos o prazer de convidar a todas e todos para mais um seminário do
Programa de Pós-Graduação em Estatística da UFRGS.

Nosso próximo encontro ocorrerá de forma *REMOTA* no *dia 13 de agosto* e
terá como palestrante a pesquisadora Clarice Garcia Borges Demétrio -
Professora da Universidade de São Paulo, do Departamento de Ciências Exatas
da ESALQ/USP  .
*Título:* Discrete Survival Models for Biological Pest Control

*Resumo:* In many experimental settings, event times are recorded at
discrete intervals, e.g. days, and can be viewed as a form of longitudinal
repeated-measurement data with the outcome on each day being death or
survival. Here we consider such data for groups of individuals,
specifically groups of the termite Heterotermes tenuis treated by different
isolates of the fungus Beauveria bassiana with 5 replicates of each
isolate. The aim is to identify the effective isolates, characterised for
example by short LT50s, and also to look for reliable isolates with small
replicate variability. We introduce a family of discrete survival models
that can incorporate both proportional and additive hazards to describe the
mortality history of the groups of termites. These models include an
arbitrary piecewise constant baseline hazard and additional parameters are
used to characterise the efficacy of the different isolates. Random effect
versions of these models are also used to account for the appreciable
isolate and replicate variation. In addition, we make links between ordered
multinomial models (with ordered categories given by days) and the discrete
survival approach using conditional responses over time for the deaths from
the number at risk in each time interval, a form of sequential binomial
model. In the discrete survival models, including time as a factor gives a
baseline discrete hazard function. The ordered multinomial models do not
include this and try to capture this time dependence through terms for the
ordered time, such as a simple trend. Fitting a continuation ratio
sequential binomial model without the time factor and just a simple time
trend gives comparable results to the ordered multinomial. In addition, we
have explored using different link functions, including logit, cloglog, and
clog, for the ordered multinomial models and the discrete survival models,
giving very little difference in the results. Different link functions are
more interesting in the discrete survival models, where they have an
interpretation in terms of the hazards. We can replace the binomial by the
quasi-binomial or beta-binomial, and the estimated dispersions are similar
to those from an ordered Dirichlet multinomial model. The fitted models can
be used to summarise the effectiveness and reliability of the isolates.
Furthermore, by using a non-parametric distribution for the isolate random
effect, a more formal clustering of similar isolates can be obtained. The
models can also be extended to include time-dependent isolate effects,
allowing for a more general classification of the efficacy and virulence of
the isolates. This study was partially financed by the São Paulo Research
Foundation (FAPESP), Brazil. Project Numbers: 2022/11865-1 and 2024/01638-3.
Trabalho em conjunto com: Lida Fallah (TU Dublin, Ireland), Silvia Maria de
Freitas (UFC, CE, Brazil) e John Hinde (University of Galway, Ireland)

*Data: 13/08/2026*

*Horário: 13h30*
*Local: *https://mconf.ufrgs.br/webconf/ppgest

Abraço,

Márcia Barbian

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