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Longevity evaluation of cattle Curraleiro Pé-Duro breed using the inverse Gaussian frailty model


Biometrics & Biostatistics International Journal
KMM S Soares, VLD Tomazella, SCS Júnior, GMC Carvalho, CMM Lima, KR Santos

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Abstract

Cattle of the Curraleiro Pé-Duro cattle breed have a great ability to adapt to extreme climatic conditions in a natural environment, providing the minimization of losses in productive and reproductive performance, helping in disease resistance, consequently reducing mortality and increasing longevity when exposed to stressful conditions. The research aimed to use of the survival analysis technique, analyzing the effects of unobserved factors, in a study on the stay ability in the herd of cattle Curraleiro Pé-Duro breed using the model of inverse Gaussian fragility. Data from records of 102 cattle born between 2005 and 2014 in an experimental herd of Embrapa Meio-Norte located in São João do Piauí, in the semiarid region of Piauí, Brazil, were observed. The failure was considered to be the inactivity of the cattle caused by the death or sale and the censorship as the animal remaining active in the herd. The methodology of inverse Gaussian frailty models with log normal basis risk was used. Were considered significant (P < 0.05) the covariates season of birth, sex, weight at 365 days and the interaction weight at 365 days with sex. There was a predominance of birth in the dry season (July to December). It was observed that the cattle that more remained in the herd were born in the dry season and were male. The use of fragility models proved to be efficient to meet the proposed objectives, pointing out the potential of its use to contemplate unobserved heterogeneity, being a great tool for animal breeding. Frailty models allowed incorporating a term for the unobserved heterogeneity that affects the estimation of risk influencing longevity of the bovine. Thus, so these models can be used as a tool to help in animal improvement.  

Keywords

Survival, univariate frailty, censorship, failure, Gaussian inverse

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