Year: 1999
Type: article
Abstract: Genetic evaluation for the length of productive life (LPL) in livestock species requires specific methods because of following reasons: (i) some animals are still alive at the time of evaluation and their complete LPL is not known, which implies that records of such individuals must be treated as censored; (ii) effects influencing LPL do not act linearly and vary with time; and (iii) the distribution of LPL data is often unknown and extremely skewed.Models based on survival analysis provide appropriate statistical tools for the analysis of LPL.These models are based on a hazard function λ(t) which describes the risk of being culled at time t conditional upon survival to time t.The hazard function of an animal is modeled as a product of a baseline hazard function, λ 0 (t), describing the ageing process, and a vector containing explanatory variables that supposedly influence the culling process and may depend on time.Because the form of λ 0 (t) is not always known with certainty, λ 0 (t) can be left unspecified, as in a Cox model.However, with complex models and large amount of data typical for national genetic evaluation, it is computationally advantageous to assume a parametric, e.g., Weibull hazard function.Such a model can also be easily extended to include a random sire or animal effect and enable estimation of genetic parameters for LPL.Routine genetic evaluation for LPL based on a Weibull model has been implemented in many countries worldwide.This paper gives an example of implementation of survival analysis techniques in dairy populations in Switzerland.
Author: Natascha Vukasinovic
Language: English
Subfield: Genetics
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W1569691493