Survival analysis methodology

Cow longevity is highly related to dairy farm profit. Cows are culled for a variety of reasons. The predominant reasons for culling are reproduction (i.e., failure to conceive), health, and low production (Bascom and Young, 1998). Half of the herd removals occur involuntarily and prematurely because of health disorders (Beaudeau et al., 2000). The decision to cull is a complex one. Farmers may consider many individual (such as age, stage of lactation, milk production, health status, and reproductive performance) and economic (such as milk price, the price of culled cows, and the price and availability of replacement heifers) factors when deciding to cull a cow. On the other hand, the risk of culling is not consistent across all stages of lactation. Cows experience the highest risk shortly after calving (Fetrow et al., 2006). Survival analysis allows for a more appropriate management of censored data and time-dependent covariates. Analyses of the reason and timing of culling is needed to predict herd performance. The objective of this study was to study the reasons and timing of cows leaving herd in two large Holstein dairy farms in Iran.

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Assessment of the Impact of Somatic Cell Count on Functional Longevity in Holstein and Jersey Cattle Using Survival Analysis Methodology

Journal of Dairy Science ◽

10.3168/jds.s0022-0302(05)72745-4 ◽

2005 ◽

Vol 88 (2) ◽

pp. 804-811 ◽

Cited By ~ 32

Author(s):

D.Z. Caraviello ◽

K.A. Weigel ◽

G.E. Shook ◽

P.L. Ruegg

Keyword(s):

Survival Analysis ◽

Somatic Cell ◽

Cell Count ◽

Somatic Cell Count ◽

Jersey Cattle ◽

Analysis Methodology ◽

The Impact

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Survival Analysis Methodology on Ophthalmic Area

Journal of Retina ◽

10.21561/jor.2018.3.1.1 ◽

2018 ◽

Vol 3 (1) ◽

pp. 1-12

Author(s):

Eunjeong Ji ◽

Sang Jun Park ◽

Soyeon Ahn ◽

Minjung Lee

Keyword(s):

Survival Analysis ◽

Analysis Methodology

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Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data

Bioinformatics ◽

10.1093/bioinformatics/bts306 ◽

2012 ◽

Vol 28 (15) ◽

pp. 1998-2003 ◽

Cited By ~ 8

Author(s):

C. D. Tekwe ◽

R. J. Carroll ◽

A. R. Dabney

Keyword(s):

Survival Analysis ◽

Quantitative Analysis ◽

Proteomics Data ◽

Analysis Methodology

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Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data

2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS) ◽

10.1109/gensips.2011.6169453 ◽

2011 ◽

Author(s):

Carmen D. Tekwe ◽

Alan R. Dabney ◽

Raymond J. Carroll

Keyword(s):

Survival Analysis ◽

Quantitative Analysis ◽

Proteomics Data ◽

Analysis Methodology

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Genetic evaluation of productive life in Iranian Holstein using survival analysis

Proceedings of the British Society of Animal Science ◽

10.1017/s1752756200010425 ◽

2005 ◽

Vol 2005 ◽

pp. 131-131 ◽

Cited By ~ 2

Author(s):

M. Dadpasand Taromsari ◽

S. R. Miraei-Ashtiani ◽

M. Moradi Shahrebabak ◽

R. Vaez Torshizi

Keyword(s):

Survival Analysis ◽

Dairy Cattle ◽

Genetic Parameters ◽

Linear Models ◽

Failure Time ◽

Genetic Evaluation ◽

Time Analysis ◽

Productive Life ◽

Analysis Methodology ◽

Time Dependent Covariates

Improvement of herd life increases profitability due to lower replacement costs of heifers, higher proportion of mature cows that produce at their maximum potential and increased opportunity for voluntary culling. Functional productive life (PL) after adjustment for production is the ability of a cow to remain healthy and delay involuntary culling (Ducrocq et al 1988). Survival or failure time analysis has replaced linear model approaches for routine genetic evaluation of dairy cattle in several countries (Sewalem et. al. 2003). It allows proper treatment of censored data, inclusion of time-dependent covariates and skewed or non normal distribution of data. Approximate estimates of the heritability of longevity traits typically range from 0.05 to 0.10 and 0.15 to 0.20 using linear models and survival analysis, respectively (Vollema et al. 2000 and Caraviello et. al. 2004). The objective of this study was to apply survival analysis methodology for assessing the most important factor influencing PL, estimation of genetic parameters of productive life and genetic evaluation in Iranian Holstein dairy cattle.

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