Analysis of censored survival data using random regression models

2001 ◽  
Vol 72 (1) ◽  
pp. 1-10 ◽  
Author(s):  
R. F. Veerkamp ◽  
S. Brotherstone ◽  
B. Engel ◽  
T. H. E. Meuwissen
Keyword(s):  
Milk Yield ◽  
Survival Data ◽  
Variance Components ◽  
Regression Models ◽  
Proportional Hazards ◽  
Random Regression ◽  
Breeding Values ◽  
Censored Survival Data ◽  
Proportional Hazards Models ◽  
Randomly Censored Data

AbstractCensoring of records is a problem in the prediction of breeding values for longevity, because breeding values are required before actual lifespan is known. In this study we investigated the use of random regression models to analyse survival data, because this method combines some of the advantages of a multitrait approach and the more sophisticated proportional hazards models. A model was derived for the binary representation of survival data and links with proportional hazards models and generalized linear models are shown. Variance components and breeding values were predicted using a linear approximation, including time-dependent fixed effects and random regression coefficients. Production records in lactations 1 to 5 were available on 24741 cows in the UK, all having had the opportunity to survive five lactations. The random regression model contained a linear regression on milk yield within herd (no. = 1417) by lactation number (no. = 4), Holstein percentage and year-month of calving effect (no. = 72). The additive animal genetic effects were modelled using orthogonal polynomials of order 1 to 4 with random coefficients and the error terms were fitted for each lactation separately, either correlated or not. Variance components from the full (i.e. uncensored) data set, were used to predict breeding values for survival in each lactation from both uncensored and randomly censored data. In the uncensored data, estimates of heritabilities for culling probability in each lactation ranged from 0·02 to 0·04. Breeding values for lifespan (calculated from the survival breeding values) had a range of 2·4 to 3·6 lactations and a standard deviation of 0·25. Correlations between predicted breeding values for 129 bulls, each with more than 30 daughters, from the various data sets ranged from 0·81 to 0·99 and were insensitive to the model used. It is concluded that random regression analysis models used for test-day records analysis of milk yield, might also be of use in the analysis of censored survival data.

Predicting breeding values for milk yield of Guzerá (Bos indicus) cows using random regression models

2014 ◽  
Vol 167 ◽  
pp. 41-50 ◽  
Author(s):  
D.J.A. Santos ◽  
M.G.C.D. Peixoto ◽  
R.R. Aspilcueta Borquis ◽  
J.C.C. Panetto ◽  
L. El Faro ◽  
...  
Keyword(s):  
Milk Yield ◽  
Regression Models ◽  
Bos Indicus ◽  
Random Regression ◽  
Breeding Values

scholarly journals Genetic evaluation of growth traits in Nellore cattle through multi-trait and random regression models

2018 ◽  
Vol 63 (No. 6) ◽  
pp. 212-221 ◽  
Author(s):  
B.B. Teixeira ◽  
R.R. Mota ◽  
R.B. Lôbo ◽  
L.P. Silva ◽  
A.P. Souza Carneiro ◽  
...  
Keyword(s):  
Random Effects ◽  
Variance Components ◽  
Regression Models ◽  
Growth Traits ◽  
Growth Curves ◽  
Genetic Evaluation ◽  
Information Criteria ◽  
Random Regression ◽  
Nellore Cattle ◽  
Heritability Estimates

We aimed to evaluate different orders of fixed and random effects in random regression models (RRM) based on Legendre orthogonal polynomials as well as to verify the feasibility of these models to describe growth curves in Nellore cattle. The proposed RRM were also compared to multi-trait models (MTM). Variance components and genetic parameters estimates were performed via REML for all models. Twelve RRM were compared through Akaike (AIC) and Bayesian (BIC) information criteria. The model of order three for the fixed curve and four for all random effects (direct genetic, maternal genetic, permanent environment, and maternal permanent environment) fits best. Estimates of direct genetic, maternal genetic, maternal permanent environment, permanent environment, phenotypic and residual variances were similar between MTM and RRM. Heritability estimates were higher via RRM. We presented perspectives for the use of RRM for genetic evaluation of growth traits in Brazilian Nellore cattle. In general, moderate heritability estimates were obtained for the majority of studied traits when using RRM. Additionally, the precision of these estimates was higher when using RRM instead of MTM. However, concerns about the variance components estimates in advanced ages via Legendre polynomial must be taken into account in future studies.

Effect of HPV Status on Survival of Oropharynx Cancer with Distant Metastasis

2020 ◽  
Vol 163 (2) ◽  
pp. 372-374 ◽  
Author(s):  
Adam W. Kaplon ◽  
Thomas J. Galloway ◽  
Mihir K. Bhayani ◽  
Jeffrey C. Liu
Keyword(s):  
Distant Metastasis ◽  
Survival Data ◽  
Regression Models ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
National Cancer Database ◽  
Proportional Hazards Regression ◽  
Kaplan Meier ◽  
Hpv Status ◽  
Oropharynx Squamous Cell Carcinoma

Human papillomavirus (HPV)–positive oropharynx squamous cell carcinoma (OPSCC) is known to have improved survival over HPV-negative disease. However, it is largely unknown whether HPV status similarly affects survival in patients presenting with distant metastatic disease. We queried the National Cancer Database for OPSCC with distant metastasis. Kaplan-Meier curves and Cox proportional hazards regression models controlling for relevant demographics were used to evaluate overall survival. In total, 768 OPSCC cases were available for analysis with HPV and survival data: 50% of cases were HPV negative and 50% were HPV positive. The 1- and 2-year survival for HPV-negative disease was 49% and 27%, respectively, as compared with 67% and 42% in the HPV-positive cohort. HPV positivity was associated with improved median survival in treated and untreated patients. Age, comorbidities, and HPV status were predictive of improved survival on multivariate analysis. HPV-positive OPSCC has improved survival in the setting of distant metastatic presentation as compared with HPV-negative disease and shows greater responsiveness to treatment.

scholarly journals Test-day or 305-day milk yield for genetic evaluation of Gir cattle

2019 ◽  
Vol 54 ◽  
Author(s):  
Rodrigo Junqueira Pereira ◽  
Denise Rocha Ayres ◽  
Mário Luiz Santana Junior ◽  
Lenira El Faro ◽  
Aníbal Eugênio Vercesi Filho ◽  
...  
Keyword(s):  
Regression Model ◽  
Milk Yield ◽  
Genetic Evaluation ◽  
Random Regression ◽  
Breeding Values ◽  
Random Regression Model ◽  
The Mean ◽  
Yield Record ◽  
Genetic Evaluations ◽  
Dairy Breed

Abstract: The objective of this work was to compare genetic evaluations of milk yield in the Gir breed, in terms of breeding values and their accuracy, using a random regression model applied to test-day records or the traditional model (TM) applied to estimates of 305-day milk yield, as well as to predict genetic trends for parameters of interest. A total of 10,576 first lactations, corresponding to 81,135 test-day (TD) records, were used. Rank correlations between the breeding values (EBVs) predicted with the two models were 0.96. The percentage of animals selected in common was 67 or 82%, respectively, when 1 or 5% of bulls were chosen, according to EBVs from random regression model (RRM) or TM genetic evaluations. Average gains in accuracy of 2.7, 3.0, and 2.6% were observed for all animals, cows with yield record, and bulls (sires of cows with yield record), respectively, when the RRM was used. The mean annual genetic gain for 305-day milk yield was 56 kg after 1993. However, lower increases in the average EBVs were observed for the second regression coefficient, related to persistency. The RRM applied to TD records is efficient for the genetic evaluation of milk yield in the Gir dairy breed.

scholarly journals Bayesian analysis of random regression models using B-splines to model test-day milk yield of Holstein cattle in Brazil

2012 ◽  
Vol 150 (1-3) ◽  
pp. 401-406 ◽  
Author(s):  
A.B. Bignardi ◽  
L. El Faro ◽  
M.L. Santana ◽  
G.J.M. Rosa ◽  
V.L. Cardoso ◽  
...  
Keyword(s):  
Bayesian Analysis ◽  
Milk Yield ◽  
Model Test ◽  
Regression Models ◽  
Random Regression ◽  
Holstein Cattle ◽  
B Splines

Fitting regression models to censored survival data

1983 ◽  
Vol 2 (2) ◽  
pp. 287-293 ◽  
Author(s):  
M. Hudec ◽  
H. Platz
Keyword(s):  
Survival Data ◽  
Regression Models ◽  
Censored Survival Data
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