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

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.

scholarly journals Parametric regression model for response time in clinical trials – a bayesian approach

2017 ◽  
Vol 7 (1) ◽  
pp. 1-7 ◽  
Author(s):  
SUNDARAM N
Keyword(s):  
Clinical Trials ◽  
Monte Carlo ◽  
Regression Model ◽  
Survival Data ◽  
Regression Models ◽  
Bayesian Regression ◽  
The Other ◽  
Mcmc Methods ◽  
Censored Survival Data ◽  
Parametric Regression

In this paper an attempt has been made to model the censored survival data using Bayesian regressions with Markov Chain Monte Carlo (MCMC) methods. Bayesian LogNormal (LN) regression model are found to be providing better fit than the other Bayesian regression models namely Exponential (E), Generalized Exponential (GE), Webull (W), LogLogistic (LL) and Gamma (G).

scholarly journals Analisis Survival dengan Model Regresi pada Data Tersensor Berdistribusi Log-Logistik

2018 ◽  
Vol 2 (2) ◽  
pp. 28-35
Author(s):  
Gatri Eka Kusumawardhani ◽  
Vera Maya Santi ◽  
Suyono Suyono
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Survival Data ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Likelihood Method ◽  
Censored Survival Data ◽  
Logistic Regression Models ◽  
Lung Cancer Patients ◽  
Time Required

Survival analysis is an analysis used to determine the length of time required by an object in order to survive. That time is sometimes influenced by several factors called independent variables. One way to know relationship is through a regression model. The dependent variable in this regression model is a survival time which is log-logistic distributed. The data used in this study were right censored survival data. Log-logistic regression models for survival data can be expressed by transformation Y=lnT= θ0+θ1xi1+...+θixij+σԑ. The parameter of the log-logistic regression models for right censored survival data are estimated with the maximum likelihood method. In this study, the application of log-logistic regression model for survival data is in data of lung cancer patients. Based on the data already performed, best log-logistic regression model is obtained yi=1.92458+0.0242393 xi1+0.639037ԑi.

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