Abstract: Accelerated Failure Time (AFT) models can be used for the analysis of time to event data to estimate the effects of covariates on acceleration/deceleration of the survival time. The effect of the covariate is measured through a log-linear model taking logarithm of the survival time as the outcome or dependent variable. Hence, the effect of covariate is multiplicative on time scale, and the results of AFT models may be easier to interpret as the covariate effects are directly expressed in terms of time ratio (TR). Some AFT models are applied to the data on time to death of hospitalized Acute Liver Failure (ALF) patients in All India Institute of Medical Sciences, New Delhi, India to identify the prognostic factors. This type of stu...
Survival analysis is a set of methods for statistical inference concerning the time until the occurr...
The proportional hazards (PH) model and the accelerated failure time (AFT) model are the two most po...
There are two important statistical models for multivariate survival analysis, propor-tional hazards...
The proportional hazards model for survival time data usually assumes that the covariates of interes...
As a flexible alternative to the Cox model, the accelerated failure time (AFT) model assumes that th...
Investigation of the reliability of highly reliable components, and systems is an important industri...
10.1111/j.1467-842X.2007.00470.xAustralian and New Zealand Journal of Statistics492155-17
The estimation of progression to liver cirrhosis and identifying its risk factors are often of epide...
The field of survival analysis has experienced tremendous growth during the latter half of the 20th ...
The Cox proportional hazards model (CPH) is normally applied in clinical trial data analysis, but it...
Summary. The accelerated failure time model is an attractive alternative to the Cox model when the p...
Objective: Accelerated Failure Time (AFT) models are an useful alternative of Cox- PH model to deter...
International audienceFor a given system, either biological or technological, survival and reliabili...
BackgroundThe Cox model has been the mainstay of survival analysis in the critically ill and time-de...
We introduce new methods of analysing time to event data via extended versions of the proportional h...
Survival analysis is a set of methods for statistical inference concerning the time until the occurr...
The proportional hazards (PH) model and the accelerated failure time (AFT) model are the two most po...
There are two important statistical models for multivariate survival analysis, propor-tional hazards...
The proportional hazards model for survival time data usually assumes that the covariates of interes...
As a flexible alternative to the Cox model, the accelerated failure time (AFT) model assumes that th...
Investigation of the reliability of highly reliable components, and systems is an important industri...
10.1111/j.1467-842X.2007.00470.xAustralian and New Zealand Journal of Statistics492155-17
The estimation of progression to liver cirrhosis and identifying its risk factors are often of epide...
The field of survival analysis has experienced tremendous growth during the latter half of the 20th ...
The Cox proportional hazards model (CPH) is normally applied in clinical trial data analysis, but it...
Summary. The accelerated failure time model is an attractive alternative to the Cox model when the p...
Objective: Accelerated Failure Time (AFT) models are an useful alternative of Cox- PH model to deter...
International audienceFor a given system, either biological or technological, survival and reliabili...
BackgroundThe Cox model has been the mainstay of survival analysis in the critically ill and time-de...
We introduce new methods of analysing time to event data via extended versions of the proportional h...
Survival analysis is a set of methods for statistical inference concerning the time until the occurr...
The proportional hazards (PH) model and the accelerated failure time (AFT) model are the two most po...
There are two important statistical models for multivariate survival analysis, propor-tional hazards...