coxed: An R Package for Computing Duration-Based Quantities from the Cox Proportional Hazards Model
نویسندگان
چکیده
منابع مشابه
Gradient lasso for Cox proportional hazards model
MOTIVATION There has been an increasing interest in expressing a survival phenotype (e.g. time to cancer recurrence or death) or its distribution in terms of a subset of the expression data of a subset of genes. Due to high dimensionality of gene expression data, however, there is a serious problem of collinearity in fitting a prediction model, e.g. Cox's proportional hazards model. To avoid th...
متن کامل0.1 coxph: Cox Proportional Hazards Regression for Duration Dependent Variables
Choose the Cox proportional hazards regression model if the values in your dependent variable are duration observations. The advantage of the semi-parametric Cox proportional hazards model over fully parametric models such as the exponential or Weibull models is that it makes no assumptions about the shape of the baseline hazard. The model only requires the proportional hazards assumption that ...
متن کامل0.1 coxph: Cox Proportional Hazards Regression for Duration Dependent Variables
Choose the Cox proportional hazards regression model if the values in your dependent variable are duration observations. The advantage of the semi-parametric Cox proportional hazards model over fully parametric models such as the exponential or Weibull models is that it makes no assumptions about the shape of the baseline hazard. The model only requires the proportional hazards assumption that ...
متن کامل0.1 coxph: Cox Proportional Hazards Regression for Duration Dependent Variables
Choose the Cox proportional hazards regression model if the values in your dependent variable are duration observations. The advantage of the semi-parametric Cox proportional hazards model over fully parametric models such as the exponential or Weibull models is that it makes no assumptions about the shape of the baseline hazard. The model only requires the proportional hazards assumption that ...
متن کاملL1 penalized estimation in the Cox proportional hazards model.
This article presents a novel algorithm that efficiently computes L(1) penalized (lasso) estimates of parameters in high-dimensional models. The lasso has the property that it simultaneously performs variable selection and shrinkage, which makes it very useful for finding interpretable prediction rules in high-dimensional data. The new algorithm is based on a combination of gradient ascent opti...
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ژورنال
عنوان ژورنال: The R Journal
سال: 2019
ISSN: 2073-4859
DOI: 10.32614/rj-2019-042