Results 31 to 40 of about 402,477 (353)
Factors affecting the survival of prediabetic patients: comparison of Cox proportional hazards model and random survival forest method. [PDF]
BMC Med Inform Decis MakSharafi M +8 more
europepmc +2 more sources
Tree-augmented Cox proportional hazards models [PDF]
Biostatistics, 2005 We study a hybrid model that combines Cox proportional hazards regression with tree-structured modeling. The main idea is to use step functions, provided by a tree structure, to 'augment' Cox (1972) proportional hazards models. The proposed model not only provides a natural assessment of the adequacy of the Cox proportional hazards model but also ...
Su, Xiaogang, Tsai, Chih Ling
openaire +5 more sources
Fitting the Cox proportional hazards model to big data. [PDF]
BiometricsAbstractThe semiparametric Cox proportional hazards model, together with the partial likelihood principle, has been widely used to study the effects of potentially time-dependent covariates on a possibly censored event time. We propose a computationally efficient method for fitting the Cox model to big data involving millions of study subjects ...
Wang J, Zeng D, Lin DY.
europepmc +3 more sources
Variable Selection for Cox's proportional Hazards Model and Frailty Model [PDF]
The Annals of Statistics, 2002 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jianqing Fan, Runze Li
semanticscholar +3 more sources
The Dantzig Selector in Cox's Proportional Hazards Model [PDF]
Scandinavian Journal of Statistics, 2010 The Dantzig selector (DS) is a recent approach of estimation in high-dimensional linear regression models with a large number of explanatory variables and a relatively small number of observations. As in the least absolute shrinkage and selection operator (LASSO), this approach sets certain regression coefficients exactly to zero, thus performing ...
Antoniadis, Anestis +2 more
openaire +4 more sources
A Federated Cox Model with Non-proportional Hazards
, 2022 Recent research has shown the potential for neural networks to improve upon classical survival models such as the Cox model, which is widely used in clinical practice. Neural networks, however, typically rely on data that are centrally available, whereas healthcare data are frequently held in secure silos.
D. Kai Zhang +2 more
openaire +3 more sources
Adaptive Lasso for Cox's proportional hazards model [PDF]
Biometrika, 2007 SUMMARY We investigate the variable selection problem for Cox's proportional hazards model, and propose a unified model selection and estimation procedure with desired theoretical properties and computational convenience. The new method is based on a penalized log partial likelihood with the adaptively weighted L1 penalty on regression coefficients ...
Hao Helen Zhang, Wenbin Lu
semanticscholar +4 more sources
Life Cycle, 2023 In medical research, analyzing the time it takes for a phenomenon to occur is sometimes crucial. However, various factors can contribute to the length of survival or observation periods, and removing specific data can lead to bias results. In this paper,
S. Lee
semanticscholar +1 more source
Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent.
Journal of Statistical Software, 2011 We introduce a pathwise algorithm for the Cox proportional hazards model, regularized by convex combinations of ℓ1 and ℓ2 penalties (elastic net). Our algorithm fits via cyclical coordinate descent, and employs warm starts to find a solution along a ...
N. Simon +3 more
semanticscholar +1 more source
A mixed model approach for structured hazard regression [PDF]
, 2004 The classical Cox proportional hazards model is a benchmark approach to analyze continuous survival times in the presence of covariate information. In a number of applications, there is a need to relax one or more of its inherent assumptions, such as ...
Fahrmeir, Ludwig, Kneib, Thomas
core +4 more sources