Results 1 to 10 of about 203,044 (255)
Statistics in MedicineINTRODUCTION The Cox proportional hazards regression model is frequently used to develop clinical prediction models for time-to-event outcomes, allowing clinicians to estimate an individual’s risk of experiencing the outcome within specified time horizons (e.g., estimate an individual’s 10-year risk of death) [1].
Austin P. C., Giardiello D.
exaly +7 more sources
The impact of violation of the proportional hazards assumption on the discrimination of the Cox proportional hazards model. [PDF]
Diagn Progn ResBackground The Cox proportional hazards regression model is frequently used to estimate an individual’s probability of experiencing an outcome within a specified prediction horizon. A key assumption of this model is that of proportional hazards.
Austin PC, Giardiello D.
europepmc +4 more sources
A Bayesian network interpretation of the Cox's proportional hazard model
International Journal of Approximate Reasoning, 2018 Cox's proportional hazards (CPH) model is quite likely the most popular modeling technique in survival analysis. While the CPH model is able to represent a relationship between a collection of risks and their common effect, Bayesian networks have become an attractive alternative with an increased modeling power and far broader applications.
Jidapa Kraisangka, Marek J. Druzdzel
openaire +6 more sources
Generating survival times to simulate Cox proportional hazards models [PDF]
Statistics in Medicine, 2005 Abstract Simulation studies present an important statistical tool to investigate the performance, properties and adequacy of statistical models in pre‐specified situations. One of the most important statistical models in medical research is the proportional hazards model of Cox. In this paper, techniques to generate survival times for
Bender, Ralf +2 more
openaire +5 more sources
A Federated Cox Model with Non-Proportional Hazards
CoRR, 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.
Dekai Zhang +2 more
openaire +3 more sources
Estimation in a Cox Proportional Hazards Cure Model [PDF]
Biometrics, 2000 Summary.Some failure time data come from a population that consists of some subjects who are susceptible to and others who are nonsusceptible to the event of interest. The data typically have heavy censoring at the end of the follow‐up period, and a standard survival analysis would not always be appropriate.
Sy, Judy P., Taylor, Jeremy M. G.
openaire +4 more sources
Gradient lasso for Cox proportional hazards model [PDF]
Bioinformatics, 2009 AbstractMotivation: 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,
Insuk Sohn +3 more
openaire +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 +4 more sources
On model specification and selection of the Cox proportional hazards model [PDF]
Statistics in Medicine, 2013 Prognosis plays a pivotal role in patient management and trial design. A useful prognostic model should correctly identify important risk factors and estimate their effects. In this article, we discuss several challenges in selecting prognostic factors and estimating their effects using the Cox proportional hazards model.
Lin, Chen-Yen, Halabi, Susan
openaire +3 more sources
What is Cox's Proportional Hazards Model?
Significance, 2022 Abstract Robert Tibshirani gives an overview of one of David Cox's most widely applied ideas, for which he was awarded the International Prize in Statistics in ...
openaire +1 more source