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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
Maria Blettner, Thomas Augustin, Ralf Bender   +2 more
exaly   +6 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.
Marek J. Druzdzel, Jidapa Kraisangka
exaly   +5 more sources

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Model inconsistency, illustrated by the cox proportional hazards model

Statistics in Medicine, 1995
AbstractWe consider problems involving the comparison of two or more treatments where we have the opportunity to adjust for relevant covariates either conditionally in a regression model or implicitly in repeated measures data, for example, in crossover trials. It is seen that for data arising from non‐Normal distributions there is the possibility that
I, Ford, J, Norrie, S, Ahmadi
openaire   +2 more sources

The Cox Proportional Hazards Model

2016
The proportional hazards (PH) or Cox model holds on E, if the hazard rate has the form $$\begin{aligned} \lambda _{x(\cdot )}(t) = r\{x(t)\} \;\lambda _0(t), \quad x(\cdot ) \in E, \end{aligned}$$ where \(\lambda _0(\cdot )\) is an unspecified baseline hazard rate function , and \(r(\cdot )\) is a positive function on E. The function \(r(\cdot )\
Mikhail Nikulin, Hong-Dar Isaac Wu
openaire   +1 more source