found that models with a categorized exposure variable removed only 67% of the confounding controlled when the continuous version was used. Categorizing continuous variables may not only miss the message, it can also get it wrong. Under some circumstances, categorizing continuous variables can give biased results. In a simulation study, Taylor and Yu [6] found that categorizing one continuous v...

In a Bayesian network with continuous variables containing a variable(s) that is a conditionally deterministic function of its continuous parents, the joint density function for the variables in the network does not exist. Conditional linear Gaussian distributions can handle such cases when the deterministic function is linear and the continuous variables have a multi-variate normal distributio...

we consider the bifurcation of periodic solutions from an equilibrium point of the given equation: x =f(x,?) , where x ? r , ? is a vector of real parameters ? , ? , ... , ? and f:r x r ->r has at least second continuous derivations in variables

In a Bayesian network with continuous variables containing a variable(s) that is a conditionally deterministic function of its continuous parents, the joint density function does not exist. Conditional linear Gaussian distributions can handle such cases when the deterministic function is linear and the continuous variables have a multi-variate normal distribution. In this paper, operations requ...

Clinical research focuses on the relationship between one or more independent variables and some dependent variable outcome chosen to reflect underlying process. For categorical variables, may either be focused a specific end point such as myocardial infarction (MI) an construct vascular disease (with MI just exemplar). In latter instance, composite index major adverse cardiovascular event (MAC...