17,989 research outputs found
On Partial Identification of the Pure Direct Effect
In causal mediation analysis, nonparametric identification of the pure
(natural) direct effect typically relies on, in addition to no unobserved
pre-exposure confounding, fundamental assumptions of (i) so-called
"cross-world-counterfactuals" independence and (ii) no exposure- induced
confounding. When the mediator is binary, bounds for partial identification
have been given when neither assumption is made, or alternatively when assuming
only (ii). We extend existing bounds to the case of a polytomous mediator, and
provide bounds for the case assuming only (i). We apply these bounds to data
from the Harvard PEPFAR program in Nigeria, where we evaluate the extent to
which the effects of antiretroviral therapy on virological failure are mediated
by a patient's adherence, and show that inference on this effect is somewhat
sensitive to model assumptions.Comment: 24 pages, 4 figure
Predictive haemodynamics in a one-dimensional human carotid artery bifurcation. Part II: application to graft design
A Bayesian surrogate modelling technique is proposed that may be able to predict an optimal bypass graft configuration for patients suffering with stenosis in the internal carotid artery (ICA). At the outset, this statistical technique is considered as a means for identifying key geometric parameters influencing haemodynamics in the human carotid bifurcation. This methodology uses a design of experiments (DoE) technique to generate candidate geometries for flow analysis. A pulsatile one dimensional Navier-Stokes solver incorporating fluid-wall interactions for a Newtonian fluid which predicts pressure and flow in the carotid bifurcation (comprising a stenosed segment in the internal carotid artery) is used for the numerical simulations. Two metrics, pressure variation factor (PVF) and maximum pressure (pm) are employed to directly compare the global and local effects, respectively, of variations in the geometry. The values of PVF and pm are then used to construct two Bayesian surrogate models. These models are statistically analysed to visualise how each geometric parameter influences PVF and pm. Percentage of stenosis is found to influence these pressure based metrics more than any other geometric parameter. Later, we identify bypass grafts with optimal geometric and material properties which have low values of PVF on five test cases with 70%, 75%, 80%, 85% and 90% stenosis in the ICA, respectively
Design of Experiments for Screening
The aim of this paper is to review methods of designing screening
experiments, ranging from designs originally developed for physical experiments
to those especially tailored to experiments on numerical models. The strengths
and weaknesses of the various designs for screening variables in numerical
models are discussed. First, classes of factorial designs for experiments to
estimate main effects and interactions through a linear statistical model are
described, specifically regular and nonregular fractional factorial designs,
supersaturated designs and systematic fractional replicate designs. Generic
issues of aliasing, bias and cancellation of factorial effects are discussed.
Second, group screening experiments are considered including factorial group
screening and sequential bifurcation. Third, random sampling plans are
discussed including Latin hypercube sampling and sampling plans to estimate
elementary effects. Fourth, a variety of modelling methods commonly employed
with screening designs are briefly described. Finally, a novel study
demonstrates six screening methods on two frequently-used exemplars, and their
performances are compared
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