The addition of depression to the Framingham Risk Equation model for predicting coronary heart disease risk in women

BACKGROUND: Depression is widely considered to be an independent and robust predictor of Coronary Heart Disease (CHD), however is seldom considered in the context of formal risk assessment. We assessed whether the addition of depression to the Framingham Risk Equation (FRE) improved accuracy for predicting 10-year CHD in a sample of women.

DESIGN: A prospective, longitudinal design comprising an age-stratified, population-based sample of Australian women collected between 1993 and 2011 (n=862).

METHODS: Clinical depressive disorder was assessed using the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders (SCID-I/NP), using retrospective age-of-onset data. A composite measure of CHD included non-fatal myocardial infarction, unstable angina coronary intervention or cardiac death. Cox proportional-hazards regression models were conducted and overall accuracy assessed using area under receiver operating characteristic (ROC) curve analysis.

RESULTS: ROC curve analyses revealed that the addition of baseline depression status to the FRE model improved its overall accuracy (AUC:0.77, Specificity:0.70, Sensitivity:0.75) when compared to the original FRE model (AUC:0.75, Specificity:0.73, Sensitivity:0.67). However, when calibrated against the original model, the predicted number of events generated by the augmented version marginally over-estimated the true number observed.

CONCLUSIONS: The addition of a depression variable to the FRE equation improves the overall accuracy of the model for predicting 10-year CHD events in women, however may over-estimate the number of events that actually occur. This model now requires validation in larger samples as it could form a new CHD risk equation for women.

Customer repurchase intention: a general structural equation model

This paper develops a general service sector model of repurchase intention from the consumer theory literature. A key contribution of the structural equation model is the incorporation of customer perceptions of equity and value and customer brand preference into an integrated repurchase intention analysis. The model describes the extent to which customer repurchase intention is influenced by seven important factors – service quality, equity and value, customer satisfaction, past loyalty, expected switching cost and brand preference. The general model is applied to customers of comprehensive car insurance and personal superannuation services. The analysis finds that although perceived quality does not directly affect customer satisfaction, it does so indirectly via customer equity and value perceptions. The study also finds that past purchase loyalty is not directly related to customer satisfaction or current brand preference and that brand preference is an intervening factor between customer satisfaction and repurchase intention. The main factor influencing brand preference was perceived value with customer satisfaction and expected switching cost having less influence.

Shear- free perfect fluids with solenoidal magnetic curvature and a γ-law equation of state

We show that shear-free perfect fluids obeying an equation of state p = (γ − 1)μ are non-rotating or non-expanding under the assumption that the spatial divergence of the magnetic part of the Weyl tensor is zero.

Representing Islam: female subjects in Suzanne Fisher Staples's novels

This article offers a reading of Suzanne Fisher Staples's novels Shabanu, Haveli,and Under the Persimmon Tree, drawing on postcolonial theory (in particular, Chandra Talpade Mohanty's essay "Under Western Eyes: Feminist Scholarship and Colonial Discourses" and Edward Said's Orientalism) to inform its analysis of the construction of girls and women in these texts. It argues that the novels' representations of Muslim girls are built on a naturalized contrast between liberal humanist paradigms of individualism and personal freedom, and homogenizing depictions of oppressed Muslim females, thus producing Orientalist distinctions between the West and the Orient.

Interpreting Fanger's comfort equation within the adaptive paradigm

This paper reviews the evolution of Fanger's heat balance equation in regard of adaptive opportunities. Heat balance and adaptive response are integrated into one model as two fundamental aspects of human-environment interaction that define thermal comfort perception, rather than being seen as two concepts of alternative comfort paradigms. The paper suggests to extent Fanger's model with a heat storage term in order to account for comfort perception under transient thermal conditions, and to review Fanger's modelling assumptions in order to allow for a greater variety of adaptive response options. In the presented model heat exchange is modulated through adaptation of physiological, environmental and behavioural parameters in the human-environment system defined through Fanger's heat exchange equations. A computational prototype is implemented to determine 'comfortable' values and ranges of the six comfort dimensions alternatively to Fanger's comfort indices. Thereby values of for example 'comfortable' clothing and metabolic rate are results rather than necessary input parameters, which are difficult to determine. This approach allows generating design advice for physical, organisational and social environments based on heat balance calculation in the six-dimensional opportunity space defined through Fanger's comfort equation. A starting point for the development of a dynamic adaptive comfort model is set.

Structural equation modelling of complex sample survey : an application to brand signalling data

The purpose of this paper is to present an empirical analysis of complex sample data with regard to the biasing effect of nonindependence of observations on standard error parameter estimates. In a two-factor confirmatory factor analysis model, using real data, we show how the bias in standard errors can be derived when the nonindependence is ignored. We demonstrate that the standard error bias produced by the nonindependence of observations can be considerable and we briefly discuss solutions to overcome the problem.

Computational aspects of the numerical solution of SDEs

In the last 30 to 40 years, many researchers have combined to build the knowledge base of theory and solution techniques that can be applied to the case of differential equations which include the effects of noise. This class of ``noisy'' differential equations is now known as stochastic differential equations (SDEs). Markov diffusion processes are included within the field of SDEs through the drift and diffusion components of the Itô form of an SDE. When these drift and diffusion components are moderately smooth functions, then the processes' transition probability densities satisfy the Fokker-Planck-Kolmogorov (FPK) equation -- an ordinary partial differential equation (PDE). Thus there is a mathematical inter-relationship that allows solutions of SDEs to be determined from the solution of a noise free differential equation which has been extensively studied since the 1920s. The main numerical solution technique employed to solve the FPK equation is the classical Finite Element Method (FEM). The FEM is of particular importance to engineers when used to solve FPK systems that describe noisy oscillators. The FEM is a powerful tool but is limited in that it is cumbersome when applied to multidimensional systems and can lead to large and complex matrix systems with their inherent solution and storage problems. I show in this thesis that the stochastic Taylor series (TS) based time discretisation approach to the solution of SDEs is an efficient and accurate technique that provides transition and steady state solutions to the associated FPK equation. The TS approach to the solution of SDEs has certain advantages over the classical techniques. These advantages include their ability to effectively tackle stiff systems, their simplicity of derivation and their ease of implementation and re-use. Unlike the FEM approach, which is difficult to apply in even only two dimensions, the simplicity of the TS approach is independant of the dimension of the system under investigation. Their main disadvantage, that of requiring a large number of simulations and the associated CPU requirements, is countered by their underlying structure which makes them perfectly suited for use on the now prevalent parallel or distributed processing systems. In summary, l will compare the TS solution of SDEs to the solution of the associated FPK equations using the classical FEM technique. One, two and three dimensional FPK systems that describe noisy oscillators have been chosen for the analysis. As higher dimensional FPK systems are rarely mentioned in the literature, the TS approach will be extended to essentially infinite dimensional systems through the solution of stochastic PDEs. In making these comparisons, the advantages of modern computing tools such as computer algebra systems and simulation software, when used as an adjunct to the solution of SDEs or their associated FPK equations, are demonstrated.

Structural equation modelling : tips for getting started with your research

At a time when numerical expression of data is becoming more important in the business of obtaining grant money research tools such as structural equation modelling (SEM) are becoming more popular. Structural equation modelling enables researchers to show relationships between variables numerically. It is of particular use to educational researchers who are interested in developing or using standard questionnaires in their research. This session will introduce SEM and explore its uses in educational research to enable the reader to decide whether it is a tool to use to add an extra dimension to their research findings.