Insulin resistance is the main pathologic mechanism that links the constellation of clinical, metabolic and anthropometric traits with increased risk for cardiovascular disease and type II diabetes mellitus. These traits include hyperinsulinemia, impaired glucose intolerance, endothelial dysfunction, dyslipidemia, hypertension, and generalized and upper body fat redistribution. This cluster is often referred to as insulin resistance syndrome. The progression of insulin resistance to diabetes mellitus parallels the progression of endothelial dysfunction to atherosclerosis leading to cardiovascular disease and its complications. In fact, insulin resistance assessed by homeostasis model assessment (HOMA) has shown to be independently predictive of cardiovascular disease in several studies and one unit increase in insulin resistance is associated with a 5.4% increase in cardiovascular disease risk. This review article addresses the role of insulin resistance as a main causal factor in the development of metabolic syndrome and endothelial dysfunction, and its relationship with cardiovascular disease. In addition to this, we review the type of lifestyle modification and pharmacotherapy that could possibly ameliorate the effect of insulin resistance and reverse the disturbances in insulin, glucose and lipid metabolism.

BACKGROUND: Lipoprotein-associated phospholipase A2 (Lp-PLA(2)) is a novel inflammatory biomarker that is associated with increased cardiovascular disease risk independent of and additive to traditional risk factors. Lp-PLA(2) activity is correlated with the degree of inflammation in the atherosclerotic plaque. In human blood, approximately 80% of Lp-PLA(2) is associated with low-density lipoproteins (LDL). Thus, it is hypothesized that changes in Lp-PLA(2) should imitate the changes in the LDL cholesterol.OBJECTIVE: In this present study, we examined the efficacy of lifestyle intervention and combination lipid-lowering therapy on reducing the Lp-PLA(2) levels and determined the relationship between changes in LDL-C and Lp-PLA(2).METHODS: This retrospective chart review study includes two hundred forty eight patients (58% men and 42% women) who completed the life style intervention in combination with pharmacologic therapy for an average period of 10.5 months. Life style modification included diet and exercise counseling. Combination therapy included omega 3 fish oil (2000 mg/d), ex tended-re lease niacin (500-1000 mg/d), ezetimibe (10 mg/d), fenofibrate 160 mg/d and colesevelam HCI (1850 mg/d), as well as statins. The statins used were either simvastatin (20-40 mg/d) or rosuvastatin (5-20 mg/d). Sixty five percent (n = 161) received low to medium doses of simvastatin, whereas 35% (n = 87) received low to medium doses of rosuvastatin.RESULTS: The study revealed a 32.5% reduction in mean Lp-PLA(2) values (baseline 181.1 +/- 41.5 vs 122.1 +/- 28.1 ng/mL after treatment; P < .001). The change observed in LDL-C was 41%, (baseline 126.2 +/- 43 vs 73.9 +/- 37.7 mg/dL after treatment), which also was significant (P < .001). However, a Pearson correlation test analysis revealed only a weak positive association between changes in Lp-PLA(2) and LDL-C (r(2) = 0.052, P < .001).CONCLUSION: Lp-PLA(2) is reduced with the use of lifestyle counseling and combination lipid lowering therapy. Results also revealed that changes in Lp-PLA(2) maybe partially explained by the changes in LDL-C. (C) 2009 National Lipid Association. All rights reserved.

The objective of this study was to evaluate the efficacy of combination drug pulse therapy in maintaining lipid levels in patients intolerant of a daily dose of statins. Twenty-three patients, previously receiving aggressive statin therapy, were treated twice weekly with rosuvastatin or atorvastatin in different dosages along with ezetimibe as well as daily doses of bile acid sequestrant for a mean period of 4.5 months. The recommended National Cholesterol Education Program Adult Treatment Panel III goals had already been achieved in 78% of patients (n=18) before starting combination pulse therapy. This combination therapy significantly increased high-density lipoprotein cholesterol values by 5.82% (t=2.138, P=.044), while the increases in total cholesterol, low-density lipoprotein cholesterol, triglyceride, and apolipoprotein B levels compared with baseline were not statistically significant. Overall, 3 of 23 patients (13%) discontinued the combination therapy because of muscle-related symptoms over a mean course of 4.5 months of treatment.

During the last several last decades, reduction in lipids has been the main focus to decrease the risk of coronary heart disease (CHD). Several lines of evidence, however, have indicated that lipids account only for the <50% of variability in cardiovascular risk in the United States. Therefore, for better identification of people at high cardiovascular risk, a more effective and complete approach is required. Our understanding of atherosclerosis has shifted from a focal disease resulting in symptoms caused by severe stenosis to a systemic disease distinguished by plaque inflammation with a potential to rupture and thrombosis, turning a substenotic atherosclerotic lesion into a complete occlusive lesion. Lipoprotein-associated phospholipase A(2) (Lp-PLA(2)) is a novel inflammatory biomarker that can provide much needed information about plaque inflammation and plaque stability. Lp-PLA(2) is among the multiple biomarkers that have been associated with increased CHD risk. In this present work, we review the evidence from previous studies addressing the effect of different therapies on decreasing Lp-PLA(2) and the role of direct Lp-PLA(2) inhibitors. This work also briefly reviews the evidence of Lp-PLA(2) clinical utility as a potential marker of vascular inflammation and formation of rupture prone plaques. Additionally, we also discuss the implication of available evidence in context of current cardiovascular inflammatory biomarkers recommendations and the evidence from epidemiologic studies addressing the relationship of Lp-PLA(2) and risk of cardiovascular disease.

Existing random utility models of brand choice behavior, in the tradition of McFadden (1974), are based on the assumption that consumers employ the decision rule of maximum utility to choose a brand. However, there is a rich body of empirical work in mathematical psychology that suggests that consumers choose probabilistically between brands (see, for example, Luce 1959). In this paper, we propose a random utility model of brand choice that is based on the decision rule of probabilistic choice, and nests the traditional random utility model as a special case. We argue that the proposed random utility model is mathematically more flexible than existing random utility models. We illustrate the empirical implementation of the proposed random utility model using real-world choice data as well as experimental choice data. We compare the empirical performance of our proposed model to the traditional random utility model. Interestingly, our proposed model outperforms the traditional random utility model (that it nests as a special case) in explaining both consumers’ real-world choices and experimental choices.

This short note introduces a new methodology for capturing and portraying the competitive structure of a market. Using the current Presidential contest as the context, the methodology first describes a simple and efficient data collection approach that generates the data necessary for applying Tversky’s Elimination-By-Aspects Model (1972a, 1972b) at the market level. Then, relying on recently published mathematical results (Batsell, Polking, Cramer, and Miller, 2003) the method shows the complete decomposition of the competitive structure of a market based on the elimination model. Also introduced is a new way to graphically portray the competitive structure of a market in the form of a choice-based perceptual map, which has several desirable properties.

On behalf of the Wharton School, Dean Patrick Harker, and myself, I would like to welcome those of you who have traveled to be with us to recognize Paul Green's many contributions.

It has been over 30 years since Tversky's elimination by aspects (EBA) model was introduced. Despite the potential importance of this elegant model, no method for parameter estimation has emerged. In this paper we prove that simple differences in observed choice probabilities across choice sets are linearly related to the parameters in the EBA model. Once the appropriate probability differences are calculated it is then easy to use widely available least-squares software to estimate the EBA parameters. This paper also corrects a misconception about the number of free parameters in the EBA model. The parameter estimation approach is demonstrated on data from repeated choices of one individual.

People often have knowledge about the chances of events but are unable to express their knowledge in the form of coherent probabilities. This study proposed to correct incoherent judgment via an optimization procedure that seeks the (coherent) probability distribution nearest to a judge's estimates of chance. This method was applied to the chances of simple and complex meteorological events, as estimated by college undergraduates. No judge responded coherently, but the optimization method found close (coherent) approximations to their estimates. Moreover, the approximations were reliably more accurate than the original estimates, as measured by the quadratic scoring rule. Methods for correcting incoherence facilitate the analysis of expected utility and allow human judgment to be more easily exploited in the construction of expert systems.

This article addresses a key question in the application of Card, Moran, and Newell's (1983) keystroke-level model to software in which users specify a command by working through a system of hierarchical menus. For example, to insert a row in Lotus 1-2-3®, the user makes three menu choices: W for worksheet, I for insert, and R for row. In the keystroke-level model, it is assumed that a time-consuming mental operation precedes each command. The question in the application of the keystroke-level model to hierarchical menu systems is whether the keystrokes WIR in the previous example constitute the execution of three commands and thus require three mental operations or whether WIR acts as a single command and requires only one mental operation. Data were collected from four highly experienced Lotus 1-2-3 users as they went about their day-to-day work. Strong evidence that only one mental operation is involved in choosing from a hierarchical menu system was obtained. We hypothesize that the discrepancy of our results from the data of others is due to the fact that our subjects were more experienced. The implications of our findings for the design of menus is discussed.

Knowledge of how software is actually used by people can assist software developers and internal MIS application development personnel to improve the user-interface of existing software, in creating new user interface styles for existing software packages, and to improve the training for personnel using software packages. This article reports results from a study that examined the use of a popular spreadsheet software by 40 experienced users in their work environment. Of the 505 commands that could be used, 18 (3.6%) accounted for over 80% of the usage. More than 50% of the available commands were never used. Most of the command usage was related to creating, maintaining, and printing spreadsheets.

Our paper reviews and summarizes the state-of-the-art in the design and analysis of consumer choice experiments. We emphasize experiments involving discrete choices, but also review related work on the design and analysis of ranking and resource allocation experiments. Major topics include 1) Choice experiments and conjoint analysis, 2) Random utility and constant utility probabilistic discrete choice models as a theoretical foundation for choice experiments, and 3) The design of choice experiments. Other topics include a) Experimental procedure, b) Model specification, c) Model estimation, and d) Model validation. Suggestions for future research are made with respect to each topic.

The ability to predict users' commands in Lotus 1-2-3 was investigated. Keystroke data was collected from four subjects in their normal work environment, with over 2700 commands being analysed. The data was used to construct transitional probability matrices that were then used to predict commands based on their immediate predecessors. Approximately half of the users' commands were correctly predicted in this manner. Also of interest was how often the users' commands would be either the most likely or the second most likely command given the previous commands. When the previous two commands were used to derive two predictions, the probability that the users' next command would be one of the two predicted commands was 0.81. The average probability of command repetition was 0.33. It was concluded that users appear to be extremely predictable in the Lotus 1-2-3 environment, and this predictability could serve as the foundation for an interface that aids the user by providing easy access to the next most likely command to be issued and by adapting the probabilities of command use over time.

People process natural language in real time and with very limited short-term memories. This article describes a computational architecture for syntactic performance that also requires fixed finite resources.

Marketing researchers use the multinomial logit (MNL) model to analyze discrete choice, and estimate parameters either by maximum likelihood (ML) or minimum logit chi square (MLCS). Some controversy persists, however, over which is better. Review articles in marketing recommend ML over MLCS, but the statistics literature suggests that MLCS should be preferred. No studies have directly compared the performance of ML and MLCS in a marketing context. The authors assess the relative performance of ML, MLCS, and three other candidate estimators for MNL marketing applications involving repeated-measures datasets collected by means of multiple-subset designs. In contrast to most previous findings in the statistics literature, the results strongly support the use of ML. ML is found to outperform the other estimators on a variety of point estimation, predictive accuracy, and statistical inference criteria and ML test statistics are found to have asymptotic behavior for datasets involving relatively few replications.

Applications of market share models which (implicitly) rely on Luce's choice axiom have been widely criticized because they cannot account for the effects of differential product substitutability and product dominance. Three types of choice models—Tversky's Elimination-By-Aspects model, Tree Models, and Generalized PROBIT—have been offered as solutions to the problems identified with the Luce model, but they each suffer from limitations which have prevented their widespread application in marketing contexts. Tversky's elegant EBA model has not been widely used because it requires a large number of parameters and no special-purpose parameter estimation software has yet emerged. Tree models have been offered as more parsimonious special cases of EBA, but they are more restrictive in that they presume: (1) that products, and the process of choosing from among them, can be characterized in terms of hierarchical, attribute-based trees; and (2) that the aspects governing choice are well-known. Generalized PROBIT can paramorphically handle the problems with the Luce model, but parameter estimation software has proved problematic because it cannot guarantee a globally optimum solution. This paper proposes a new class of market share models. Rather than model the choice process explicitly, the new models simply scale the effects competing products have on each other's market share. These competitive effects are scaled in the context of a class of market-share models which: (1) do not assume a tree-like structure for the competing products; (2) do not presume any a priori knowledge about the attributes governing choice; (3) are characterized in terms of parameters that can be estimated using ordinary least-squares; and (4) provide clear managerial insight into the sources of competition. The paper begins with a brief review of previous work. Following the review, the paper offers a theorem and proof which guarantees the existence of the new class of models. The empirical validity and strategic utility of the models are demonstrated using two separate sets of data.

The authors describe a model and measurement methodology by which to predict the probability that an individual consumer will choose a product from an offered set of competing products. After a brief review of alternative approaches, they use a simple example to illustrate the proposed methodology. They demonstrate the empirical validity of the methodology by using data from an experiment in repetitive choice.