A note on the dual scaling of dominance data and its relationship to correspondence analysis

Dual scaling of a subjects-by-objects table of dominance data (preferences, paired comparisons and successive categories data) has been contrasted with correspondence analysis, as if the two techniques were somehow different. In this note we show that dual scaling of dominance data is equivalent to the correspondence analysis of a table which is doubled with respect to subjects. We also show that the results of both methods can be recovered from a principal components analysis of the undoubled dominance table which is centred with respect to subject means.Correspondence analysis, dominance data, dual scaling, paired comparisons, preferences, principal component analysis, ratings

Measuring asymmetries in brand associations using correspondence analysis

Correspondence analysis is introduced in the brand association literature as an alternative tool to measure dominance, for the particular case of free choice data. The method is also used to analyse differences, or asymmetries, between brand-attribute associations where attributes are associated with evoked brands, and brand-attribute associations where brands are associated with the attributes. An application to a sample of deodorants is used to illustrate the proposed methodology.Brand dominance, attribute dominance, measure of assymetries, correspondence analysis

The epidemiology and clinical correlates of HIV-1 co-receptor tropism in non-subtype B infections from India, Uganda and South Africa

BACKGROUND: The introduction of C-C chemokine receptor type-5 (CCR5) antagonists as antiretroviral therapy has led to the need to study HIV co-receptor tropism in different HIV-1 subtypes and geographical locations. This study was undertaken to evaluate HIV-1 co-receptor tropism in the developing world where non-B subtypes predominate, in order to assess the therapeutic and prophylactic potential of CCR5 antagonists in these regions. METHODS: HIV-1-infected patients were recruited into this prospective, cross-sectional, epidemiologic study from HIV clinics in South Africa, Uganda and India. Patients were infected with subtypes C (South Africa, India) or A or D (Uganda). HIV-1 subtype and co-receptor tropism were determined and analyzed with disease characteristics, including viral load and CD4+ and CD8+ T cell counts. RESULTS: CCR5-tropic (R5) HIV-1 was detected in 96% of treatment-naive (TN) and treatment-experienced (TE) patients in India, 71% of TE South African patients, and 86% (subtype A/A1) and 71% (subtype D) of TN and TE Ugandan patients. Dual/mixed-tropic HIV-1 was found in 4% of Indian, 25% of South African and 13% (subtype A/A1) and 29% (subtype D) of Ugandan patients. Prior antiretroviral treatment was associated with decreased R5 tropism; however, this decrease was less in subtype C from India (TE: 94%, TN: 97%) than in subtypes A (TE: 59%; TN: 91%) and D (TE: 30%; TN: 79%). R5 virus infection in all three subtypes correlated with higher CD4+ count. CONCLUSIONS: R5 HIV-1 was predominant in TN individuals with HIV-1 subtypes C, A, and D and TE individuals with subtypes C and A. Higher CD4+ count correlated with R5 prevalence, while treatment experience was associated with increased non-R5 infection in all subtypes

A divergent role for estrogen receptor-beta in node-positive and node-negative breast cancer classified according to molecular subtypes: an observational prospective study

Introduction: Estrogen receptor-alpha (ER-alpha) and progesterone receptor (PgR) are consolidated predictors of response to hormonal therapy (HT). In contrast, little information regarding the role of estrogen receptor-beta (ER-beta) in various breast cancer risk groups treated with different therapeutic regimens is available. In particular, there are no data concerning ER-beta distribution within the novel molecular breast cancer subtypes luminal A (LA) and luminal B (LB), HER2 (HS), and triple-negative (TN). Methods: We conducted an observational prospective study using immunohistochemistry to evaluate ER-beta expression in 936 breast carcinomas. Associations with conventional biopathological factors and with molecular subtypes were analyzed by multiple correspondence analysis (MCA), while univariate and multivariate Cox regression analysis and classification and regression tree analysis were applied to determine the impact of ER-beta on disease-free survival in the 728 patients with complete follow-up data. Results: ER-beta evenly distributes (55.5%) across the four molecular breast cancer subtypes, confirming the lack of correlation between ER-beta and classical prognosticators. However, the relationships among the biopathological factors, analyzed by MCA, showed that ER-beta positivity is located in the quadrant containing more aggressive phenotypes such as HER2 and TN or ER-alpha/PgR/Bcl2- tumors. Kaplan-Meier curves and Cox regression analysis identified ER-beta as a significant discriminating factor for disease-free survival both in the node-negative LA (P = 0.02) subgroup, where it is predictive of response to HT, and in the node-positive LB (P = 0.04) group, where, in association with PgR negativity, it conveys a higher risk of relapse. Conclusion: Our data indicated that, in contrast to node-negative patients, in node-positive breast cancer patients, ER-beta positivity appears to be a biomarker related to a more aggressive clinical course. In this context, further investigations are necessary to better assess the role of the different ER-beta isoforms

A note on the dual scaling of dominance data and its relationship to correspondence analysis

Dual scaling of a subjects-by-objects table of dominance data (preferences, paired comparisons and successive categories data) has been contrasted with correspondence analysis, as if the two techniques were somehow different. In this note we show that dual scaling of dominance data is equivalent to the correspondence analysis of a table which is doubled with respect to subjects. We also show that the results of both methods can be recovered from a principal components analysis of the undoubled dominance table which is centred with respect to subject means

A note on the dual scaling of dominance data and its relationship to correspondence analysis

Dual scaling of a subjects-by-objects table of dominance data (preferences,paired comparisons and successive categories data) has been contrasted with correspondence analysis, as if the two techniques were somehow different. In this note we show that dual scaling of dominance data is equivalent to the correspondence analysis of a table which is doubled with respect to subjects. We also show that the results of both methods can be recovered from a principal components analysis of the undoubled dominance table which is centred with respect to subject means

A Note on the Dual Scaling of Dominance Data and its Relationship to Correspondence Analysis

Dual scaling of a subjects-by-objects table of dominance data (preferences, paired comparisons and successive categories data) has been contrasted with correspondence analysis, as if the two techniques were somehow dierent. In this note we show that dual scaling of dominance data is equivalent to the correspondence analysis of a table which is doubled with respect to subjects. We also show that the results of both methods can be recovered from a principal components analysis of the undoubled dominance table which is centred with respect to subject means. Journal of Economic Literature Classication: C19, C88 Keywords: Correspondence analysis, domin 2 1 Introduction Dual scaling (Nishisato 1980, 1994) is a multivariate method for assigning scale values to the rows and columns of a table of data, with certain optimal properties. Correspondence analysis (Benzecri 1973, Greenacre 1984, Lebart et al 1984) is a method for assigning optimal spatial positions to the rows and columns of a da..