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Searching for Stability as we Age: The PCA-Biplot Approach
Pages 255-262
Renata Noce Kirkwood, Scott C.E. Brandon, Bruno de Souza Moreira and Kevin J. Deluzio
DOI:
http://dx.doi.org/10.6000/1929-6029.2013.02.04.2
Published: 31 October 2013


Abstract: Principal component analysis (PCA) has been successfully applied to gait data; however, interpretation of the components is challenging. An alternative is to use a graphical display called biplot that gives insights into relationships and trends of data sets. Our goal was to demonstrate the sensitivity of gait variables to aging in elderly women with PCA-biplot. One hundred fifty-one elderly females (71.6±5.0 yrs), 152 adults (44.7±5.4 yrs) and 150 young (21.7±4.1 yrs) participated in the study. Gait spatial and temporal parameters were collected using a computerized carpet. PCA-biplot, discriminant analysis and MANOVA were used in the analysis. PCA-biplot revealed that elderly females walked with lower velocity, shorter step length, reduced swing time, higher cadence, and increased double support time compared to the other two groups. The greatest distances between the groups were along the variable step length with the elderly group showing a decrease of 8.4 cm in relation to the younger group. The discriminant function confirmed the importance of principal component 2 for group separation. Because principal component 2 was heavily weighted by step length and swing time, it represents a measure of stability. As women age they seek a more stable gait by decreasing step length, swing time, and velocity. PCA-biplot highlighted the importance of the variable step length in distinguishing between women of different age groups. It is well-known that as we age we seek a more stable gait. The PCA-biplot emphasized that premise and gave further important insights into relationships and trends of this complex data set.

Keywords: Gait, Principal Components Analysis, Biplot, Elderly, Balance, Step Length.
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Factors Affecting Self-Image in Patients with a Diagnosis of Eating Disorders on the Basis of a Cluster Analysis
Pages 2463-274
Maciej Wojciech Pilecki, Kinga Sałapa and Barbara Józefik
DOI:
http://dx.doi.org/10.6000/1929-6029.2013.02.04.3
Published: 31 October 2013Open Access


Abstract: The aim of this study was to assess the relationship between self-image in eating disorders and age, duration and severity of the disorder, comorbidity, depressiveness and self-evaluation of eating problems. The results of the Offer self-image questionnaire for adolescents (QSIA) were compared in four groups: anorexia nervosa restrictive subtype (ANR, n: 47), anorexia nervosa binge/purge subtype (ANBP, n: 16), bulimia nervosa (BUL, n: 34) and eating disorders NOS (EDNOS, n: 19). The control group was age matched female pupils (NOR, n = 76). The Kruskal-Wallis test revealed significant differences between the age of patients from the ANR (16.34, SD 1.58) and BUL (17.56, SD 0.96) groups (p = .008). The self-image of schoolgirls from the NOR group was on most scales significantly better than the self-image of girls from clinical groups. On four scales differences between the (better) self-image in the ANR group and that in the BUL group were observed. Next, a cluster analysis using a generalised k-means algorithm with v-fold cross validation of QSIA questionnaire results was conducted in the group of clinical eating disorders (ANR, ANBP, and BUL). Three clusters were obtained. The first was characterized by very good self-image (above the averagefor the general population), the second by poor self-image and the third by negative self-image. Severity of depressiveness measured using the Beck Depression Inventory turned out to be the only factor which differentiated the clusters of self-image in eating disorders.

Keywords: Anorexia, bulimia, QSIA, DATA MINING, cluster analysis.
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Long-Run Macroeconomic Determinants of Cancer Incidence
Pages 275-288
Fabrizio Ferretti, Simon Jones and Bryan McIntosh
DOI:
http://dx.doi.org/10.6000/1929-6029.2013.02.04.4
Published: 31 October 2013


Abstract: Background: Understanding how cancer incidence evolves during economic growth is useful for forecasting the economic impact of cancerous diseases, and for governing the process of resources allocation in planning health services. We analyse the relationship between economic growth and cancer incidence in order to describe and measure the influence of an increasing real per capita income on the overall rate of cancer incidence.

Method:We test the relationship between real per capita income and the overall rate of cancer incidence with a cross-sectional analysis, using data from the World Bank and the World Health Organization databases, for 165 countries in 2008. We measure the elasticity of cancer incidence with respect to per capita income, and we decompose the elasticities coefficients into two components: age-effect and lifestyle-effect.

Results: An Engel’s model, in a double-log quadratic specification, explains about half of the variations in the age-standardised rates and nearly two thirds of the variations in the incidence crude rates. All the elasticities of the crude rates are positive, but less than one. The income elasticity of the age-standardised rates are negative in lower income countries, and positive (around 0.25 and 0.32) in upper middle and high income countries, respectively.

Conclusions:These results are used to develop a basic framework in order to explain how demand-side economic structural changes may affect the long run evolution of cancer incidence. At theoretical level, a J-Curve is a possible general model to represents, other things being equal, how economic growth influence cancer incidence.

Keywords: Cancer Incidence, Economic Growth, Engel’s function, Income elasticity, Structural Change.
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Snapshot of Statistical Methods Used in Geriatric Cohort Studies: How Do We Treat Missing Data in Publications?
Pages 289-296
Diklah Geva, Danit Shahar, Tamara Harris, Sigal Tepper, Geert Molenberghs and Michael Friger
DOI:
http://dx.doi.org/10.6000/1929-6029.2013.02.04.5
Published: 31 October 2013Open Access


Abstract: Background: Geriatric studies often miss data of frail participants. The aim of this paper is to explore which missing data methodologies have entered current practice and to discuss the potential impact of ignoring the issue.

Methods: A Sample of 103 articles was drawn from key cohort studies: Health ABC, InCHIANTI, LASA, BLSA, EPESE, and KLoSHA. The studies wereclassified according to missing data methodologies used.

Results: Seventy-seven percent described the selected analysis data set and only 28% used a method of handling all available observations per case. Missing data dedicated methods were rare (< 10%), applying single or multiple imputations for baseline variables. Studies with longer follow-up periods more often employed longitudinal analysis methodologies.

Conclusions: Despite the recognition that missing data is a major problem in studies of older persons, few published studies account for missing data using limited methodologies; this could affect the validity of study conclusions. We propose researchers apply Joint Modeling of longitudinal and time-to-event data, using shared-parameter model.

Keywords: Missing data, geriatric cohort studies, methodologies review, longitudinal analysis.
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