Different Measures Of Association Research Articles

Metabolic syndrome and cognition: A systematic review across cognitive domains and a bibliometric analysis

The aim of this review is to investigate the association between metabolic syndrome (MetS) and cognitive decline in distinct cognitive domains, and to perform a complementary study description through the bibliometric analysis. PubMed and Scopus databases were searched from inception to 15 December 2021 to identify longitudinal studies that examined the association of MetS with incident decline, in order to prevent reverse causality. The Preferred Reporting Items for Systematic Review and Meta-Analysis checklist was used to conduct the present systematic review. Thirty studies were included and results were analyzed across the cognitive domains of global cognition, memory, executive functions, attention, visuoconstructive abilities, and language. The majority of the studies reviewed did not report statistically significant results for most cognitive domains investigated, and decline in specific cognitive domains was not consistently associated with the presence of MetS. Meta-analyses were not conducted due to the high degree of between-study heterogeneity regarding the MetS definitions, the cognitive domains examined, the specific tests used for each cognitive domain and the different measures of association used. Bibliometric analysis revealed that most studies are conducted by research teams from USA and China, and that cognitive tasks that reflect real-life abilities are rarely examined. Future studies should employ larger sample sizes, longer follow-up periods, a global consensus for MetS definition and standardized tests of the above mentioned cognitive domains as well as problem-solving tasks with high sensitivity and specificity to clarify the impact of MetS on cognition and its underlying mechanisms.

Intercultural Approach in Foreign Language Teaching and Formation of Cultural Mediators: A Corpus Analysis of the Adjective polite in French and Polish

This study aims to analyse the notion of politeness through the collocations of the adjective polite and its Polish equivalents, i.e. grzeczny and uprzejmy, chosen as cultural keywords in order to verify and expand the conclusions of my previous research on the noun politeness. The study has a double perspective. It is based on a linguistic corpus-based analysis of French politeness in a contrastive approach to French and Polish data. Also, the study considers ways of integrating such data into foreign language teaching. The research is carried out on three corpora: the French language corpus Frantext, the National Polish Language Corpus (NKJP) and the Leipzig Corpora Collection. The study includes the analysis of frequency classes of the three words and their most frequent collocations identified on the basis of different measures of association between co-occurring words. By integrating the results of this linguistic approach into language teaching, it is possible to clarify certain semantic subtleties of the words polite, grzeczny and uprzejmy and explain to the learners why these words are not perfect equivalents, or how linguistic analysis reveals cultural differences. Complex approach promoted in this paper may contribute to the development of the intercultural awareness in foreign language teaching and cultural mediation.

Measuring variability and association for categorical data

The quantification of the variability of the categorical data is an important topic not only in statistics, but also in many other disciplines. We suggest different variability measures to describe the variability of categorical data. In our approach, any set of categorical data for a determinate categorical variable is treated as a fuzzy set. Therefore, measuring the variability of categorical data is the same as measuring its fuzziness. Different measures of association between two categorical variables are also proposed. The measures can be easily applied to categoric random variables.

Tracking sickness effects on social encounters via continuous proximity sensing in wild vampire bats

Abstract Sickness behaviors can slow the spread of pathogens across a social network. We conducted a field experiment to investigate how sickness behavior affects individual connectedness over time using a dynamic social network created from high-resolution proximity data. After capturing adult female vampire bats (Desmodus rotundus) from a roost, we created “sick” bats by injecting a random half of bats with the immune-challenging substance, lipopolysaccharide, while the control group received saline injections. Over the next 3 days, we used proximity sensors to continuously track dyadic associations between 16 “sick” bats and 15 control bats under natural conditions. Compared to control bats, “sick” bats associated with fewer bats, spent less time near others, and were less socially connected to more well-connected individuals (sick bats had on average a lower degree, strength, and eigenvector centrality). High-resolution proximity data allow researchers to flexibly define network connections (association rates) based on how a particular pathogen is transmitted (e.g., contact duration of >1 vs. >60 min, contact proximity of <1 vs. <10 m). Therefore, we inspected how different ways of measuring association rates changed the observed effect of LPS. How researchers define association rates influences the magnitude and detectability of sickness effects on network centrality. When animals are sick, they often encounter fewer individuals. We tracked this unintentional “social distancing” effect hour-by-hour in a wild colony of vampire bats. Using bat-borne proximity sensors, we compared changes in the social network connectedness of immune-challenged “sick” bats versus “control” bats over time. “Sick” bats had fewer encounters with others and spent less time near others. Associations changed dramatically by time of day, and different measures of association influenced the sickness effect estimates.

Measuring Variability and Association for Categorical Data

The quantification of the variability of the categorical data is an important topic not only in statistics, but also in many other disciplines. We suggest different variability measures to describe the variability of categorical data. In our approach, any set of categorical data for a determinate categorical variable is treated as a fuzzy set. Therefore, measuring the variability of categorical data is the same as measuring its fuzziness. Different measures of association between two categorical variables are also proposed. The measures can be easily applied to multinomial random variables.

Modelo Bivariado para las Pruebas Saber11 en el Departamento Del Tolima (Colombia)

En muchas aplicaciones encontramos datos que se restringen al intervalo (0;1), tales como porcentajes y proporciones, y que además pueden ser explicados por otras variables a través de un modelo de regresión en el que la variable respuesta está distribuida como una beta. Por otro lado, se encuentran pares de variables que tienen cierta dependencia como es el caso del rendimiento en matemáticas y lectura crítica dados en las pruebas saber11 en el departamento del Tolima (Colombia) 2016. La teoría de las funciones cópula surgen como una alternativa para medir la dependencia de variables aleatorias con distribuciones marginales dadas, permitiendo estimar diferentes medidas de asociación y diferentes métodos de estimación. En este artículo se usó un Modelo Bivariado bajo el contexto de las funciones Cópula para datos que están en el intervalo (0;1). Se verificaron las propiedades de los modelos ajustados y se compararon diferentes métodos de estimaciones usando el paquete Cópula y VineCopula del software R con el fin de establecer cuál es el mejor. Se usaron datos simulados para realizar este proceso y se aplicaron los modelos a datos reales de rendimiento en lectura crítica y matemáticas en estudiantes entre los 14 a 24 años.

Smoking and heavy drinking patterns in rural, urban and rural-to-urban migrants: the PERU MIGRANT Study

BackgroundPrevious studies have found mixed results about cigarette and alcohol consumption patterns among rural-to-urban migrants. Moreover, there are limited longitudinal data about consumption patterns in this population. As such, this study aimed to compare the smoking and heavy drinking prevalence among rural, urban, and rural-to-urban migrants in Peru, as well as the smoking and heavy drinking incidence in a 5-year follow-up.MethodsWe analyzed the PERU MIGRANT Study data from rural, urban, and rural-to-urban migrant populations in Peru. The baseline study was carried out in 2006–2007 and follow-up was performed five years later. For the baseline data analysis, the prevalence of lifetime smoking, current smokers, and heavy drinking was compared by population group using prevalence ratios (PR) and 95% confidence intervals (95% CI). For the longitudinal analysis, the incidence of smoking and heavy drinking was compared by population group with risk ratios (RR) and 95% CI. Poisson regression with robust variance was used to calculate both PRs and RRs.ResultsWe analyzed data from 988 participants: 200 rural dwellers, 589 migrants, and 199 urban dwellers. Compared with migrants, lifetime smoking prevalence was higher in the urban group (PR = 2.29, 95% CI = 1.64–3.20), but lower in the rural group (PR = 0.55, 95% CI = 0.31–0.99). Compared with migrants, the urban group had a higher current smoking prevalence (PR = 2.29, 95% CI = 1.26–4.16), and a higher smoking incidence (RR = 2.75, 95% CI = 1.03–7.34). Current smoking prevalence and smoking incidence showed no significant difference between rural and migrant groups. The prevalence and incidence of heavy drinking was similar across the three population groups.ConclusionsOur results show a trend in lifetime smoking prevalence (urban > migrant > rural), while smoking incidence was similar between migrant and rural groups, but higher in the urban group. In addition, our results suggest that different definitions of smoking status could lead to different smoking rates and potentially different measures of association. The prevalence and incidence of heavy drinking were similar between the three population groups.

Shedding Light on the Etiology of Sports Injuries: A Look Behind the Scenes of Time-to-Event Analyses

The etiological mechanism underpinning any sports-related injury is complex and multifactorial. Frequently, athletes perceive "excessive training" as the principal factor in their injury, an observation that is biologically plausible yet somewhat ambiguous. If the applied training load is suddenly increased, this may increase the risk for sports injury development, irrespective of the absolute amount of training. Indeed, little to no rigorous scientific evidence exists to support the hypothesis that fluctuations in training load, compared to absolute training load, are more important in explaining sports injury development. One reason for this could be that prospective data from scientific studies should be analyzed in a different manner. Time-to-event analysis is a useful statistical tool in which to analyze the influence of changing exposures on injury risk. However, the potential of time-to-event analysis remains insufficiently exploited in sports injury research. Therefore, the purpose of the present article was to present and discuss measures of association used in time-to-event analyses and to present the advanced concept of time-varying exposures and outcomes. In the paper, different measures of association, such as cumulative relative risk, cumulative risk difference, and the classical hazard rate ratio, are presented in a nontechnical manner, and suggestions for interpretation of study results are provided. To summarize, time-to-event analysis complements the statistical arsenal of sports injury prevention researchers, because it enables them to analyze the complex and highly dynamic reality of injury etiology, injury recurrence, and time to recovery across a range of sporting contexts.

Natural vs. random protein sequences: Discovering combinatorics properties on amino acid words

Casual mutations and natural selection have driven the evolution of protein amino acid sequences that we observe at present in nature. The question about which is the dominant force of proteins evolution is still lacking of an unambiguous answer. Casual mutations tend to randomize protein sequences while, in order to have the correct functionality, one expects that selection mechanisms impose rigid constraints on amino acid sequences. Moreover, one also has to consider that the space of all possible amino acid sequences is so astonishingly large that it could be reasonable to have a well tuned amino acid sequence indistinguishable from a random one.In order to study the possibility to discriminate between random and natural amino acid sequences, we introduce different measures of association between pairs of amino acids in a sequence, and apply them to a dataset of 1047 natural protein sequences and 10,470 random sequences, carefully generated in order to preserve the relative length and amino acid distribution of the natural proteins. We analyze the multidimensional measures with machine learning techniques and show that, to a reasonable extent, natural protein sequences can be differentiated from random ones.

On multivariate network analysis of statistical data sets with different measures of association

The main goal of the present paper is the development of a general framework of multivariate network analysis of statistical data sets. A general method of multivariate network construction, on the basis of measures of association, is proposed. In this paper we consider Pearson correlation network, sign similarity network, Fechner correlation network, Kruskal correlation network, Kendall correlation network, and the Spearman correlation network. The problem of identification of the threshold graph in these networks is discussed. Different multiple decision statistical procedures are proposed. It is shown that a statistical procedure used for threshold graph identification in one network can be efficiently used for any other network. Our approach allows us to obtain statistical procedures with desired properties for any network.

Covariate Effect on Constancy Assumption in Noninferiority Clinical Trials

Noninferiority (NI) clinical trials are getting a lot of attention of late due to their direct application in biosimilar studies. Because of the missing placebo arm, NI is an indirect approach to demonstrate efficacy of a test treatment. One of the key assumptions in the NI test is the constancy assumption, that is, that the effect of the reference treatment is the same in current NI trials as in historical superiority trials. However, if a covariate interacts with the treatment arms, then changes in distribution of this covariate will likely result in violation of constancy assumption. In this article, we propose four new NI methods and compare them with two existing methods to evaluate the change of background constancy assumption on the performance of these six methods. To achieve this goal, we study the impact of three elements—(1) strength of covariate, (2) degree of interaction between covariate and treatment, and (3) differences in distribution of the covariate between historical and current trials—on both the type I error rate and power using three different measures of association: difference, log relative risk, and log odds ratio. Based on this research, we recommend using a modified covariate-adjustment fixed margin method.

On the Performance Evaluation of Different Measures of Association

In this article our objective is to evaluate the performance of different measures of associations for hypothesis testing purposes. We have considered different measures of association (including some commonly used) in this study, one of which is parametric and others are non-parametric including three proposed modifications. Performance of these tests are compared under different symmetric, skewed and contaminated probability distributions that include Normal, Cauchy, Uniform, Laplace, Lognormal, Exponential, Weibull, Gamma, t, Chi-square, Half Normal, Mixed Weibull and Mixed Normal. Performances of these tests are measured in terms of power. We have suggested appropriate tests which may perform better under different situations based on their efficiency grading(s). It is expected that researchers will find these results useful in decision making.

Modelling Correlated Clinical Outcomes in Health Technology Appraisal

ObjectivesMany clinical treatments have multiple effects that can only be effectively captured on multiple outcome scales. It might be important to understand how these outcomes are correlated to evaluate the effectiveness and cost-effectiveness of treatments in decision models. MethodsThe probabilities are estimated that both, one, or neither outcome occurs, given estimates of the marginal probability for each outcome and information about the correlation between them. Methods are shown for different measures of association. Lower and upper bounds for the correlation coefficient are calculated for given values of the marginal probabilities. The approach is illustrated using a simplified decision model based on a recent evaluation of adalimumab, a biologic drug for psoriatic arthritis. ResultsAssuming the outcomes are positively correlated, the probability of both a skin and arthritis response after adalimumab was estimated to be 0.387 (95% confidence interval 0.210–0.570). The incremental cost-effectiveness ratio (ICER) of adalimumab versus no biologic is £18,500 per quality-adjusted life-year (QALY). The ICER increases to £19,500 per QALY if the responses are independent. ConclusionEstimates of ICERs can be sensitive to assumptions about how multiple outcomes are correlated. These assumptions should be explored in univariate and probabilistic sensitivity analyses.

Measuring the Dependence between Non-Gaussian Financial Assets Using Copulae: Risk Management, Option Pricing and Default Risk

This paper aims to provide an introductory review of copulae and their potential application finance, in particular in capturing the dependence between financial assets that follow non-gaussian distributions and hence for modelling credit risk, pricing options and portfolio design. We briefy review the failure of methods based on multivariate normality and where dependency is measured by correlation. Using a copula approach enables us to measure the different relationships that may exist between financial assets in different ranges of their behaviour- for instance do assets exhibit similar dependency patterns in the tails of their distributions as they do around their means? We review different measures of association and show that when these dependency measures can be expressed simply as functions of a copula they will be invariant to strictly monotone transformations of the random variables and hence the units in which we choose to express our data. The standard Pearson Correlation statistic is not in general invariant to scale changes in the data. We then discuss several statistical issues relating to the estimation of Copulae and their empirical application through both parametric and non parametric methods. An important issue lies in the statistical discrimination between copulae and we propose a encompassing framework based on simulation for discriminating between what are effectively separate statistical families. We then consider several applications, modelling default risk, tail dependence, quantile regression and portfolio design for non-gaussian assets.

Symmetry and dependence properties within a semiparametric family of bivariate copulas

In this paper, we study a semiparametric family of bivariate copulas. The family is generated by a univariate function, determining the symmetry (radial symmetry, joint symmetry) and dependence property (quadrant dependence, total positivity, \\ldots ) of the copulas. We provide bounds on different measures of association (such as Kendall's Tau, Spearman's Rho) for this family and several choices of generating functions for which these bounds can be reached.

Child-care attendance and common morbidity: evidence of association in the literature and questions of design.

Papers on child-care attendance as a risk factor for acute respiratory infections and diarrhea were reviewed. There was great variety among the studies with regard to the design, definition of exposure and definition of outcomes. All the traditional epidemiological study designs have been used. The studies varied in terms of how child-care attendance in general was defined, and for different settings. These definitions differed especially in relation to the minimum time of attendance required. The outcomes were also defined and measured in several different ways. The analyses performed were not always appropriate, leading to sets of results of uneven quality, and composed of different measures of association relating different exposures and outcomes, that made summarizing difficult. Despite that, the results reported were remarkably consistent. Only two of the papers reviewed failed to show some association between child-care attendance and increased acute respiratory infections, or diarrhea. On the other hand, the magnitude of the associations reported varied widely, especially for lower respiratory infections. Taken together, the studies so far published provide evidence that children attending child-care centers, especially those under three years of age, are at a higher risk of upper respiratory infections, lower respiratory infections, and diarrhea. The studies were not consistent, however, in relation to attendance at child-care homes. Children in such settings were sometimes similar to those in child-care centers, sometimes similar to those cared for at home, and sometimes presented an intermediate risk.

Using a geographic information system (GIS) to associate forest stand characteristics with top kill due to defoliation by the jack pine budworm

The occurrence and spatial distribution of top kill 1 year following a jack pine budworm (Choristoneura pinus pinus Freeman) outbreak were examined for possible associations with forest stand characteristics derived from resource inventory maps. Associations were computed between top-kill severity and jack pine (Pinus banksiana Lamb.) maturity, stand height, crown closure, and site quality. Three different measures of association with data from a geographic information system (GIS) (Cramer's V, Spearman's nonparametric rank correlation, and Minnick's coefficient of areal determination) were used to assess the strength of these associations. There were no statistical differences among average top-kill lengths, which averaged 2 m, among the light, moderate, and severe top-kill categories. The proportion of trees that experienced top kill ranged from 55% in the light to 61% in the severe top-kill classes. Site quality and stand maturity were more highly associated with top kill than stand height and crown closure. Jack pine areas that sustained moderate and severe top kill were those on poor sites that were overmature (> 85 years), 15-20 m tall with 30-55% crown closure. The GIS approach can be a useful tool for identifying vulnerable stands, and increasingly so as forest inventory classification systems become more detailed.

Validity and Reliability of Subjective Social Indicators: The effect of different measures of association

In an international research project, a meta analysis of multitrait-multimethod matrices was used to evaluate the effects of characteristics of survey measurement instruments on the quality of survey data. The analyses in this project were done on the basis of polychoric/polyserial correlations. The result of such a study can be used to correct for measurement errors if the correlation in the substantive study is also a polychoric/polyserial correlation. It is, however, doubtful if these quality estimates of survey measurement instruments are appropriate for studies based on the most frequently used Pearson correlations. In this paper, the general approach will be discussed first. After that, the results with respect to validity and reliability estimates, obtained using two different measures of association, will be compared: the polychoric/polyserial correlations and the Pearson correlations. This study suggests that the differences between estimates of the reliability for scales with different numbers of categories are quite large, while all other effects remain approximately the same. The reasons for the differences will also be given. All the results are based on experiments containing questions with respect to satisfaction with life and aspects of life. Thus the study provides estimates of validity and reliability on measures of satisfaction with life and aspects of life which can be used in practice for the prediction and optimization of data quality and for correction of the data with regard to measurement error.

Deriving relative risks from aggregate data. 1. Theory.

Sociological macro analyses of the association between risk factors and mortality can be seen as a valuable supplement to epidemiological micro studies. However, sociologists and epidemiologists typically employ different measures of association and this hampers strict comparisons of findings. This study presents a synthetic approach relying on both micro and macro data. In Part 1, the mathematical relations between the relative risk and the attributable fraction on the one hand, and the regression coefficient on the other are derived in order to make cross level comparisons possible. Part 2 provides an empirical illustration of the approach.

A technique for evaluating automatic term clustering

AbstractThis study reexamines the feasibility of automatic thesaurus generation by establishing a framework for analyzing the degree of correspondence between automatically generated thesauri and a thesaurus of the same terms constructed by a team of subject specialists. In order to test the evaluative procedure, a series of thesauri are generated using the same clustering algorithm and three different measures of association. By comparing the relative effectiveness of the generated thesauri to the human standard, it is determined that substantial differences exist among them.