Variants of Simple Correspondence Analysis

This paper presents a description of the R package CAvariants. It performs six variants of correspondence analysis on a two-way contingency table. The main function that shares the same name as the package - CAvariants - allows the user to choose (via a series of input parameters) from six different correspondence analysis procedures. These include the classical approach to (symmetrical) correspondence analysis, singly ordered correspondence analysis, doubly ordered correspondence analysis, non symmetrical correspondence analysis, singly ordered non symmetrical correspondence analysis and doubly ordered non symmetrical correspondence analysis. The code provides the flexibility for constructing either a classical correspondence plot or a biplot graphical display. It also allows the user to consider other important features that allow for one to assess the reliability of the graphical representations, such as the inclusion of algebraically derived elliptical confidence regions. This paper provides R functions that elaborates more fully on the code presented in Beh and Lombardo (2014)

Application of Correspondence Analysis to Graphically Investigate Associations Between Foods and Eating Locations.

This paper presents the application of correspondence analysis (CA) for investigating associations using confidence regions (CRs) with a focus on facilitating mining the data and hypothesis generation. We study the relationship between locations and "less-healthy" food consumption by UK teenagers. CA allows for a quick visual inspection of the various association structures that exist between the categories of cross-classified variables in large datasets derived with varying study designs. The hypotheses generated by the visual display can then be independently tested using suitable regression models. CA makes use of readily available software tools and of robust statistical tests amenable to interpretation

Can we use the approaches of ecological inference to learn about the potential for dependence bias in dualsystem estimation? An application to cancer registration data

The dual-system estimator, or estimators with a similar underlying set of assumptions and structure, is a widely used approach to estimate the unknown size of a population. Within official statistics its use is linked with population census, while in health applications it is often used to estimate true levels of incidence from imperfect reporting systems; the classic example being work by Sekar and Deming exploring the estimation of births in India in the 1940s. Critical to the implementation of dual-system estimation are the assumptions that the probability of being counted in a source is homogeneous and that the event of being counted in each source is independent. When either of these assumptions fails, the two by two table will have an odds ratio different to one and the dual-system estimator will be biased. Inferential frameworks such as the aggregate association index (AAI) have been developed to allow the researcher to assess the plausibility of independence between two variables in a two by two table, when only the margins are observed. Given any appropriate measure of relationship, this strategy relies on determining the AAI, which provides an indication of the likely association structure between the variables given only the marginal information. Further advances of the AAI have also been established including its link with the odds ratio and its relationship with the size of the study being undertaken. Determining the population size from a two by two table given limited information is an alternative variation of the framework on which the AAI is built. Therefore the underlying theoretical properties of the two by two table are identical in both scenarios – it is only the nature of the unknown information that differs. In this paper we make the first steps to exploring the use of an AAI type framework (and its relatives) to assess the plausibility of an independence assumption in applications of population size estimation. We use alternative data set-ups based on real data relating to historical cancer registration (with three sources of registration) to demonstrate that the chi-square statistic behaves differently over a range of values for the missing data for differing true relationships between the two variables. We then apply the approach to the cancer registration from two of the registration systems to show that we can see evidence of potential dependence from the observed but incomplete data. The first results in this paper demonstrate the possibility of exploring the independence assumption when estimating the unknown population size from two lists. As with the AAI framework, the aim is not to directly estimate the level of the association but rather alert the analyst to the potential for an association and its direction allowing them to assess the likelihood of a biased estimate for the population size. This has important implications within a health setting where it is potentially useful to understand if the true population size, of say cancer patients, is likely to be higher or lower than the estimate constructed assuming independence. Within the official statistics setting, it can alert us to situations where it is advantageous to explore whether external data exist that would allow an adjustment for dependence in our two lists

Application of correspondence analysis to graphically investigate associations between foods and eating locations

This paper presents the application of correspondence analysis (CA) for investigating associations using confidence regions (CRs) with a focus on facilitating mining the data and hypothesis generation. We study the relationship between locations and “less-healthy” food consumption by UK teenagers. CA allows for a quick visual inspection of the various association structures that exist between the categories of cross-classified variables in large datasets derived with varying study designs. The hypotheses generated by the visual display can then be independently tested using suitable regression models. CA makes use of readily available software tools and of robust statistical tests amenable to interpretation

Stress-induced c-Fos expression is differentially modulated by dexamethasone, diazepam and imipramine

Immobilization stress upregulates c-Fos expression in several CNS areas. Repeated stress or the use of drugs can modulate stress-induced c-Fos expression. Here, we investigated in 40 different areas of the rat brain the effects of dexamethasone (SDX, a synthetic glucocorticoid), diazepam (SBDZ, a benzodiazepine), and imipramine (IMI, an antidepressant) on the c-Fos expression induced by restraint stress. Wistar rats were divided into four groups and submitted to 20 days of daily injection of saline (three first groups) or imipramine, 15 mg/kg, i.p. On day 21, animals were submitted to injections of saline (somatosensory, SS), SDX (1 mg/kg, i.p.), SBDZ (5 mg/kg, i.p.), or IMI (15 mg/kg, i.p.) before being submitted to restraint. Immediately after stress, the animals were perfused and their brains processed with immunohistochemistry for c-Fos (Ab-5 Oncogene Science). Dexamethasone reduced stress- induced c-Fos expression in SS cortex, hippocampus, paraventricular nucleus of the hypothalamus (PVH), and locus coeruleus (LC), whereas diazepam reduced c-Fos staining in the SS cortex, hippocampus, bed nucleus of stria terminalis, septal area, and hypothalamus (preoptic area and supramammillary nucleus). Chronic administration of imipramine decreased staining in the hippocampus, PVH, and LC, while increasing it in the nucleus raphe pallidus. We conclude that dexamethasone, diazepam and imipramine differentially modulate stress-induced Fos expression. the present study provides an important comparative background that may help in the further understanding of the effects of these compounds and on the brain activation as well as on the behavioral, neuroendocrine, and autonomic responses to stress.UFRRJ, Dept Physiol Sci, BR-23890000 Rio de Janeiro, BrazilUniversidade Federal de São Paulo, Dept Physiol, São Paulo, BrazilUniversidade Federal de São Paulo, Dept Psychobiol, São Paulo, BrazilUniversidade Federal de São Paulo, Dept Physiol, São Paulo, BrazilUniversidade Federal de São Paulo, Dept Psychobiol, São Paulo, BrazilWeb of Scienc

How do we know that research ethics committees are really working? The neglected role of outcomes assessment in research ethics review

<p>Abstract</p> <p>Background</p> <p>Countries are increasingly devoting significant resources to creating or strengthening research ethics committees, but there has been insufficient attention to assessing whether these committees are actually improving the protection of human research participants.</p> <p>Discussion</p> <p>Research ethics committees face numerous obstacles to achieving their goal of improving research participant protection. These include the inherently amorphous nature of ethics review, the tendency of regulatory systems to encourage a focus on form over substance, financial and resource constraints, and conflicts of interest. Auditing and accreditation programs can improve the quality of ethics review by encouraging the development of standardized policies and procedures, promoting a common base of knowledge, and enhancing the status of research ethics committees within their own institutions. However, these mechanisms focus largely on questions of structure and process and are therefore incapable of answering many critical questions about ethics committees' actual impact on research practices.</p> <p>The first step in determining whether research ethics committees are achieving their intended function is to identify what prospective research participants and their communities hope to get out of the ethics review process. Answers to this question can help guide the development of effective outcomes assessment measures. It is also important to determine whether research ethics committees' guidance to investigators is actually being followed. Finally, the information developed through outcomes assessment must be disseminated to key decision-makers and incorporated into practice. This article offers concrete suggestions for achieving these goals.</p> <p>Conclusion</p> <p>Outcomes assessment of research ethics committees should address the following questions: First, does research ethics committee review improve participants' understanding of the risks and potential benefits of studies? Second, does the process affect prospective participants' decisions about whether to participate in research? Third, does it change participants' subjective experiences in studies or their attitudes about research? Fourth, does it reduce the riskiness of research? Fifth, does it result in more research responsive to the local community's self-identified needs? Sixth, is research ethics committees' guidance to researchers actually being followed?</p

Multiple Correspondence Analysis via Polynomial Transformations of Ordered Categorical Variables

Multiple correspondence analysis, Ordered categorical variables, Singular value decomposition, Bivariate moment decomposition, Hybrid decomposition, Automatic clustering of objects,

Modelling Trends in Ordered Correspondence Analysis using Orthogonal Polynomials

The core of the paper consists of the treatment of two special decompositions for correspondence analysis of two-way ordered contingency tables: the bivariate moment decomposition and the hybrid decomposition, both using orthogonal polynomials rather than the commonly used singular vectors. To this end, we will detail and explain the basic characteristics of a particular set of orthogonal polynomials, called Emerson polynomials. It is shown that such polynomials, when used as bases for the row and/or column spaces, can enhance the interpretations via linear, quadratic and higher-order moments of the ordered categories. To aid such interpretations, we propose a new type of graphical display-the <i>polynomial biplot</i>