1,720,984 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
ESTIMATES OF CANCER POPULATION ATTRIBUTABLE FRACTIONS FOR MULTIPLE RISK FACTORS FROM A NETWORK OF ITALIAN CASE-CONTROL STUDIES
Get PDFIntroduction. Attributable fraction (AF), proposed by Levin, quantifies the reduction in the disease prevalence that could be achieved by eliminating the exposure (or risk factor) of interest from the population. Disease etiology involves multiple risk factors that may act simultaneously in the occurrence of disease and the optimal approach to quantify the individual and the joint effects of different risk factors on the disease burden is one of the goals in epidemiological research. Adjusted AFs quantify the effect of one risk factor after controlling of other factors (i.e., risk factors that may act together to cause disease, adjustment variables or confounders). Adjusted AFs may add up more than the joint AF (i.e., the AF for eliminating all risk factors from the population) and in some situation may add up to more than 1, leading to the conclusion that adjusted AFs should not be used to the purpose of partitioning the joint effect into individual contributions. Eide and Gefeller proposed a way to accomplish this task. Sequential AFs quantify the additional effect of one risk factor on the disease risk after the preceding risk factors have already been removed in a specified order from the population. However, sequential AFs depend on the order in which risk factors are removed from the population. Average AFs overcome this shortcoming by averaging sequential AFs for a risk factor over all orders by which risk factors can be removed from the population. Average AFs quantify the additional effect of one risk factor on the disease risk after the preceding factors selected randomly have already been removed from the population.
Objective. This work aims to illustrate the main methodologies to estimate AFs and corresponding confidence intervals in presence of multiple risk factors with a focus on case-control study design. Moreover, we provide AF estimates for the major risk factors using Italian case-control data on oral cavity and breast cancers.
Modification of case-control study design. In the original notation, sequential and average AFs could not be used in case-control study design, since the ratio of controls to cases in the sample is fixed a priori and the resulting AF estimates will be biased. Ferguson et al. proposed a prevalence-based weighting approach to correct the imbalance between controls and cases. The method consists in weighting the likelihood function of the model used to estimate sequential and average AFs for the disease prevalence.
Variance estimation. The main approaches for estimating AF confidence intervals (CIs) are based on asymptotic approximation (Delta method) and simulations (Monte Carlo method). Ferguson proposed a method based on Monte Carlo simulations for constructing average AF variance. They also proposed the “averisk” R package for calculating average AFs and corresponding CIs in both prospective and case-control studies. In this work, we proposed a modification of the Ferguson’s method to account for sequential AF variability on the total variability.
Variances comparison. We compared our and Ferguson’s methods to estimate average AF variance using simulated data. We generate two classes of simulated dataset. Each class included four scenarios according to different correlation structure: from independence (scenario 1) to strong correlation among risk factors (scenario 4). The two classes differed in the prevalence and strength of the association between risk factors. In particular, the first class had a high prevalence and modest relative risks, whereas the second class had a low prevalence and huge relative risks.
For both classes of simulated data, standard deviation increment (i.e., the relative difference between our and Ferguson’s methods) became gradually larger increasing the number of independent risk factors (from two to ten). Conversely, standard deviation increment decreased incrementing the number of correlated risk factors. Although in some situations (i.e., for correlated risk factors) the contribution of our method could have a substantial relative impact on total AF variability (up to 88%), the absolute standard deviation differences between two methods were very small (less than 0.15) indicating a limited contribution of our method than the Feguson’s one.
Application to real data. We estimated average AFs using a case-control study conducted in Italy on 946 oral cavity cases and 2492 controls. Risk factors considered for AF estimation were smoking, alcohol drinking, red meat intake, vegetables intake, fruit intake, and family history of oral cavity cancer. The final model included also terms for sex, age, study centre, years of education, BMI, and non-alcohol energy intake to account for possible confounding effect. We set a prevalence of oral cavity cancer according to statistics from the consortium of Italian Cancer Registry (AIRTUM) to adjust average AFs for case-control data structure. Eighty-eight percent (95% CI: 78%; 98%) of oral cavity cases were attributable to the considered risk factors. In particular, the average AF for smoking was 0.34 (95% CI: 0.27; 0.41), indicating that 34% of oral cavity cases would not has occurred if smoking was randomly removed from the population over all possible risk factor removal orders. For the remaining risk factors, average AFs were 0.27 (95% CI: 0.17; 0.37) for alcohol drinking, 0.11 (95% CI: 0.06; 0.17) for low vegetables intake, 0.08 (95% CI: 0.02; 0.15) for low fruit intake, 0.06 (95% CI: 0.01; 0.12) for high red meat intake, and 0.009 (95% CI: -0.001; 0.02) for family history.
We analyzed a further case-control study on 2569 breast cancer cases and 2588 controls. We set a prevalence of breast cancer to adjust average AFs for case-control data structure. The final model included alcohol drinking, parity, breastfeeding, use of oral contraceptives (OCs), and family history of breast cancer as risk factors; study centre, age, years of education, smoking, age at menarche and use of hormonal replacement therapy (HRT) as adjusting factors. The joint AF was 0.49 (95% CI: 0.35; 0.63) indicating that approximately half of the breast cancer cases would not has occurred if all risk factors were simultaneously eliminated from the population. In particular, average AFs were 0.27 (95% CI: 0.16; 0.39) for parity, 0.12 (95% CI: 0.06; 0.18) for alcohol drinking, 0.04 (95% CI: -0.02; 0.10) for breastfeeding (No or <4 months), 0.04 (95% CI: 0.03; 0.06) for family history of breast cancer, and 0.01 (95% CI: -0.01; 0.03) for OCs users.
Conclusions. Sequential and average AFs are useful tools to apportion exposure-specific contributions in a population exposed to multiple risk factors. Sequential and average AFs share some mathematical properties such as component-additivity, symmetry, marginal rationality, and internal marginal rationality. Average AFs, however, do not represent the actual amount of disease ascribable for each risk factors because they assume that risk factors are removed from the population in a random order. Nevertheless, average AFs could be useful parameters to estimate the average burden of disease for each risk factors across all possible removal orders.
In this work, we proposed an alternative approach to estimate the average AF confidence interval accounting for sequential AF variability on the total AF one. We compared the performance between our and Fergusons’ methods to estimate AF variance. Although our method could have a relative impact on total AF variability, the absolute standard deviation differences suggest a limited contribution of our method. However, this topic should be further analyzed
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Attributable fraction for multiple risk factors: Methods, interpretations, and examples
No full textThe attributable fraction is the candidate tool to quantify individual shares of each risk factor on the disease burden in a population, expressing the proportion of cases ascribable to the risk factors. The original formula ignored the presence of other factors (i.e. multiple risk factors and/or confounders), and several adjusting methods for potential confounders have been proposed. However, crude and adjusted attributable fractions do not sum up to their joint attributable fraction (i.e. the number of cases attributable to all risk factors together) and their sum may exceed one. A different approach consists of partitioning the joint attributable fraction into exposure-specific shares leading to sequential and average attributable fractions. We provide an example using Italian case-control data on oral cavity cancer comparing crude, adjusted, sequential, and average attributable fractions for smoking and alcohol and provide an overview of the available software routines for their estimation. For each method, we give interpretation and discuss shortcomings. Crude and adjusted attributable fractions added up over than one, whereas sequential and average methods added up to the joint attributable fraction = 0.8112 (average attributable fractions for smoking and alcohol were 0.4894 and 0.3218, respectively). The attributable fraction is a well-known epidemiological measure that translates risk factors prevalence and disease occurrence in useful figures for a public health perspective. This work endorses their proper use and interpretation
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
- …
