1,721,028 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe 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
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Estimating Causal Effects in Pragmatic Settings With Imperfect Information
No full textPrecision medicine seeks to identify the optimal treatment for each individual based on his or her unique features. This invariably involves some form of estimation of causal effects for different patient subgroups to determine the treatment that leads to superior outcomes. Implementing methods to estimate causal effects in modern large and rich data sources such as electronic medical records (EMR), however, still faces challenges as information on patients is imperfectly captured in the observed data. In this work, we propose approaches to address some of the primary issues encountered in estimating causal effects in these pragmatic settings.
In Chapter 1, we consider estimating average treatment effects (ATE) in observational data where the number of covariates is not small relative to the sample size. We develop a double-index propensity score (DiPS) obtained by smoothing treatment over linear predictors for the covariates from initial working parametric propensity score (PS) and outcome models fit with regularization. We show that an inverse probability weighting (IPW) estimator based on DiPS maintains the doubly-robustness and local semiparametric efficiency properties of the usual doubly-robust estimator and achieves further gains in robustness and efficiency under model misspecification. Simulations demonstrate the benefit of the approach in finite samples, and the method is illustrated by applications estimating the effects of statins on colorectal cancer risk and smoking on C-reactive protein.
In Chapter 2, we extend the work from Chapter 1 to allow for incorporation of a large set of unlabeled data. This arises in EMR data when chart review is performed to ascertain gold-standard outcomes in case outcomes of interest are not directly observed. We frame the problem in a semi-supervised learning setting, where a small set of observations are labeled and a large set of observations are unlabeled but includes features predictive of the outcome. We develop an imputation followed by IPW approach that is robust to misspecification of the imputation model. The estimator is also doubly-robust and efficient under an ideal semi-supervised model where the distribution of the unlabeled data is known. We demonstrate the robustness and efficiency of the approach through simulations and an application to compare rates of response to biologic therapies among inflammatory bowel disease patients.
In Chapter 3, we turn to the problem of identifying interpretable treatment subgroups. Although many statistical and machine learning approaches have been developed to discriminate patients exhibiting enhanced treatment effects, many produce output that are difficult to interpret for clinicians. Tree-based methods are a natural way of producing interpretable output but are typically not competitive in discriminative performance. We consider adapting the method of ``born-again'' trees (Breiman and Shang, 1996) for subgroup identification to balance interpretability and performance by re-approximating flexible initial estimators for the conditional average treatment effect (CATE). The approach is applied to data from two large phase 3 trials evaluating the effect of oral fumarate for preventing relapses among patients with multiple sclerosis.
In Chapter 4, we further consider estimating CATE when both randomized and observational data are simultaneously observed. Observational estimates could potentially be combined with randomized estimates to improve efficiency, but there may be concerns about whether confounding and treatment effect heterogeneity have been adequately addressed. We propose a combination approach that always yields an estimator consistent for a conditional causal effect. It weights heavily towards the randomized estimator in case bias in the OS estimator is detected or else combines the estimators for optimal efficiency. We show the weights can be estimated through a penalized least square criteria. The performance of the weights are evaluated through simulations, and we illustrate the method by estimating effects of hormone therapy on coronary heart disease in data from the Women's Health Initiative.Biostatistic
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Robust and Efficient Machine Learning Methods for the Analysis of Electronic Medical Records Data
No full textIn the last decade, electronic medical records (EMR) have emerged as a powerful tool to store and process health data worldwide. Though primarily implemented to improve the quality of patient care, EMR have simultaneously generated a promising data source for clinical and translational research, particularly when linked to specimen bio-repositories. However, much of the data stored in routine practice is difficult to make use of in secondary applications. The first step in recycling EMR data for research, identifying patients with specific diseases of interest or so-called phenotyping, has proven to be especially challenging due to the time intensiveness of obtaining validated disease status information. Typically, gold standard phenotype labels obtained from manual chart review are only available for a small training set nested in a large cohort. In contrast, information on a large number of clinical predictors of the phenotype are available for all subjects. To improve the robustness and efficiency of phenotyping, this thesis proposes semi-supervised learning (SSL) methods that fully leverage the auxiliary information contained in the predictors as well as an unsupervised feature selection method that does not rely on any gold standard labels. Chapter 1 proposes a semi-supervised approach for efficient evaluation of prediction performance measures for a binary classifier. In Chapters 2 and 3, I extend the SSL paradigm to settings where the gold standard labels are not randomly selected from the underlying pool of data as is typically assumed in the SSL literature in the context of estimating and evaluating prediction rules. I conclude with Chapter 4 where I introduce a feature selection procedure based entirely on unlabeled data.Biostatistic
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