1,720,969 research outputs found
Spatial modeling, covariate measurement error and design issues in environmental epidemiology
No full textUniversity of Technology Sydney. Faculty of Science.In this thesis we develop methods to resolve a series of problems motivated by the analysis of administrative data to help explain geographical variation in disease rates. The Conditional auto-regressive (CAR) structure within a hierarchical generalized linear model offers a robust, flexible, and popular class of models for the exploration and analysis of geographical variation across small areas. However, lack of modeling strategies for individual level covariate data is a limitation of the existing methodology. We propose an individual level covariate adjusted conditional auto-regressive (indiCAR) model to incorporate both individual and area level covariates while adjusting for spatial correlation in disease rates. We also extend the indiCAR method to a semiparametric mixed model framework that allows adjustment for smooth covariate effects (smooth-indiCAR). We illustrate the applicability of both methods in a distributed computing framework that enhances its application in the Big Data domain with a large number of individual/group level covariates involved. We evaluate the performance of indiCAR and smooth-indiCAR through simulation studies. Our results indicate that both methods provide reliable estimates of all the regression and random effect parameters. The estimated regression coefficient based on the CAR modeling, however, appears to be sensitive to the assumed spatial correlation structure. We hypothesize that such sensitivity is especially likely to occur when the covariate of interest has been measured with error. We quantify the biases of covariate measurement error, showing that the amount of attenuation depends on the degree of spatial correlation in both the covariate of interest and the assumed random error from the regression model. These results explain why the estimates obtained from spatial regression modeling are often so sensitive to the assumed model error structure. We propose and develop both a parametric and a semiparametric approach to obtain bias corrected estimate. Statistical analysis of administrative data often helps in uncovering trends and patterns that need to be followed up via traditional epidemiologic investigations. Case control studies are often the first choice. However, appropriate selection of controls and lack of power to detect interaction effect are the main concerns of a case control design. We propose a variant of the classical case-control design, the exposure enriched case-control (EECC) design, where not only cases, but also high (or low) exposed individuals are over-sampled, depending on the skewness of the exposure distribution. We show that the judicious oversampling of exposure is possible and can boost the study power particularly when susceptibility genes are rare and environmental exposure is highly skewed
A comparison of multiple imputation methods for missing data in longitudinal studies
Get PDFBackground
Multiple imputation (MI) is now widely used to handle missing data in longitudinal studies. Several MI techniques have been proposed to impute incomplete longitudinal covariates, including standard fully conditional specification (FCS-Standard) and joint multivariate normal imputation (JM-MVN), which treat repeated measurements as distinct variables, and various extensions based on generalized linear mixed models. Although these MI approaches have been implemented in various software packages, there has not been a comprehensive evaluation of the relative performance of these methods in the context of longitudinal data.
Method
Using both empirical data and a simulation study based on data from the six waves of the Longitudinal Study of Australian Children (N = 4661), we investigated the performance of a wide range of MI methods available in standard software packages for investigating the association between child body mass index (BMI) and quality of life using both a linear regression and a linear mixed-effects model.
Results
In this paper, we have identified and compared 12 different MI methods for imputing missing data in longitudinal studies. Analysis of simulated data under missing at random (MAR) mechanisms showed that the generally available MI methods provided less biased estimates with better coverage for the linear regression model and around half of these methods performed well for the estimation of regression parameters for a linear mixed model with random intercept. With the observed data, we observed an inverse association between child BMI and quality of life, with available data as well as multiple imputation.
Conclusion
Both FCS-Standard and JM-MVN performed well for the estimation of regression parameters in both analysis models. More complex methods that explicitly reflect the longitudinal structure for these analysis models may only be needed in specific circumstances such as irregularly spaced data
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
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
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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