1,720,971 research outputs found
Approximate likelihood and pseudo‐likelihood inference in meta‐analysis of diagnostic accuracy studies accounting for disease prevalence and study design
Full text linkBivariate random-effects models represent a recommended approach for meta-analysis of diagnostic test accuracy, jointly modeling study-specific sensitivity and specificity. As the severity of the disease status can vary across studies, a proper analysis should account for the dependence of the accuracy measures on the disease prevalence. To this aim, trivariate generalized linear mixed-effects models have been proposed in the literature, although computational difficulties strongly limit their applicability. In addition, the attention has been mainly paid to cohort studies, where the study-specific disease prevalence can be estimated from, while information from case-control studies is often neglected. To overcome such limits, this article introduces a trivariate approximate normal model, which accounts for disease prevalence along with accuracy measures in cohort studies and sensitivity and specificity in case-control studies. The model represents an extension of the bivariate normal mixed-effects model originally developed for meta-analysis not accounting for disease prevalence, under an approximate normal within-study distribution for the logit of estimated sensitivity and specificity. The components of the approximate within-study covariance matrix are derived and the likelihood function is obtained in closed-form. The approximate likelihood approach is compared to that based on the exact within-study distribution and to its modifications following a pseudo-likelihood strategy aimed at reducing the computational effort. The comparison is based on simulation studies in a variety of scenarios, and illustrated in a meta-analysis about the accuracy of a test to diagnose fungal infection and a meta-analysis of a noninvasive test to detect colorectal cancer
Modeling the Role of Baseline Risk and Additional Study-Level Covariates in Meta-Analysis of Treatment Effects
Full text link: The relationship between the treatment effect and the baseline risk is a recognized tool to investigate the heterogeneity of treatment effects in meta-analyses of clinical trials. Since the baseline risk is difficult to measure, a proxy is adopted, which is based on the rate of events for the subject under the control condition. The use of the proxy in terms of aggregated information at the study level implies that the data are affected by measurement errors, a problem that the literature has explored and addressed in recent years. This paper proposes an extension of the classical meta-analysis with baseline risk information, which includes additional study-specific covariates other than the rate of events to explain heterogeneity. Likelihood-based inference is carried out by including measurement error correction techniques necessary to prevent unreliable inference due to the measurement errors affecting the covariates summarized at the study level. Within-study covariances between risk measures and the covariate components are computed using Taylor expansions based on study-level covariate subgroup summary information. When such information is not available and, more generally, in order to reduce computational difficulties, a pseudo-likelihood solution is developed under a working independence assumption between the observed error-prone measures. The performance of the methods is investigated in a series of simulation studies under different specifications for the sample size, the between-study heterogeneity, and the underlying risk distribution. They are applied to a meta-analysis about the association between COVID-19 and schizophrenia
Marginal Beta Regression for Time Series Analysis
No full textA marginal beta regression model with autoregressive and moving average errors is developed for the analysis of time series of values in the standard unit interval (0,1), such as proportions and rates. The dependence structure is conveniently related to the marginal model through a Gaussian copula specification. Likelihood inference, model validation via residual analysis, and prediction are briefly discussed. The methodology is applied to the time series of the rate of hidden unemployment in S ̃ao Paulo, Brazil
metaLik: Likelihood inference in meta-analysis and meta-regression models
No full textFirst- and higher-order likelihood inference in meta-analysis and meta-regression models
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
Beta regression for time series analysis of bounded data, with application to Canada Google Flu Trends.
No full textBounded time series consisting of rates or proportions are often encountered in applications. This manuscript proposes a practical approach to analyze bounded time series, through a beta regression model. The method allows the direct interpretation of the regression parameters on the original response scale, while properly accounting for the heteroskedasticity typical of bounded variables. The serial dependence is modeled by a Gaussian copula, with a correlation matrix corresponding to a stationary autoregressive and moving average process. It is shown that inference, prediction, and control can be carried out straightforwardly, with minor modifications to standard analysis of autoregressive and moving average models. The methodology is motivated by an application to the influenza-like-illness incidence estimated by the Google® Flu Trends project
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