Search

Hypothesis testing in contingency tables is usually based on asymptotic results, thereby restricting its proper use to large samples. To study these tests in small samples, we consider the likelihood ratio test (LRT) and define an accurate index for the celebrated hypotheses of homogeneity, independence, and Hardy-Weinberg equilibrium. The aim is to understand the use of the asymptotic results of the frequentist Likelihood Ratio Test and the Bayesian FBST (Full Bayesian Significance Test) under small-sample scenarios. The proposed exact LRT p-value is used as a benchmark to understand the other indices. We perform analysis in different scenarios, considering different sample sizes and different table dimensions. The conditional Fisher’s exact test for 2 × 2 tables and the Barnard’s exact test are also discussed. The main message of this paper is that all indices have very similar behavior, except for Fisher and Barnard tests that has a discrete behavior. The most powerful test was the asymptotic p-value from the likelihood ratio test, suggesting that is a good alternative for small sample sizes. [ABSTRACT FROM AUTHOR]

Researchers are increasingly using on line social networks to promote their work. Some authors have suggested that measuring social media activity can predict the impact of a primary study (i.e., whether or not an article will be highly cited). However, the influence of variables such as scientific quality, research disclosures, and journal characteristics on systematic reviews and meta-analyses has not yet been assessed. The present study aims to describe the effect of complex interactions between bibliometric factors and social media activity on the impact of systematic reviews and meta-analyses about psoriasis (PROSPERO 2016: CRD42016053181). Methodological quality was assessed using the Assessing the Methodological Quality of Systematic Reviews (AMSTAR) tool. Altmetrics, which consider Twitter, Facebook, and Google+ mention counts as well as Mendeley and SCOPUS readers, and corresponding article citation counts from Google Scholar were obtained for each article. Metadata and journal-related bibliometric indices were also obtained. One-hundred and sixty-four reviews with available altmetrics information were included in the final multifactorial analysis, which showed that social media and impact factor have less effect than Mendeley and SCOPUS readers on the number of cites that appear in Google Scholar. Although a journal’s impact factor predicted the number of tweets (OR, 1.202; 95% CI, 1.087–1.049), the years of publication and the number of Mendeley readers predicted the number of citations in Google Scholar (OR, 1.033; 95% CI, 1.018–1.329). Finally, methodological quality was related neither with bibliometric influence nor social media activity for systematic reviews. In conclusion, there seems to be a lack of connectivity between scientific quality, social media activity, and article usage, thus predicting scientific success based on these variables may be inappropriate in the particular case of systematic reviews. [ABSTRACT FROM AUTHOR]

Catch-per-unit-effort (CPUE) is often the main piece of information used in fisheries stock assessment; however, the catch and effort data that are traditionally compiled from commercial logbooks can be incomplete or unreliable due to many reasons. Pacific bluefin tuna (PBF) is a seasonal target species in the Taiwanese longline fishery. Since 2010, detailed catch information for each PBF has been made available through a catch documentation scheme. However, previously, only market landing data with a low coverage of logbooks were available. Therefore, several nontraditional procedures were performed to reconstruct catch and effort data from many alternative data sources not directly obtained from fishers for 2001–2015: (1) Estimating the catch number from the landing weight for 2001–2003, for which the catch number information was incomplete, based on Monte Carlo simulation; (2) deriving fishing days for 2007–2009 from voyage data recorder data, based on a newly developed algorithm; and (3) deriving fishing days for 2001–2006 from vessel trip information, based on linear relationships between fishing and at-sea days. Subsequently, generalized linear mixed models were developed with the delta-lognormal assumption for standardizing the CPUE calculated from the reconstructed data, and three-stage model evaluation was performed using (1) Akaike and Bayesian information criteria to determine the most favorable variable composition of standardization models, (2) overall R2 via cross-validation to compare fitting performance between area-separated and area-combined standardizations, and (3) system-based testing to explore the consistency of the standardized CPUEs with auxiliary data in the PBF stock assessment model. The last stage of evaluation revealed high consistency among the data, thus demonstrating improvements in data reconstruction for estimating the abundance index, and consequently the stock assessment. [ABSTRACT FROM AUTHOR]