Two Examples of Bayesian Evidence Synthesis with the Hierarchical Meta-Regression Approach

This is the Information Age. We can expect that for a particular research question that is empirically testable, we should have a collection of evidence which indicates the best way to proceed. Unfortunately, this is not the case in several areas of empirical research and decision making. Instead, when researchers and policy makers ask a specific question, such as “What is the effectiveness of a new treatment?”, the structure of the evidence available to answer this question may be complex and fragmented (e.g. published experiments may have different grades of quality, observational data, subjective judgments, etc.)

Cardiovascular Consequences of Unfair Pay

This paper investigates physiological responses to perceptions of unfair pay. In a simple principal agent experiment agents produce revenue by working on a tedious task. Principals decide how this revenue is allocated between themselves and their agents. In this environment unfairness can arise if an agent's reward expectation is not met. Throughout the experiment we record agents' heart rate variability. Our findings provide evidence of a link between perceived unfairness and heart rate variability. The latter is an indicator of stress-related impaired cardiac autonomic control, which has been shown to predict coronary heart diseases in the long run. Establishing a causal link between unfair pay and heart rate variability therefore uncovers a mechanism of how perceptions of unfairness can adversely affect cardiovascular health. We further test potential adverse health effects of unfair pay using data from a large representative data set. Complementary to our experimental findings we find a strong and highly significant association between health outcomes, in particular cardiovascular health, and fairness of pay.fairness, social preferences, inequality, heart rate variability, health, experiments, SOEP

Cardiovascular Consequences of Unfair Pay

This paper investigates physiological responses to perceptions of unfair pay. In a simple principal agent experiment agents produce revenue by working on a tedious task. Principals decide how this revenue is allocated between themselves and their agents. In this environment unfairness can arise if an agent's reward expectation is not met. Throughout the experiment we record agents' heart rate variability. Our findings provide evidence of a link between perceived unfairness and heart rate variability.The latter is an indicator of stressrelated impaired cardiac autonomic control, which has been shown to predict coronary heart diseases in the long run. Establishing a causal link between unfair pay and heart rate variability therefore uncovers a mechanism of how perceptions of unfairness can adversely affect cardiovascular health. Wefurther test potential adverse health effects of unfair pay using data from a large representative data set. Complementary to our experimental findings we find a strong and highly significant association between health outcomes, in particular cardiovascular health, and fairness of pay.Fairness, social preferences, inequality, heart rate variability, health, experiments, SOEP

bamdit: An R Package for Bayesian Meta-Analysis of Diagnostic Test Data

In this paper we present the R package bamdit. The name of the package stands for "Bayesian meta-analysis of diagnostic test data". bamdit was developed with the aim of simplifying the use of models in meta-analysis, that up to now have demanded great statistical expertise in Bayesian meta-analysis. The package implements a series of innovative statistical techniques including: the Bayesian summary receiver operating characteristic curve, the use of prior distributions that avoid boundary estimation problems of variances and correlations of random effects, analysis of conflict of evidence and robust estimation of model parameters. In addition, the package comes with several published examples of meta-analysis that can be used for illustration or further research in this area

Role of age and comorbidities in mortality of patients with infective endocarditis

[Purpose]: The aim of this study was to analyse the characteristics of patients with IE in three groups of age and to assess the ability of age and the Charlson Comorbidity Index (CCI) to predict mortality. [Methods]: Prospective cohort study of all patients with IE included in the GAMES Spanish database between 2008 and 2015.Patients were stratified into three age groups:<65 years,65 to 80 years,and ≥ 80 years.The area under the receiver-operating characteristic (AUROC) curve was calculated to quantify the diagnostic accuracy of the CCI to predict mortality risk. [Results]: A total of 3120 patients with IE (1327 < 65 years;1291 65-80 years;502 ≥ 80 years) were enrolled.Fever and heart failure were the most common presentations of IE, with no differences among age groups.Patients ≥80 years who underwent surgery were significantly lower compared with other age groups (14.3%,65 years; 20.5%,65-79 years; 31.3%,≥80 years). In-hospital mortality was lower in the <65-year group (20.3%,<65 years;30.1%,65-79 years;34.7%,≥80 years;p < 0.001) as well as 1-year mortality (3.2%, <65 years; 5.5%, 65-80 years;7.6%,≥80 years; p = 0.003).Independent predictors of mortality were age ≥ 80 years (hazard ratio [HR]:2.78;95% confidence interval [CI]:2.32–3.34), CCI ≥ 3 (HR:1.62; 95% CI:1.39–1.88),and non-performed surgery (HR:1.64;95% CI:11.16–1.58).When the three age groups were compared,the AUROC curve for CCI was significantly larger for patients aged <65 years(p < 0.001) for both in-hospital and 1-year mortality. [Conclusion]: There were no differences in the clinical presentation of IE between the groups. Age ≥ 80 years, high comorbidity (measured by CCI),and non-performance of surgery were independent predictors of mortality in patients with IE.CCI could help to identify those patients with IE and surgical indication who present a lower risk of in-hospital and 1-year mortality after surgery, especially in the <65-year group

Taking the pulse of Earth's tropical forests using networks of highly distributed plots

Tropical forests are the most diverse and productive ecosystems on Earth. While better understanding of these forests is critical for our collective future, until quite recently efforts to measure and monitor them have been largely disconnected. Networking is essential to discover the answers to questions that transcend borders and the horizons of funding agencies. Here we show how a global community is responding to the challenges of tropical ecosystem research with diverse teams measuring forests tree-by-tree in thousands of long-term plots. We review the major scientific discoveries of this work and show how this process is changing tropical forest science. Our core approach involves linking long-term grassroots initiatives with standardized protocols and data management to generate robust scaled-up results. By connecting tropical researchers and elevating their status, our Social Research Network model recognises the key role of the data originator in scientific discovery. Conceived in 1999 with RAINFOR (South America), our permanent plot networks have been adapted to Africa (AfriTRON) and Southeast Asia (T-FORCES) and widely emulated worldwide. Now these multiple initiatives are integrated via ForestPlots.net cyber-infrastructure, linking colleagues from 54 countries across 24 plot networks. Collectively these are transforming understanding of tropical forests and their biospheric role. Together we have discovered how, where and why forest carbon and biodiversity are responding to climate change, and how they feedback on it. This long-term pan-tropical collaboration has revealed a large long-term carbon sink and its trends, as well as making clear which drivers are most important, which forest processes are affected, where they are changing, what the lags are, and the likely future responses of tropical forests as the climate continues to change. By leveraging a remarkably old technology, plot networks are sparking a very modern revolution in tropical forest science. In the future, humanity can benefit greatly by nurturing the grassroots communities now collectively capable of generating unique, long-term understanding of Earth's most precious forests. Resumen Los bosques tropicales son los ecosistemas más diversos y productivos del mundo y entender su funcionamiento es crítico para nuestro futuro colectivo. Sin embargo, hasta hace muy poco, los esfuerzos para medirlos y monitorearlos han estado muy desconectados. El trabajo en redes es esencial para descubrir las respuestas a preguntas que trascienden las fronteras y los plazos de las agencias de financiamiento. Aquí mostramos cómo una comunidad global está respondiendo a los desafíos de la investigación en ecosistemas tropicales a través de diversos equipos realizando mediciones árbol por árbol en miles de parcelas permanentes de largo plazo. Revisamos los descubrimientos más importantes de este trabajo y discutimos cómo este proceso está cambiando la ciencia relacionada a los bosques tropicales. El enfoque central de nuestro esfuerzo implica la conexión de iniciativas locales de largo plazo con protocolos estandarizados y manejo de datos para producir resultados que se puedan trasladar a múltiples escalas. Conectando investigadores tropicales, elevando su posición y estatus, nuestro modelo de Red Social de Investigación reconoce el rol fundamental que tienen, para el descubrimiento científico, quienes generan o producen los datos. Concebida en 1999 con RAINFOR (Suramérica), nuestras redes de parcelas permanentes han sido adaptadas en África (AfriTRON) y el sureste asiático (T-FORCES) y ampliamente replicadas en el mundo. Actualmente todas estas iniciativas están integradas a través de la ciber-infraestructura de ForestPlots.net, conectando colegas de 54 países en 24 redes diferentes de parcelas. Colectivamente, estas redes están transformando nuestro conocimiento sobre los bosques tropicales y el rol de éstos en la biósfera. Juntos hemos descubierto cómo, dónde y porqué el carbono y la biodiversidad de los bosques tropicales está respondiendo al cambio climático y cómo se retroalimentan. Esta colaboración pan-tropical de largo plazo ha expuesto un gran sumidero de carbono y sus tendencias, mostrando claramente cuáles son los factores más importantes, qué procesos se ven afectados, dónde ocurren los cambios, los tiempos de reacción y las probables respuestas futuras mientras el clima continúa cambiando. Apalancando lo que realmente es una tecnología antigua, las redes de parcelas están generando una verdadera y moderna revolución en la ciencia tropical. En el futuro, la humanidad puede beneficiarse enormemente si se nutren y cultivan comunidades de investigadores de base, actualmente con la capacidad de generar información única y de largo plazo para entender los que probablemente son los bosques más preciados de la tierra. Resumo Florestas tropicais são os ecossistemas mais diversos e produtivos da Terra. Embora uma boa compreensão destas florestas seja crucial para o nosso futuro coletivo, até muito recentemente os esforços de medições e monitoramento foram amplamente desconexos. É essencial formarmos redes para obtermos respostas que transcendem fronteiras e horizontes de agências financiadoras. Neste estudo nós mostramos como uma comunidade global está respondendo aos desafios da pesquisa de ecossistemas tropicais, com equipes diversas medindo florestas, árvore por árvore, em milhares de parcelas monitoradas à longo prazo. Nós revisamos as maiores descobertas científicas deste trabalho, e mostramos também como este processo está mudando a ciência de florestas tropicais. Nossa abordagem principal envolve unir iniciativas de base a protocolos padronizados e gerenciamento de dados a fim de gerar resultados robustos em escalas ampliadas. Ao conectar pesquisadores tropicais e elevar seus status, nosso modelo de Rede de Pesquisa Social reconhece o papel-chave do produtor dos dados na descoberta científica. Concebida em 1999 com o RAINFOR (América do Sul), nossa rede de parcelas permanentes foi adaptada para África (AfriTRON) e Sudeste asiático (T-FORCES), e tem sido extensamente reproduzida em todo o mundo. Agora estas múltiplas iniciativas estão integradas através de uma infraestrutura cibernética do ForestPlots.net, conectando colegas de 54 países de 24 redes de parcelas. Estas iniciativas estão transformando coletivamente o entendimento das florestas tropicais e seus papéis na biosfera. Juntos nós descobrimos como, onde e por que o carbono e a biodiversidade da floresta estão respondendo às mudanças climáticas, e seus efeitos de retroalimentação. Esta duradoura colaboração pantropical revelou um grande sumidouro de carbono persistente e suas tendências, assim como tem evidenciado quais direcionadores são mais importantes, quais processos florestais são mais afetados, onde eles estão mudando, seus atrasos no tempo de resposta, e as prováveis respostas das florestas tropicais conforme o clima continua a mudar. Dessa forma, aproveitando uma notável tecnologia antiga, redes de parcelas acendem faíscas de uma moderna revolução na ciência das florestas tropicais. No futuro a humanidade pode se beneficiar incentivando estas comunidades basais que agora são coletivamente capazes de gerar conhecimentos únicos e duradouros sobre as florestas mais preciosas da Terra. Résume Les forêts tropicales sont les écosystèmes les plus diversifiés et les plus productifs de la planète. Si une meilleure compréhension de ces forêts est essentielle pour notre avenir collectif, jusqu'à tout récemment, les efforts déployés pour les mesurer et les surveiller ont été largement déconnectés. La mise en réseau est essentielle pour découvrir les réponses à des questions qui dépassent les frontières et les horizons des organismes de financement. Nous montrons ici comment une communauté mondiale relève les défis de la recherche sur les écosystèmes tropicaux avec diverses équipes qui mesurent les forêts arbre après arbre dans de milliers de parcelles permanentes. Nous passons en revue les principales découvertes scientifiques de ces travaux et montrons comment ce processus modifie la science des forêts tropicales. Notre approche principale consiste à relier les initiatives de base à long terme à des protocoles standardisés et une gestion de données afin de générer des résultats solides à grande échelle. En reliant les chercheurs tropicaux et en élevant leur statut, notre modèle de réseau de recherche sociale reconnaît le rôle clé de l'auteur des données dans la découverte scientifique. Conçus en 1999 avec RAINFOR (Amérique du Sud), nos réseaux de parcelles permanentes ont été adaptés à l'Afrique (AfriTRON) et à l'Asie du Sud-Est (T-FORCES) et largement imités dans le monde entier. Ces multiples initiatives sont désormais intégrées via l'infrastructure ForestPlots.net, qui relie des collègues de 54 pays à travers 24 réseaux de parcelles. Ensemble, elles transforment la compréhension des forêts tropicales et de leur rôle biosphérique. Ensemble, nous avons découvert comment, où et pourquoi le carbone forestier et la biodiversité réagissent au changement climatique, et comment ils y réagissent. Cette collaboration pan-tropicale à long terme a révélé un important puits de carbone à long terme et ses tendances, tout en mettant en évidence les facteurs les plus importants, les processus forestiers qui sont affectés, les endroits où ils changent, les décalages et les réactions futures probables des forêts tropicales à mesure que le climat continue de changer. En tirant parti d'une technologie remarquablement ancienne, les réseaux de parcelles déclenchent une révolution très moderne dans la science des forêts tropicales. À l'avenir, l'humanité pourra grandement bénéficier du soutien des communautés de base qui sont maintenant collectivement capables de générer une compréhension unique et à long terme des forêts les plus précieuses de la Terre. Abstrak Hutan tropika adalah di antara ekosistem yang paling produktif dan mempunyai kepelbagaian biodiversiti yang tinggi di seluruh dunia. Walaupun pemahaman mengenai hutan tropika amat penting untuk masa depan kita, usaha-usaha untuk mengkaji dan mengawas hutah-hutan tersebut baru sekarang menjadi lebih diperhubungkan. Perangkaian adalah sangat penting untuk mencari jawapan kepada soalan-soalan yang menjangkaui sempadan dan batasan agensi pendanaan. Di sini kami menunjukkan bagaimana sebuah komuniti global bertindak balas terhadap cabaran penyelidikan ekosistem tropika melalui penglibatan pelbagai kumpulan yang mengukur hutan secara pokok demi pokok dalam beribu-ribu plot jangka panjang. Kami meninjau semula penemuan saintifik utama daripada kerja ini dan menunjukkan bagaimana proses ini sedang mengubah bidang sains hutan tropika. Teras pendekatan kami memberi tumpuan terhadap penghubungan inisiatif akar umbi jangka panjang dengan protokol standar serta pengurusan data untuk mendapatkan hasil skala besar yang kukuh. Dengan menghubungkan penyelidik-penyelidik tropika dan meningkatkan status mereka, model Rangkaian Penyelidikan Sosial kami mengiktiraf kepentingan peranan pengasas data dalam penemuan saintifik. Bermula dengan pengasasan RAINFOR (Amerika Selatan) pada tahun 1999, rangkaian-rangkaian plot kekal kami kemudian disesuaikan untuk Afrika (AfriTRON) dan Asia Tenggara (T-FORCES) dan selanjutnya telah banyak dicontohi di seluruh dunia. Kini, inisiatif-inisiatif tersebut disepadukan melalui infrastruktur siber ForestPlots.net yang menghubungkan rakan sekerja dari 54 negara di 24 buah rangkaian plot. Secara kolektif, rangkaian ini sedang mengubah pemahaman tentang hutan tropika dan peranannya dalam biosfera. Kami telah bekerjasama untuk menemukan bagaimana, di mana dan mengapa karbon serta biodiversiti hutan bertindak balas terhadap perubahan iklim dan juga bagaimana mereka saling bermaklum balas. Kolaborasi pan-tropika jangka panjang ini telah mendedahkan sebuah sinki karbon jangka panjang serta arah alirannya dan juga menjelaskan pemandu-pemandu perubahan yang terpenting, di mana dan bagaimana proses hutan terjejas, masa susul yang ada dan kemungkinan tindakbalas hutan tropika pada perubahan iklim secara berterusan di masa depan. Dengan memanfaatkan pendekatan lama, rangkaian plot sedang menyalakan revolusi yang amat moden dalam sains hutan tropika. Pada masa akan datang, manusia sejagat akan banyak mendapat manfaat jika memupuk komuniti-komuniti akar umbi yang kini berkemampuan secara kolektif menghasilkan pemahaman unik dan jangka panjang mengenai hutan-hutan yang paling berharga di dunia

Concordance-analysis and evaluation of different diagnostic algorithms used in first trimester screening for late-onset preeclampsia

Objective Concordance-analysis and evaluation of existing algorithms detecting late-onset preeclampsia during first trimester screening Methods Retrospective cohort study investigating risk algorithms of late-onset preeclampsia during first trimester screening in a German prenatal center. Three previously developed algorithms including anamnestic factors (Apriori) and biophysical markers (BioM) were investigated by using detection rates (DR) with fixed FPR 10% and fixed cutoff >1:100. Furthermore, we set up a concordance-analysis of test results in late-onset preeclampsia cases to examine the effect of influencing factors and to detect potential weaknesses of the algorithms. Therefore, we modeled the probability of discordances as a function of the influencing factors based on a logistic regression, that was fitted using a Bayesian approach. Results 6,113 pregnancies were considered, whereof 700 have been excluded and 5,413 pregnancies were analyzed. 98 (1.8%) patients developed preeclampsia (79 late-onsets, 19 early-onsets). The Apriori-algorithm reaches a DR of 34.2%, by adding BioM (MAP and UtA-PI) the DR improves to 57.0% (FPR of 10%). In concordance-analysis of Apriori algorithm and Apriori+BioM algorithms, influencing factor BMI<25 increases the chance of discordances sigificantly. Additional, in the subgroup of late-onset preeclampsias with BMI<25 the DR is higher in Apriori+BioM algorithms than in Apriori algorithm alone. If both compared algorithms include BioM, influencing factor MAP decreases the chance of discordances significantly. All other tested influencing factors do not have a statistically significant effect on discordances Conclusion Normal-weight patients benefit more from the integration of MAP and UtA-PI compared to overweight/obese patients