The importance of cross‐disciplinary scientific engagement in the development of quantitative procedures for aggregate safety assessments

Recent guidance on safety monitoring during drug development, issued by regulatory authorities in the United States and European Union, indicate a shift in focus towards aggregate safety monitoring and scientific evaluation of integrated safety data. The call for program‐level reviews of accumulating safety data, including from ongoing studies, provides an opportunity to leverage the scientific expertise and medical judgment of safety management teams with (a) a multidisciplinary approach, (b) quantitative frameworks to measure level of evidence, and (c) assessments that are product‐specific and driven by medical judgment. A multidisciplinary team, regularly reviewing aggregate safety data throughout the development program, is vital not only for early signal detection but also for generating a better understanding of the accumulating data and context needed for decreasing false alarms.     

Do small cities affect bird assemblages? An evaluation from Patagonia

Urban Ecosystems - Most studies about the impact of urbanization on bird assemblages are from the Northern Hemisphere and most previous research has reported that cities reduced bird species...     

OECD: One or Many? Ranking Countries with a Composite Well-Being Indicator

In the social sciences it is hardly contested that “institutions matter”. Consequently the analysis of differences and similarities of institutions should be a routine task. Prevailing methods to the measurement of institutions are however problematic, in particular when they have to deal with different levels of measurement. To overcome this, in this paper we present an innovative method to the quantification of qualitative institutional data. We do this by, first, developing benchmarks for comparison based on Weberian ideal types. Then we identify and operationalise the dimensions and indicators to a nuanced measurement of institutions, after which we apply a careful and differentiated scoring, weighting and aggregation procedure in order to guarantee the highest standards of comparability, transparency and reliability of the method. Finally, we demonstrate our method by applying it to the analysis of institutional individualisation in European welfare institutions.     

A network analysis of student mobility patterns from high school to master’s

Human migration involves the movement of people from one place to another. An example of undirected migration is Italian student mobility where students move from the South to the Center-North. This kind of mobility has become of general interest, and this work explores student mobility from Sicily towards universities outside the island. The data used in this paper regards six cohorts of students, from 2008/09 to 2013/14. In particular, our goal is to study the 3-step migration path: the area of origin (Sicilian provinces), the regional university for the bachelor’s degree, and the regional university for the master’s. Our analysis is conducted by building a multipartite network with four sets of nodes: students; Sicilian provinces; bachelor region of studies; and the master region of studies. By projecting the students’ set onto the others, we obtain a tripartite network where the number of students represents the link weight. Results show that the big Sicilian cities—Palermo, Catania, and Messina—have different preferential paths compared to small Sicilian cities. Furthermore, the results reveal preferential paths of 3-step mobility that only, in part, reflect a south-north orientation in the transition from the region of study for the bachelor degree to that for the master’s.

     

Bibliometric analysis of production planning and control (1990–2016)

Production Planning and Control (PPC) has been one of the primary publishing outlets for operations management research for nearly three decades. This paper presents bibliometric analysis of the journal from its inception through 2016. PPC was analysed from six different perspectives to provide reliable and in-depth information about the journal’s performance and development. The analysis considered the development of the journal itself, the countries of contributing authors, authors’ academic and practitioner organisation affiliations, the top publishing and most cited authors, the most cited articles, and finally the topics of published papers through analysis of titles, keywords and abstracts. It was found that the journal is considered an outlet for both practice and academic research, reflecting changes over time in the landscape of operations management.     

Extreme value and cluster analysis of European daily temperature series

Time series of daily mean temperature obtained from the European Climate Assessment data set is analyzed with respect to their extremal properties. A time-series clustering approach which combines Bayesian methodology, extreme value theory and classification techniques is adopted for the analysis of the regional variability of temperature extremes. The daily mean temperature records are clustered on the basis of their corresponding predictive distributions for 25-, 50- and 100-year return values. The results of the cluster analysis show a clear distinction between the highest altitude stations, for which the return values are lowest, and the remaining stations. Furthermore, a clear distinction is also found between the northernmost stations in Scandinavia and the stations in central and southern Europe. This spatial structure of the return period distributions for 25-, 50- and 100-years seems to be consistent with projected changes in the variability of temperature extremes over Europe pointing to a different behavior in central Europe than in northern Europe and the Mediterranean area, possibly related to the effect of soil moisture and land-atmosphere coupling.     

Two-tailed asymptotic inferences for a proportion

This paper evaluates 29 methods for obtaining a two-sided confidence interval for a binomial proportion (16 of which are new proposals) and comes to the conclusion that: Wilson's classic method is only optimal for a confidence of 99%, although generally it can be applied when n≥50; for a confidence of 95% or 90%, the optimal method is the one based on the arcsine transformation (when this is applied to the data incremented by 0.5), which behaves in a very similar manner to Jeffreys’ Bayesian method. A simpler option, though not so good as those just mentioned, is the classic-adjusted Wald method of Agresti and Coull.