Qualité des statistiques de causes de décès: problèmes actuels et perspectives. [Quality of statistics on causes of death: current problems and perspectives].

Statistics of causes of death remain an important source of epidemiological data for the evaluation of various medical and health problems. The improvement of analytical techniques and, above all, the transformation of demographic and morbid structures of populations have prompted researchers in the field to give more importance to the quality of death certificates. After describing the data collection system presently used in Switzerland, the paper discusses various indirect estimations of the quality of Swiss data and reviews the corresponding international literature.

Enterprise Architecture Management Design: Towards Understanding Organizational Contingencies, Design Principles and Effectiveness

Despite the increasing popularity of enterprise architecture management (EAM) in practice, many EAM initiatives either do not fully meet the expected targets or fail. Several frameworks have been suggested as guidelines to EA implementation, but companies seldom follow prescriptive frameworks. Instead, they follow very diverse implementation approaches that depend on their organizational contingencies and the way of adopting and evolving EAM over time. This research strives for a broader understanding of EAM by exploring context-dependent EAM adoption approaches as well as identifying the main EA principles that affect EA effectiveness. Based on two studies, this dissertation aims to address two main questions: (1) EAM design: Which approaches do companies follow when adopting EAM? (2) EA principles and their impact: What impact does EA principles have on EA effectiveness/quality? By utilizing both qualitative and quantitative research methods, this research contributes to exploring different EAM designs in different organizational contingencies as well as using EA principles as an effective means to achieve principle-based EAM design. My research can help companies identify a suitable EAM design that fits their organizational settings and shape their EA through a set of principles.

Meta-analysis of gene-level associations for rare variants based on single-variant statistics.

Meta-analysis of genome-wide association studies (GWASs) has led to the discoveries of many common variants associated with complex human diseases. There is a growing recognition that identifying "causal" rare variants also requires large-scale meta-analysis. The fact that association tests with rare variants are performed at the gene level rather than at the variant level poses unprecedented challenges in the meta-analysis. First, different studies may adopt different gene-level tests, so the results are not compatible. Second, gene-level tests require multivariate statistics (i.e., components of the test statistic and their covariance matrix), which are difficult to obtain. To overcome these challenges, we propose to perform gene-level tests for rare variants by combining the results of single-variant analysis (i.e., p values of association tests and effect estimates) from participating studies. This simple strategy is possible because of an insight that multivariate statistics can be recovered from single-variant statistics, together with the correlation matrix of the single-variant test statistics, which can be estimated from one of the participating studies or from a publicly available database. We show both theoretically and numerically that the proposed meta-analysis approach provides accurate control of the type I error and is as powerful as joint analysis of individual participant data. This approach accommodates any disease phenotype and any study design and produces all commonly used gene-level tests. An application to the GWAS summary results of the Genetic Investigation of ANthropometric Traits (GIANT) consortium reveals rare and low-frequency variants associated with human height. The relevant software is freely available.

Combined analysis of grey matter voxel-based morphometry and white matter tract-based spatial statistics in late-life bipolar disorder.

Background: Previous magnetic resonance imaging (MRI) studies in young patients with bipolar disorder indicated the presence of grey matter concentration changes as well as microstructural alterations in white matter in various neocortical areas and the corpus callosum. Whether these structural changes are also present in elderly patients with bipolar disorder with long-lasting clinical evolution remains unclear. Methods: We performed a prospective MRI study of consecutive elderly, euthymic patients with bipolar disorder and healthy, elderly controls. We conducted a voxel-based morphometry (VBM) analysis and a tract-based spatial statistics (TBSS) analysis to assess fractional anisotropy and longitudinal, radial and mean diffusivity derived by diffusion tensor imaging (DTI). Results: We included 19 patients with bipolar disorder and 47 controls in our study. Fractional anisotropy was the most sensitive DTI marker and decreased significantly in the ventral part of the corpus callosum in patients with bipolar disorder. Longitudinal, radial and mean diffusivity showed no significant between-group differences. Grey matter concentration was reduced in patients with bipolar disorder in the right anterior insula, head of the caudate nucleus, nucleus accumbens, ventral putamen and frontal orbital cortex. Conversely, there was no grey matter concentration or fractional anisotropy increase in any brain region in patients with bipolar disorder compared with controls. Limitations: The major limitation of our study is the small number of patients with bipolar disorder. Conclusion: Our data document the concomitant presence of grey matter concentration decreases in the anterior limbic areas and the reduced fibre tract coherence in the corpus callosum of elderly patients with long-lasting bipolar disorder.

Managing Social-Business Tensions: A Review and Research Agenda for Social Enterprise

In a world filled with poverty, environmental degradation, and moral injustice, social enterprises offer a ray of hope. These organizations seek to achieve social missions through business ventures. Yet social missions and business ventures are associated with divergent goals, values, norms, and identities. Attending to them simultaneously creates tensions, competing demands, and ethical dilemmas. Effectively understanding social enterprises therefore depends on insight into the nature and management of these tensions. While existing research recognizes tensions between social missions and business ventures, we lack any systematic analysis. Our paper addresses this issue. We first categorize the types of tensions that arise between social missions and business ventures, emphasizing their prevalence and variety. We then explore how four different organizational theories offer insight into these tensions, and we develop an agenda for future research. We end by arguing that a focus on social-business tensions not only expands insight into social enterprises, but also provides an opportunity for research on social enterprises to inform traditional organizational theories. Taken together, our analysis of tensions in social enterprises integrates and seeks to energize research on this expanding phenomenon.

HIERFSTAT, a package for R to compute and test hierarchical F-statistics

The package HIERFSTAT for the statistical software R, created by the R Development Core Team, allows the estimate of hierarchical F-statistics from a hierarchy with any numbers of levels. In addition, it allows testing the statistical significance of population differentiation for these different levels, using a generalized likelihood-ratio test. The package HIERFSTAT is available at http://www.unil.ch/popgen/softwares/hierfstat.htm.