Acquiescence and Extremity in Cross-National Surveys: Domain Dependence and Country-Level Correlates

Likert-type rating scales are susceptible to response styles, such as acquiescence and extremity scoring. Although it is widely acknowledged that response styles can seriously invalidate findings of cross-cultural research, their theoretical underpinnings are hardly explored. The current study analyzed domain-dependency and country differences in acquiescence and extremity scoring in a large dataset of the International Social Survey Program. The hypothesis that response styles are more likely in domains with a high personal relevance compared to domains with a low personal relevance was tentatively confirmed. Correlations with various cultural, psychological, and economic variables were investigated. We found that acquiescence was negatively related to affluence, individualism, and well-being, while extremity was only negatively related to well-being. Positive associations were found between uncertainty avoidance and both acquiescence and extremity

Estimating damages from price-fixing: the value of transaction data

We use a unique private data set of about 340,000 invoice positions from 36 smaller and larger customers of German cement producers to study the value of such transaction data for an estimation of cartel damages. In particular, we investigate, first, how structural break analysis can be used to identify the exact end of the cartel agreement and, second, how an application of before-and-after approaches to estimate the price overcharge can benefit from such rich data sets. We conclude that transaction data allows such a detailed assessment of the cartel and its impact on direct customers that its regular application in private antitrust cases is desired as long as data collection and preparation procedures are not prohibitively expensive

Withdrawal symptoms in children after long-term administration of sedatives and/or analgesics: A literature review. "Assessment remains troublesome"

Background: Prolonged administration of benzodiazepines and/or opioids to children in a pediatric intensive care unit (PICU) may induce physiological dependence and withdrawal symptoms. Objective: We reviewed the literature for relevant contributions on the nature of these withdrawal symptoms and on availability of valid scoring systems to assess the extent of symptoms. Methods: The databases PubMed, CINAHL, and Psychinfo (1980-June 2006) were searched using relevant key terms. Results: Symptoms of benzodiazepine and opioid withdrawal can be classified in two groups: central nervous system effects and autonomic dysfunction. However, symptoms of the two types show a large overlap for benzodiazepine and opioid withdrawal. Symptoms of gastrointestinal dysfunction in the PICU population have been described for opioid withdrawal only. Six assessment tools for withdrawal symptoms are used in children. Four of these have been validated for neonates only. Two instruments are available to specifically determine withdrawal symptoms in the PICU: the Sedation Withdrawal Score (SWS) and the Opioid Benzodiazepine Withdrawal Scale (OBWS). The OBWS is the only available assessment tool with prospective validation; however, the sensitivity is low. Conclusions: Withdrawal symptoms for benzodiazepines and opioids largely overlap. A sufficiently sensitive instrument for assessing withdrawal symptoms in PICU patients needs to be developed

Improved imputation quality of low-frequency and rare variants in European samples using the ‘Genome of The Netherlands'

Although genome-wide association studies (GWAS) have identified many common variants associated with complex traits, low-frequency and rare variants have not been interrogated in a comprehensive manner. Imputation from dense reference panels, such as the 1000 Genomes Project (1000G), enables testing of ungenotyped variants for association. Here we present the results of imputation using a large, new population-specific panel: the Genome of The Netherlands (GoNL). We benchmarked the performance of the 1000G and GoNL reference sets by comparing imputation genotypes with ‘true' genotypes typed on ImmunoChip in three European populations (Dutch, British, and Italian). GoNL showed significant improvement in the imputation quality for rare variants (MAF 0.05–0.5%) compared with 1000G. In Dutch samples, the mean observed Pearson correlation, r2, increased from 0.61 to 0.71. We also saw improved imputation accuracy for other European populations (in the British samples, r2 improved from 0.58 to 0.65, and in the Italians from 0.43 to 0.47). A combined reference set comprising 1000G and GoNL improved the imputation of rare variants even further. The Italian samples benefitted the most from this combined reference (the mean r2 increased from 0.47 to 0.50). We conclude that the creation of a large population-specific reference is advantageous for imputing rare variants and that a combined reference panel across multiple populations yields the best imputation results

A high-quality human reference panel reveals the complexity and distribution of genomic structural variants

Structural variation (SV) represents a major source of differences between individual human genomes and has been linked to disease phenotypes. However, the majority of studies provide neither a global view of the full spectrum of these variants nor integrate them into reference panels of genetic variation. Here, we analyse whole genome sequencing data of 769 individuals from 250 Dutch families, and provide a haplotype-resolved map of 1.9 million genome variants across 9 different variant classes, including novel forms of complex indels, and retrotransposition-mediated insertions of mobile elements and processed RNAs. A large proportion are previously under reported variants sized between 21 and 100 bp. We detect 4 megabases of novel sequence, encoding 11 new transcripts. Finally, we show 191 known, trait-associated SNPs to be in strong linkage disequilibrium with SVs and demonstrate that our panel facilitates accurate imputation of SVs in unrelated individuals

Overview of data-synthesis in systematic reviews of studies on outcome prediction models

Background: Many prognostic models have been developed. Different types of models, i.e. prognostic factor and outcome prediction studies, serve different purposes, which should be reflected in how the results are summarized in reviews. Therefore we set out to investigate how authors of reviews synthesize and report the results of primary outcome prediction studies. Methods: Outcome prediction reviews published in MEDLINE between October 2005 and March 2011 were eligible and 127 Systematic reviews with the aim to summarize outcome prediction studies written in English were identified for inclusion. Characteristics of the reviews and the primary studies that were included were independently assessed by 2 review authors, using standardized forms. Results: After consensus meetings a total of 50 systematic reviews that met the inclusion criteria were included. The type of primary studies included (prognostic factor or outcome prediction) was unclear in two-thirds of the reviews. A minority of the reviews reported univariable or multivariable point estimates and measures of dispersion from the primary studies. Moreover, the variables considered for outcome prediction model development were often not reported, or were unclear. In most reviews there was no information about model performance. Quantitative analysis was performed in 10 reviews, and 49 reviews assessed the primary studies qualitatively. In both analyses types a range of different methods was used to present the results of the outcome prediction studies. Conclusions: Different methods are applied to synthesize primary study results but quantitative analysis is rarely performed. The description of its objectives and of the primary studies is suboptimal and performance parameters of the outcome prediction models are rarely mentioned. The poor reporting and the wide variety of data synthesis strategies are prone to influence the conclusions of outcome prediction reviews. Therefore, there is much room for improvement in reviews of outcome prediction studies. (aut.ref.

Genome-wide meta-analysis identifies five new susceptibility loci for pancreatic cancer.

In 2020, 146,063 deaths due to pancreatic cancer are estimated to occur in Europe and the United States combined. To identify common susceptibility alleles, we performed the largest pancreatic cancer GWAS to date, including 9040 patients and 12,496 controls of European ancestry from the Pancreatic Cancer Cohort Consortium (PanScan) and the Pancreatic Cancer Case-Control Consortium (PanC4). Here, we find significant evidence of a novel association at rs78417682 (7p12/TNS3, P = 4.35 × 10-8). Replication of 10 promising signals in up to 2737 patients and 4752 controls from the PANcreatic Disease ReseArch (PANDoRA) consortium yields new genome-wide significant loci: rs13303010 at 1p36.33 (NOC2L, P = 8.36 × 10-14), rs2941471 at 8q21.11 (HNF4G, P = 6.60 × 10-10), rs4795218 at 17q12 (HNF1B, P = 1.32 × 10-8), and rs1517037 at 18q21.32 (GRP, P = 3.28 × 10-8). rs78417682 is not statistically significantly associated with pancreatic cancer in PANDoRA. Expression quantitative trait locus analysis in three independent pancreatic data sets provides molecular support of NOC2L as a pancreatic cancer susceptibility gene

Taxonomy based on science is necessary for global conservation

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