datawizard: an R package for easy data preparation and statistical transformations

The {datawizard} package for the R programming language (R Core Team, 2021) provides a lightweight toolbox to assist in key steps involved in any data analysis workflow: (1) wrangling the raw data to get it in the needed form, (2) applying preprocessing steps and statistical transformations, and (3) compute statistical summaries of data properties and distributions. Therefore, it can be a valuable tool for R users and developers looking for a lightweight option for data preparation

easystats/performance: performance 0.10.7

<h2>Breaking changes</h2> <ul> <li><code>binned_residuals()</code> gains a few new arguments to control the residuals used for the test, as well as different options to calculate confidence intervals (namely, <code>ci_type</code>, <code>residuals</code>, <code>ci</code> and <code>iterations</code>). The default values to compute binned residuals have changed. Default residuals are now "deviance" residuals (and no longer "response" residuals). Default confidence intervals are now "exact" intervals (and no longer based on Gaussian approximation). Use <code>ci_type = "gaussian"</code> and <code>residuals = "response"</code> to get the old defaults.</li> </ul> <h2>Changes to functions</h2> <ul> <li><code>binned_residuals()</code> - like <code>check_model()</code> - gains a <code>show_dots</code> argument to show or hide data points that lie inside error bounds. This is particular useful for models with many observations, where generating the plot would be very slow.</li> </ul&gt

easystats/parameters: parameters 0.21.2

Changes Minor improvements to factor analysis functions. The ci_digits argument of the print() method for model_parameters() now defaults to the same value of digits. model_parameters() for objects from package marginaleffects now also accepts the exponentiate argument. The print(), print_html(), print_md() and format() methods for model_parameters() get an include_reference argument, to add the reference category of categorical predictors to the parameters table. Bug fixes Fixed issue with wrong calculation of test-statistic and p-values in model_parameters() for fixest models. Fixed issue with wrong column header for glm models with family = binomial("identiy"). Minor fixes for dominance_analysis()

Developing a list of invasive alien species likely to threaten biodiversity and ecosystems in the European Union.

The European Union (EU) has recently published its first list of invasive alien species (IAS) of EU concern to which current legislation must apply. The list comprises species known to pose great threats to biodiversity and needs to be maintained and updated. Horizon scanning is seen as critical to identify the most threatening potential IAS that do not yet occur in Europe to be subsequently risk assessed for future listing. Accordingly, we present a systematic consensus horizon scanning procedure to derive a ranked list of potential IAS likely to arrive, establish, spread and have an impact on biodiversity in the region over the next decade. The approach is unique in the continental scale examined, the breadth of taxonomic groups and environments considered, and the methods and data sources used. International experts were brought together to address five broad thematic groups of potential IAS. For each thematic group the experts first independently assembled lists of potential IAS not yet established in the EU but potentially threatening biodiversity if introduced. Experts were asked to score the species within their thematic group for their separate likelihoods of i) arrival, ii) establishment, iii) spread, and iv) magnitude of the potential negative impact on biodiversity within the EU. Experts then convened for a 2-day workshop applying consensus methods to compile a ranked list of potential IAS. From an initial working list of 329 species, a list of 66 species not yet established in the EU that were considered to be very high (8 species), high (40 species) or medium (18 species) risk species was derived. Here, we present these species highlighting the potential negative impacts and the most likely biogeographic regions to be affected by these potential IAS

Invasive Alien Species - Prioritising prevention efforts through horizon scanning: ENV.B.2/ETU/2014/0016: Final report

The European Union Regulation (EU) 1143/2014 on invasive alien species (IAS) establishes an EU-wide framework for action to prevent, minimise and mitigate the adverse impacts of IAS on biodiversity and centres around the development of a list of IAS of EU Concern. The initial list of IAS of EU concern will be based on available risk assessments compliant with agreed minimum standards but horizon scanning is seen as critical to inform future updating of the list, in order to prioritise the most threatening new and emerging IAS. A workshop was held with the overarching aim of reviewing and validating an approach to horizon scanning to derive a ranked list of IAS which are likely to arrive, establish, spread and have an impact on biodiversity or related ecosystem services in the EU over the next decade. The agreed horizon scanning approach involved two distinct phases: i) Preliminary consultation between experts within five thematic groups to derive initial scores; ii) Consensus-building across expert groups including extensive discussion on species rankings coupled with review and moderation of scores across groups. The outcome of the horizon scanning was a list of 95 species, including all taxa (except microorganisms) within marine, terrestrial and freshwater environments, considered as very high or high priority for risk assessmentLe Règlement de l’Union Européenne (UE) 1143/2014 sur les espèces notices envahissantes (EEE) établit un cadre d’actions à l’échelle européenne pour prévenir, réduire au minimum et atténuer les impacts négatifs des EEE sur la biodiversité, et se concentre sur le développement d’une liste d’EEE de préoccupation européenne. La liste initiale d’EEE de préoccupation européenne est basée sur les analyses de risque disponibles conformes aux standards minimums reconnus. Mais l’horizon scanning est essentiel pour informer les mises à jour futures de la liste, dans le but de prioritiser les EEE nouvelles et émergentes les plus menaçantes. Un workshop a été organisé avec pour but général d’évaluer et de valider une approche d’horizon scanning en vue de produire une liste ordonnée d’EEE susceptibles d’arriver, de s’établir, de se disperser et de présenter un impact sur la biodiversité et les services écosystémiques associés dans l’UE durant la prochaine décennie. L’approche d’horizon scanning avalisée comprenait deux phases distinctes: i) Une consultation préliminaire entre experts au sein de cinq groups thématiques pour produire des scores initiaux ii) L’établissement de consensus au travers des groups d’experts incluant une discussion approfondie sur les classements des espèces, combinée à une évaluation et une modération des scores entre groupes. Le résultat de l’horizon scanning consistait en une liste de 95 espèces, comprenant tous les types taxonomies (excepté des microorganismes) au sein des environnements marins, terrestres et d’eau douce, et considérées comme étant de priorité très élevée à élevée pour la réalisation d’analyses de risqu

Rituximab vs ocrelizumab in relapsing-remitting multiple sclerosis

IMPORTANCE Ocrelizumab, a humanized monoclonal antibody targeted against CD20+ B cells, reduces the frequency of relapses by 46% and disability worsening by 40% compared with interferon beta 1a in relapsing-remitting multiple sclerosis (MS). Rituximab, a chimeric monoclonal anti-CD20 agent, is often prescribed as an off-label alternative to ocrelizumab. OBJECTIVE To evaluate whether the effectiveness of rituximab is noninferior to ocrelizumab in relapsing-remitting MS. DESIGN, SETTING, AND PARTICIPANTS This was an observational cohort study conducted between January 2015 and March 2021. Patients were included in the treatment group for the duration of study therapy and were recruited from the MSBase registry and Danish MS Registry (DMSR). Included patients had a history of relapsing-remitting MS treated with ocrelizumab or rituximab, a minimum 6 months of follow-up, and sufficient data to calculate the propensity score. Patients with comparable baseline characteristics were 1:6 matched with propensity score on age, sex, MS duration, disability (Expanded Disability Status Scale), prior relapse rate, prior therapy, disease activity (relapses, disability accumulation, or both), magnetic resonance imaging lesion burden (missing values imputed), and country. EXPOSURE Treatment with ocrelizumab or rituximab after 2015. MAIN OUTCOMES AND MEASURES Noninferiority comparison of annualized rate of relapses (ARRs), with a prespecified noninferiority margin of 1.63 rate ratio. Secondary end points were relapse and 6-month confirmed disability accumulation in pairwise-censored groups. RESULTS Of the 6027 patients with MS who were treated with ocrelizumab or rituximab, a total of 1613 (mean [SD] age; 42.0 [10.8] years; 1089 female [68%]) fulfilled the inclusion criteria and were included in the analysis (898 MSBase, 715 DMSR). A total of 710 patients treated with ocrelizumab (414 MSBase, 296 DMSR) were matched with 186 patients treated with rituximab (110 MSBase, 76 DMSR). Over a pairwise censored mean (SD) follow-up of 1.4 (0.7) years, the ARR ratio was higher in patients treated with rituximab than in those treated with ocrelizumab (rate ratio, 1.8; 95% CI, 1.4-2.4; ARR, 0.20 vs 0.09; P < .001). The cumulative hazard of relapses was higher among patients treated with rituximab than those treated with ocrelizumab (hazard ratio, 2.1; 95% CI, 1.5-3.0). No difference in the risk of disability accumulation was observed between groups. Results were confirmed in sensitivity analyses. CONCLUSION In this noninferiority comparative effectiveness observational cohort study, results did not show noninferiority of treatment with rituximab compared with ocrelizumab. As administered in everyday practice, rituximab was associated with a higher risk of relapses than ocrelizumab. The efficacy of rituximab and ocrelizumab administered at uniform doses and intervals is being further evaluated in randomized noninferiority clinical trials