1Background: Arm hemiparesis, secondary to stroke, is common and disabling. The robot ARMin, 2

A conditional independence graph is a concise representation of pairwise conditional independence among many variables. Graphical Random Forests (GRaFo) are a novel method for estimating pairwise conditional independence relationships among mixed-type, i.e. continuous and discrete, variables. The number of edges is a tuning parameter in any graphical model estimator and there is no obvious number that constitutes a good choice. Stability Selection helps choosing this parameter with respect to a bound on the expected number of false positives (error control).The performance of GRaFo is evaluated and compared with various other methods for p = 50, 100, and 200 possibly mixed-type variables while sample size is n = 100 (n = 500 for maximum likelihood). Furthermore, GRaFo is applied to data from the Swiss Health Survey in order to evaluate how well it can reproduce the interconnection of functional health components, personal, and environmental factors, as hypothesized by the World Health Organization's International Classification of Functioning, Disability and Health (ICF). Finally, GRaFo is used to identify risk factors which may be associated with adverse neurodevelopment of children who suffer from trisomy 21 and experienced open-heart surgery.GRaFo performs well with mixed data and thanks to Stability Selection * Corresponding author. it provides an error control mechanism for false positive selection.

BackgroundQuality of life (QoL) in persons with spinal cord injury (SCI) has been found to differ across countries. However, comparability of measurement results between countries depends on the cross-cultural validity of the applied instruments. The study examined the metric quality and cross-cultural validity of the Satisfaction with Life Scale (SWLS), the Life Satisfaction Questionnaire (LISAT-9), the Personal Well-Being Index (PWI) and the 5-item World Health Organization Quality of Life Assessment (WHOQoL-5) across six countries in a sample of persons with spinal cord injury (SCI).MethodsA cross-sectional multi-centre study was conducted and the data of 243 out-patients with SCI from study centers in Australia, Brazil, Canada, Israel, South Africa, and the United States were analyzed using Rasch-based methods.ResultsThe analyses showed high reliability for all 4 instruments (person reliability index .78-.92). Unidimensionality of measurement was supported for the WHOQoL-5 (Chi2 = 16.43, df = 10, p = .088), partially supported for the PWI (Chi2 = 15.62, df = 16, p = .480), but rejected for the LISAT-9 (Chi2 = 50.60, df = 18, p = .000) and the SWLS (Chi2 = 78.54, df = 10, p = .000) based on overall and item-wise Chi2 tests, principal components analyses and independent t-tests. The response scales showed the expected ordering for the WHOQoL-5 and the PWI, but not for the other two instruments. Using differential item functioning (DIF) analyses potential cross-country bias was found in two items of the SWLS and the WHOQoL-5, three items of the LISAT-9 and four items of the PWI. However, applying Rasch-based statistical methods, especially subtest analyses, it was possible to identify optimal strategies to enhance the metric properties and the cross-country equivalence of the instruments post-hoc. Following the post-hoc procedures the WHOQOL-5 and the PWI worked in a consistent and expected way in all countries.ConclusionsQoL assessment using the summary scores of the WHOQOL-5 and the PWI appeared cross-culturally valid in persons with SCI. In contrast, summary scores of the LISAT-9 and the SWLS have to be interpreted with caution. The findings of the current study can be especially helpful to select instruments for international research projects in SCI.