We provide a detailed hands-on tutorial for the R add-on package mboost. The package implements boosting for optimizing general risk functions utilizing component-wise (penalized) least squares estimates as base-learners for fitting various kinds of generalized linear and generalized additive models to potentially high-dimensional data. We give a theoretical background and demonstrate how mboost can be used to fit interpretable models of different complexity.As an example we use mboost to predict the body fat based on anthropometric measurements throughout the tutorial.

Objective. Treadmill training with partial body weight support has been suggested as a useful strategy for gait rehabilitation after stroke. This prospective, blinded, randomized controlled study of gait retraining tested the feasibility and potential efficacy of using an electromechanical-driven gait orthosis (Lokomat) for treadmill training. Methods. Sixteen stroke patients, mostly within 3 months after onset, were randomized into 2 treatment groups, ABA or BAB (A = 3 weeks of Lokomat training, B = 3 weeks of conventional physical therapy) for 9 weeks of treatment. The outcome measures were the EU-Walking Scale, Rivermead Motor Assessment Scale, 10-m timed walking speed, 6-minute timed walking distance, Motricity Index, Medical Research Council Scale of strength, and Ashworth Scale of tone. Results. The EU-Walking Scale, Rivermead Motor Assessment Scale, 6-minute timed walking distance, Medical Research Council Scale, and Ashworth Scale demonstrated significantly more improvement during the Lokomat training phase than during the conventional physical therapy phase within each 3-week interval. Conclusions. Despite the small number of patients, the present data suggest that the Lokomat robotic assistive device provides innovative possibilities for gait training in stroke rehabilitation while eliminating prolonged repetitive movements in a nonergonomic position on the part of the physical therapist.

Generalized additive models for location, scale and shape (GAMLSSs) are a popular semiparametric modelling approach that, in contrast with conventional generalized additive models, regress not only the expected mean but also every distribution parameter (e.g. location, scale and shape) to a set of covariates. Current fitting procedures for GAMLSSs are infeasible for high dimensional data set-ups and require variable selection based on (potentially problematic) information criteria. The present work describes a boosting algorithm for high dimensional GAMLSSs that was developed to overcome these limitations. Specifically, the new algorithm was designed to allow the simultaneous estimation of predictor effects and variable selection. The algorithm proposed was applied to Munich rental guide data, which are used by landlords and tenants as a reference for the average rent of a flat depending on its characteristics and spatial features. The net rent predictions that resulted from the high dimensional GAMLSSs were found to be highly competitive and covariate-specific prediction intervals showed a major improvement over classical generalized additive models.

In this retrospective analysis, the influence of a continuous infusion of an endogenous hormone (arginine vasopressin) on systemic hemodynamics and laboratory variables was assessed in patients with vasodilatory shock unresponsive to conventional therapy. Arginine vasopressin was effective in reversing systemic hypotension. However, adverse effects on gastrointestinal perfusion and coagulation cannot be excluded.

Supplementary AVP infusion improved cardiocirculatory function in advanced vasodilatory shock, but an increase in liver enzymes and bilirubin, and a decrease in platelet count occurred during AVP therapy, particularly during simultaneous hemofiltration. Initiation of AVP infusion before norepinephrine requirements exceeding 0.6 microg x kg x min may improve outcome.