Optimal cutpoints" for binary classification tasks are often established by testing which cutpoint yields the best discrimination, for example the Youden index, in a specific sample. This results in "optimal" cutpoints that are highly variable and systematically overestimate the out-of-sample performance. To address these concerns, the cutpointr package offers robust methods for estimating optimal cutpoints and the out-of-sample performance. The robust methods include bootstrapping and smoothing based on kernel estimation, generalized additive models, smoothing splines, and local regression. These methods can be applied to a wide range of binary-classification and cost-based metrics. cutpointr also provides mechanisms to utilize user-defined metrics and estimation methods. The package has capabilities for parallelization of the bootstrapping, including reproducible random number generation. Furthermore, it is pipe-friendly, for example for compatibility with functions from tidyverse. Various functions for plotting receiver operating characteristic curves, precision recall graphs, bootstrap results and other representations of the data are included. The package contains example data from a study on psychological characteristics and suicide attempts suitable for applying binary classification algorithms.
We describe the design concepts of the modular humanoid robot Myon, which can be disassembled and reassembled during runtime. The body parts are fully autonomous in a threefold sense: they all possess their own energy supply, processing power, and a neural network topology which allows for stand-alone operation of single limbs. The robot has especially been designed for robustness and easy maintenance. It exhibits a combination of an endoskeleton with an exoskeleton, the latter of which can manually be detached without the need for technical equipment. One of the essential parts is a novel flange which firmly connects the body parts mechanically, whilst at the same time relaying the power supply lines and sensorimotor signals. We also address the details of the antagonistic and compliant actuation system which not only protects the gears against high impact forces but also enables biologically inspired joint control.
Registries of clinical trials are a potential source for scientometric analysis of medical research and serve important functions for the research community and the public at large. Clinical trials that recruit patients in Germany are usually registered in the German Clinical Trials Register (DRKS) or in international registries such as ClinicalTrials.gov. Furthermore, the International Clinical Trials Registry Platform (ICTRP) aggregates trials from multiple primary registries. We queried the DRKS, ClinicalTrials.gov, and the ICTRP for trials with a recruiting location in Germany. Trials that were registered in multiple registries were linked using the primary and secondary identifiers and a Random Forest model based on various similarity metrics. We identified 35,912 trials that were conducted in Germany. The majority of the trials was registered in multiple databases. 32,106 trials were linked using primary IDs, 26 were linked using a Random Forest model, and 10,537 internal duplicates on ICTRP were identified using the Random Forest model after finding pairs with matching primary or secondary IDs. In cross-validation, the Random Forest increased the F1-score from 96.4% to 97.1% compared to a linkage based solely on secondary IDs on a manually labelled data set. 28% of all trials were registered in the German DRKS. 54% of the trials on ClinicalTrials.gov, 43% of the trials on the DRKS and 56% of the trials on the ICTRP were pre-registered. The ratio of pre-registered studies and the ratio of studies that are registered in the DRKS increased over time.
Introduction
Analyses of clinical trial registries (CTRs) offer insights into methodological problems of published research studies, e.g., non-publication and outcome-switching. Here, we use CTRs as a tool to evaluate clinical studies conducted in Germany and test how their registration quality is associated with time and structural factors: Coordinating Centers for Clinical Trials (KKS) and Universities of Excellence.
Methods
We searched ClinicalTrials.gov, the DRKS, and the ICTRP for clinical trials recruiting participants in Germany. As a measure for the methodological quality, we assessed the proportion of trials that were pre-registered. In addition, the registration quality and availability of publications relating to the trials were manually assessed for a sample (n = 639). Also, the influence of the structural factors was tested using regression models.
Results
We identified 35,912 trials that were conducted in Germany. 59% of trials were pre-registered. Surprisingly, Universities of Excellence had lower pre-registration rates. The influence of KKS was unclear and also difficult to test. Interventional trials were more likely to be pre-registered. Registration quality improved over time and was higher in interventional trials. As of early 2021, 49% of trials that started until the end of 2015 have published scientific articles. 187 of 502 studies on ClinicalTrials.gov for which we found published articles did not reference any in the registry entry.
Discussion
The structural predictors did not show consistent relationships with the various outcome variables. However, the finding that the study type and time were related to better registration quality suggests that regulatory regimes may have an impact. Limitations of this non-pre-registered study were that no modifications to registry entries were tracked and the coarse measure of KKS involvement.
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