How to deal with non-detectable and outlying values in biomarker research: Best practices and recommendations for univariate imputation approaches

Non-detectable (ND) and outlying concentration values (OV) are a common challenge of biomarker investigations. However, best practices on how to aptly deal with the affected cases are still missing. The high methodological heterogeneity in biomarker-oriented research, as for example, in the field of psychoneuroendocrinology, and the statistical bias in some of the applied methods may compromise the robustness, comparability, and generalizability of research findings. In this paper, we describe the occurrence of ND and OV in terms of a model that considers them as censored data, for instance due to measurement error cutoffs. We then present common univariate approaches in handling ND and OV by highlighting their respective strengths and drawbacks. In a simulation study with lognormal distributed data, we compare the performance of six selected methods, ranging from simple and commonly used to more sophisticated imputation procedures, in four scenarios with varying patterns of censored values as well as for a broad range of cutoffs. Especially deletion, but also fixed-value imputations bear a high risk of biased and pseudo-precise parameter estimates. We also introduce censored regressions as a more sophisticated option for a direct modeling of the censored data. Our analyses demonstrate the impact of ND and OV handling methods on the results of biomarker-oriented research, supporting the need for transparent reporting and the implementation of best practices. In our simulations, the use of imputed data from the censored intervals of a fitted lognormal distribution shows preferable properties regarding our established criteria. We provide the algorithm for this favored routine for a direct application in R on the Open Science Framework (https://osf.io/spgtv). Further research is needed to evaluate the performance of the algorithm in various contexts, for example when the underlying assumptions do not hold. We conclude with recommendations and potential further improvements for the field

Wave modelling - the state of the art

This paper is the product of the wave modelling community and it tries to make a picture of the present situation in this branch of science, exploring the previous and the most recent results and looking ahead towards the solution of the problems we presently face. Both theory and applications are considered. The many faces of the subject imply separate discussions. This is reflected into the single sections, seven of them, each dealing with a specific topic, the whole providing a broad and solid overview of the present state of the art. After an introduction framing the problem and the approach we followed, we deal in sequence with the following subjects: (Section) 2, generation by wind; 3, nonlinear interactions in deep water; 4, white-capping dissipation; 5, nonlinear interactions in shallow water; 6, dissipation at the sea bottom; 7, wave propagation; 8, numerics. The two final sections, 9 and 10, summarize the present situation from a general point of view and try to look at the future developments

Let's play with Statistics!: Implementierung einer studierendenzentrierten multimedialen Lernumgebung unter Einsatz von R-Shiny Apps und Videos

Das E-Learning Modul MUVE-STAT (Statistische Grundbegriffe und Grundlagen multivariater Verfahren) ermöglicht Psychologiestudierenden einen anwendungsorientierten und interaktiven Erwerb statistischer Methodenkenntnisse. Die Inhalte umfassen anschauliche Darstellungen statistischer Grundbegriffe bis hin zur Anwendung multivariater Verfahren. MUVE-STAT soll Lehrende und Studierende in unterschiedlichen, insbesondere in interdisziplinären Bachelorstudiengängen unterstützen und eine erfolgreiche Fortsetzung des Studiums im Rahmen eines konsekutiven Masterstudiengangs, wie dem Studiengang „Psychologie: Human Performance in Socio- Technical Systems” (HPSTS) an der TU Dresden, gewährleisten

It’s not a bug, it’s a feature: A novel paradigm for studying executive functions

In everyday life, we handle a multitude of different stimuli and tasks. Mastering this challenge is based on the use of executive functions, for example, to prioritize, select and shield task processing against distractions. However, the situations that require the use of executive functions vary. There are externally structured situations and situations that can be self-structured by the people involved themselves. The latter hence vary in requiring sub-capabilities of executive functions. In the scientific study of executive functions, these different situations hence exhibit different empirical phenomena and pose different challenges to experimental control. Here, we aim to examine effects that are typical for external and self-structured situations. To address the challenges of these type of situations, we developed a paradigm which makes demands on highly self-structure for successful task processing but still provides sufficient experimental control. The paradigm was used in two experiments in which participants inspected images of spring meadows with a variety of leaves, flowers and bugs. Participants had to mark all bugs based on three different characteristics but were free to choose their processing order and strategy. The exploratory Experiment 1 was used to derive hypotheses from the behaviour of the participants; these hypotheses were tested in the preregistered Experiment 2. Our results confirm typical effects from task switching research which usually focus on externally structured situations. Furthermore, we have identified various stable processing strategies and influences of processing sequences which can only be found in self-structured situations. Therefore, our results combine established findings from paradigms with externally structured situations and hence strong experimental control with new findings from self-structured situations and thus open a window into yet experimentally largely untapped aspects of executive functions

ReproducibiliTea Dresden

Materials from ReproducibiliTea sessions in Dresden. Templates and presentations are available for others to use and edit

Let's play with Statistics!: Implementierung einer studierendenzentrierten multimedialen Lernumgebung unter Einsatz von R-Shiny Apps und Videos

Das E-Learning Modul MUVE-STAT (Statistische Grundbegriffe und Grundlagen multivariater Verfahren) ermöglicht Psychologiestudierenden einen anwendungsorientierten und interaktiven Erwerb statistischer Methodenkenntnisse. Die Inhalte umfassen anschauliche Darstellungen statistischer Grundbegriffe bis hin zur Anwendung multivariater Verfahren. MUVE-STAT soll Lehrende und Studierende in unterschiedlichen, insbesondere in interdisziplinären Bachelorstudiengängen unterstützen und eine erfolgreiche Fortsetzung des Studiums im Rahmen eines konsekutiven Masterstudiengangs, wie dem Studiengang „Psychologie: Human Performance in Socio- Technical Systems” (HPSTS) an der TU Dresden, gewährleisten

Let's play with Statistics!: Implementierung einer studierendenzentrierten multimedialen Lernumgebung unter Einsatz von R-Shiny Apps und Videos

Das E-Learning Modul MUVE-STAT (Statistische Grundbegriffe und Grundlagen multivariater Verfahren) ermöglicht Psychologiestudierenden einen anwendungsorientierten und interaktiven Erwerb statistischer Methodenkenntnisse. Die Inhalte umfassen anschauliche Darstellungen statistischer Grundbegriffe bis hin zur Anwendung multivariater Verfahren. MUVE-STAT soll Lehrende und Studierende in unterschiedlichen, insbesondere in interdisziplinären Bachelorstudiengängen unterstützen und eine erfolgreiche Fortsetzung des Studiums im Rahmen eines konsekutiven Masterstudiengangs, wie dem Studiengang „Psychologie: Human Performance in Socio- Technical Systems” (HPSTS) an der TU Dresden, gewährleisten

Open Science Initiative der Fakultät Psychologie der TUD

Die Open Science Initiative der Fakultät Psychologie der Technischen Universität Dresden (OSIP) will Forscherinnen und Forscher dabei unterstützen, Open Science Praktiken umzusetzen und sich mit Kolleginnen und Kollegen über die dabei entstehenden Erkenntnisgewinne, aber auch Probleme auszutauschen. Dieses OSF Projekt dient zur Sammlung und Bereitstellung entsprechender Dokumente und Präsentationen