Klinische Charakteristika und Erkrankungsverlauf bei Patienten mit primär progredienter Multipler Sklerose

Diese Arbeit untersuchte retrospektiv die klinischen Charakteristika (Geschlecht, Erkrankungsalter, Familienanamnese, Erstsymptome, Krankheitssymptome, Behinderungsgrad, Krankheitsdauer und MRT-, VEP- sowie SEP-Befunde), die Art der Therapie, den Erkrankungsverlauf mit und ohne Therapie, sowie mögliche Einflussfaktoren auf den Erkrankungsverlauf bei Patienten PP-MS, welche sich am Institut für klinische Neuroimmunologie vorstellten. Als wichtige Parameter zur Einschätzung der neurologischen Behinderung wurden der EDSS („Expanded Disability Status Scale“), der PI („Progressionsindex“ = EDSS/Erkrankungsdauer), wie auch der MSSS („Multiple Sclerosis Severity Score“) für jeden Patienten ermittelt

Klinische Charakteristika und Erkrankungsverlauf bei Patienten mit primär progredienter Multipler Sklerose

Diese Arbeit untersuchte retrospektiv die klinischen Charakteristika (Geschlecht, Erkrankungsalter, Familienanamnese, Erstsymptome, Krankheitssymptome, Behinderungsgrad, Krankheitsdauer und MRT-, VEP- sowie SEP-Befunde), die Art der Therapie, den Erkrankungsverlauf mit und ohne Therapie, sowie mögliche Einflussfaktoren auf den Erkrankungsverlauf bei Patienten PP-MS, welche sich am Institut für klinische Neuroimmunologie vorstellten. Als wichtige Parameter zur Einschätzung der neurologischen Behinderung wurden der EDSS („Expanded Disability Status Scale“), der PI („Progressionsindex“ = EDSS/Erkrankungsdauer), wie auch der MSSS („Multiple Sclerosis Severity Score“) für jeden Patienten ermittelt

A new conditional performance score for the evaluation of adaptive group sequential designs with sample size recalculation

In standard clinical trial designs, the required sample size is fixed in the planning stage based on initial parameter assumptions. It is intuitive that the correct choice of the sample size is of major importance for an ethical justification of the trial. The required parameter assumptions should be based on previously published results from the literature. In clinical practice, however, historical data often do not exist or show highly variable results. Adaptive group sequential designs allow a sample size recalculation after a planned unblinded interim analysis in order to adjust the sample size during the ongoing trial. So far, there exist no unique standards to assess the performance of sample size recalculation rules. Single performance criteria commonly reported are given by the power and the average sample size; the variability of the recalculated sample size and the conditional power distribution are usually ignored. Therefore, the need for an adequate performance score combining these relevant performance criteria is evident. To judge the performance of an adaptive design, there exist two possible perspectives, which might also be combined: Either the global performance of the design can be addressed, which averages over all possible interim results, or the conditional performance is addressed, which focuses on the remaining performance conditional on a specific interim result. In this work, we give a compact overview of sample size recalculation rules and performance measures. Moreover, we propose a new conditional performance score and apply it to various standard recalculation rules by means of Monte-Carlo simulations

The adoptr Package: Adaptive Optimal Designs for Clinical Trials in R

Even though adaptive two-stage designs with unblinded interim analyses are becoming increasingly popular in clinical trial designs, there is a lack of statistical software to make their application more straightforward. The package adoptr fills this gap for the common case of two-stage one- or two-arm trials with (approximately) normally distributed outcomes. In contrast to previous approaches, adoptr optimizes the entire design upfront which allows maximal efficiency. To facilitate experimentation with different objective functions, adoptr supports a flexible way of specifying both (composite) objective scores and (conditional) constraints by the user. Special emphasis was put on providing measures to aid practitioners with the validation process of the package

Improving sample size recalculation in adaptive clinical trials by resampling

Sample size calculations in clinical trials need to be based on profound parameter assumptions. Wrong parameter choices may lead to too small or too high sample sizes and can have severe ethical and economical consequences. Adaptive group sequential study designs are one solution to deal with planning uncertainties. Here, the sample size can be updated during an ongoing trial based on the observed interim effect. However, the observed interim effect is a random variable and thus does not necessarily correspond to the true effect. One way of dealing with the uncertainty related to this random variable is to include resampling elements in the recalculation strategy. In this paper, we focus on clinical trials with a normally distributed endpoint. We consider resampling of the observed interim test statistic and apply this principle to several established sample size recalculation approaches. The resulting recalculation rules are smoother than the original ones and thus the variability in sample size is lower. In particular, we found that some resampling approaches mimic a group sequential design. In general, incorporating resampling of the interim test statistic in existing sample size recalculation rules results in a substantial performance improvement with respect to a recently published conditional performance score

Directed energy deposition-arc (DED-Arc) and numerical welding simulation as a method to determine the homogeneity

This research presents a hybrid approach to for the prediction of the homogeneity of mechanical properties in 3D metal parts manufactured using directed energy deposition-arc (DED-Arc). DED-Arc is an additive manufacturing process which can offer a cost-effective way to manufacture 3D metal parts, due to high deposition rate of up to 8 kg/h. Regression equations developed in a previous study were used to predict the mechanical properties of a wall structure using only the cooling time t8/5 calculated in a numerical welding simulation. The new approach in this research paper contains the prediction of the homogeneity of the mechanical properties, especially hardness, in 3D metal parts, which can vary due to localized changes in t8/5 cooling time provoked by specific geometrical features or general changes in dimensions. In this study a method for the calculation of the hardness distribution on additively manufactured parts was developed and shown

Directed energy deposition-arc (DED-Arc) and numerical welding simulation as a hybrid data source for future machine learning applications

This research presents a hybrid approach to generate sample data for future machine learning applications for the prediction of mechanical properties in directed energy deposition-arc (DED-Arc) using the GMAW process. DED-Arc is an additive manufacturing process which offers a cost-effective way to generate 3D metal parts, due to its high deposition rate of up to 8 kg/h. The mechanical properties additively manufactured wall structures made of the filler material G4Si1 (ER70 S-6) are shown in dependency of the t8/5 cooling time. The numerical simulation is used to link the process parameters and geometrical features to a specific t8/5 cooling time. With an input of average welding power, welding speed and geometrical features such as wall thickness, layer height and heat source size a specific temperature field can be calculated for each iteration in the simulated welding process. This novel approach allows to generate large, artificial data sets as training data for machine learning methods by combining experimental results to generate a regression equation based on the experimentally measured t8/5 cooling time. Therefore, using the regression equations in combination with numerically calculated t8/5 cooling times an accurate prediction of the mechanical properties was possible in this research with an error of only 2.6%. Thus, a small set of experimentally generated data set allows to achieve regression equations which enable a precise prediction of mechanical properties. Moreover, the validated numerical welding simulation model was suitable to achieve an accurate calculation of the t8/5 cooling time, with an error of only 0.3%

Optimal planning of adaptive two-stage designs.

Funder: RUPRECHT KARLS UNIVERSITAET HEIDELBERG ‐ Projekt DEALAdaptive designs are playing an increasingly important role in the planning of clinical trials. While there exists various research on the optimal determination of a two-stage design, non-optimal versions still are frequently applied in clinical research. In this article, we strive to motivate the application of optimal adaptive designs and give guidance on how to determine them. It is demonstrated that optimizing a trial design with respect to particular objective criteria can have a substantial benefit over the application of conventional adaptive sample size recalculation rules. Furthermore, we show that in many practical situations, optimal group-sequential designs show an almost negligible performance loss compared to optimal adaptive designs. Finally, we illustrate how optimal designs can be tailored to specific operational requirements by customizing the underlying optimization problem

Glutamic Acid Residues in HIV-1 p6 Regulate Virus Budding and Membrane Association of Gag

The HIV-1 Gag p6 protein regulates the final abscission step of nascent virions from the cell membrane by the action of its two late (l-) domains, which recruit Tsg101 and ALIX, components of the ESCRT system. Even though p6 consists of only 52 amino acids, it is encoded by one of the most polymorphic regions of the HIV-1 gag gene and undergoes various posttranslational modifications including sumoylation, ubiquitination, and phosphorylation. In addition, it mediates the incorporation of the HIV-1 accessory protein Vpr into budding virions. Despite its small size, p6 exhibits an unusually high charge density. In this study, we show that mutation of the conserved glutamic acids within p6 increases the membrane association of Pr55 Gag followed by enhanced polyubiquitination and MHC-I antigen presentation of Gag-derived epitopes, possibly due to prolonged exposure to membrane bound E3 ligases. The replication capacity of the total glutamic acid mutant E0A was almost completely impaired, which was accompanied by defective virus release that could not be rescued by ALIX overexpression. Altogether, our data indicate that the glutamic acids within p6 contribute to the late steps of viral replication and may contribute to the interaction of Gag with the plasma membrane