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

Regenbogenfamilien - Sind homosexuelle Paare Eltern zweiter Klasse?: Kurzfassung

Universität Erfurt, Kurzfassung der Bachelorarbeit, erstellt 08/201

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

Statistical model building: Background “knowledge” based on inappropriate preselection causes misspecification

Background: Statistical model building requires selection of variables for a model depending on the model's aim. In descriptive and explanatory models, a common recommendation often met in the literature is to include all variables in the model which are assumed or known to be associated with the outcome independent of their identification with data driven selection procedures. An open question is, how reliable this assumed "background knowledge" truly is. In fact, "known" predictors might be findings from preceding studies which may also have employed inappropriate model building strategies. Methods: We conducted a simulation study assessing the influence of treating variables as "known predictors" in model building when in fact this knowledge resulting from preceding studies might be insufficient. Within randomly generated preceding study data sets, model building with variable selection was conducted. A variable was subsequently considered as a "known" predictor if a predefined number of preceding studies identified it as relevant. Results: Even if several preceding studies identified a variable as a "true" predictor, this classification is often false positive. Moreover, variables not identified might still be truly predictive. This especially holds true if the preceding studies employed inappropriate selection methods such as univariable selection. Conclusions: The source of "background knowledge" should be evaluated with care. Knowledge generated on preceding studies can cause misspecification

Citizen science’s transformative impact on science, citizen empowerment and socio-political processes

Citizen science (CS) can foster transformative impact for science, citizen empowerment and socio-political processes. To unleash this impact, a clearer understanding of its current status and challenges for its development is needed. Using quantitative indicators developed in a collaborative stakeholder process, our study provides a comprehensive overview of the current status of CS in Germany, Austria and Switzerland. Our online survey with 340 responses focused on CS impact through (1) scientific practices, (2) participant learning and empowerment, and (3) socio-political processes. With regard to scientific impact, we found that data quality control is an established component of CS practice, while publication of CS data and results has not yet been achieved by all project coordinators (55%). Key benefits for citizen scientists were the experience of collective impact (“making a difference together with others”) as well as gaining new knowledge. For the citizen scientists’ learning outcomes, different forms of social learning, such as systematic feedback or personal mentoring, were essential. While the majority of respondents attributed an important value to CS for decision-making, only few were confident that CS data were indeed utilized as evidence by decision-makers. Based on these results, we recommend (1) that project coordinators and researchers strengthen scientific impact by fostering data management and publications, (2) that project coordinators and citizen scientists enhance participant impact by promoting social learning opportunities and (3) that project initiators and CS networks foster socio-political impact through early engagement with decision-makers and alignment with ongoing policy processes. In this way, CS can evolve its transformative impact

Sektoralisierung als Planungsherausforderung im inklusiven Gemeinwesen

2. Hrsg.: Martin F. Reichstein Förderung durch: Forschungsinstitut für gesellschaftliche Weiterentwicklung (FGW)Seit September 2016 führt das Zentrum für Planung und Evaluation Sozialer Dienste (ZPE) der Universität Siegen das Forschungsprojekt „Koordinationspotenziale kommunaler Teilhabepolitik in der Pflege, Behindertenhilfe und Sozialpsychiatrie (KoKoP)“ durch. Das Projekt wird im Rahmen des Programms „Vorbeugende Sozialpolitik“ des nordrheinwestfä- lischen Forschungsinstituts für Gesellschaftliche Weiterentwicklung (FGW) finanziell gefördert. Ziel des Projektes ist es, anhand empirischer Untersuchungen Erkenntnisse darüber zu gewinnen, welche Möglichkeiten für Kommunen bestehen, durch Planung und Koordination die Wirkungen von Teilhabeleistungen in den Leistungsbereichen der Pflege, Behindertenhilfe und Sozialpsychiatrie zu optimieren. Zudem soll der Frage nachgegangen werden, wie professionelle Hilfen stärker mit informellen Ressourcen im Vor- und Umfeld des Leistungsgeschehens verknüpft werden können. Mögliche Problemquellen werden u.a. in einer ausgeprägten Sektoralisierung des Leistungsgeschehens, mangelnder Kooperation sowie in einer geringen Sozialraumorientierung vermutet. Im Rahmen eines eintägigen Expertenworkshops am 14. November 2017 wurden zum einen Zwischenergebnisse bisheriger Untersuchungen vorgestellt und diskutiert. Zum anderen wurden in drei Arbeitsgruppen zentrale Fragestellungen des Projekts erörtert. Der vorliegende Band ist eine Zusammenschau von Beiträgen einzelner Teilnehmer*innen dieses Workshops

Structural analysis of the Ras-like G protein MglA and its cognate GAP MglB and implications for bacterial polarity

The small G protein MglA and its cognate GAP MglB exemplify a new type of GTPase activation mechanism. In contrast to other Ras-like proteins, the key 'arginine finger' is provided not by the GAP, but by MglA itself