Non-Linear Mean Impact Analysis

The interpretation and the validity of the results from linear regression rely on strong modeling assumptions (e.g. linearity of the conditional mean of Y given X1, ...,Xk) which are known not to be satisfied in many cases. In order to overcome the problems in the interpretation of regression results Scharpenberg (2012) and Brannath and Scharpenberg (2014) introduced a new, population-based and generally non-linear measure of association called mean impact. The mean impact of an independent variable X on a target variable Y is defined as the maximum possible change in the mean of Y , when changing the density of X (in the population) in a suitably standardized way. Based on the mean impact further parameters, one of which is a non-linear measure for determination, were defined. There is also a natural extension to the case of multiple independent variables X1, ...,Xk, where we are interested in quantifying the association between Y and X1 corrected for possible associations driven by X2, ...,Xk (corresponding to multiple regression). However, Scharpenberg (2012) and Brannath and Scharpenberg (2014) point out that a restriction of the possible distributional disturbances is needed when estimating the mean impact in order to avoid overfitting problems. Therefore, they restrict themselves to functions linear in X. Doing so, they obtain conservative estimates for the mean impact and build conservative confidence intervals on their basis. Additionally, it is shown that this procedure leads to a new interpretation of linear regression coefficients under mean model miss specification. The restriction to linear distributional disturbances seems very strict and the resulting estimates are often very conservative. The goal of this thesis is to move from linear distributional disturbances to non-linear ones. Doing so we expect to obtain less conservative estimates of the mean impact. Estimates as well as confidence intervals for the mean impact based on different non-linear regression techniques will be derived and their (asymptotical) behavior will be investigated in the course of this thesis. We will do this for the single independent variable case, as well as for the case of multiple independent variable

Lung Surfactant Accelerates Skin Wound Healing : A Translational Study with a Randomized Clinical Phase I Study

Lung surfactants are used for reducing alveolar surface tension in preterm infants to ease breathing. Phospholipid films with surfactant proteins regulate the activity of alveolar macrophages and reduce inflammation. Aberrant skin wound healing is characterized by persistent inflammation. The aim of the study was to investigate if lung surfactant can promote wound healing. Preclinical wound models, e.g. cell scratch assays and full-thickness excisional wounds in mice, and a randomized, phase I clinical trial in healthy human volunteers using a suction blister model were used to study the effect of the commercially available bovine lung surfactant on skin wound repair. Lung surfactant increased migration of keratinocytes in a concentration-dependent manner with no effect on fibroblasts. Significantly reduced expression levels were found for pro-inflammatory and pro-fibrotic genes in murine wounds. Because of these beneficial effects in preclinical experiments, a clinical phase I study was initiated to monitor safety and tolerability of surfactant when applied topically onto human wounds and normal skin. No adverse effects were observed. Subepidermal wounds healed significantly faster with surfactant compared to control. Our study provides lung surfactant as a strong candidate for innovative treatment of chronic skin wounds and as additive for treatment of burn wounds to reduce inflammation and prevent excessive scarring. © 2020, The Author(s)

Nicht-lineare Mean Impact Analyse

The interpretation and the validity of the results from linear regression rely on strong modeling assumptions (e.g. linearity of the conditional mean of Y given X1, ...,Xk) which are known not to be satisfied in many cases. In order to overcome the problems in the interpretation of regression results Scharpenberg (2012) and Brannath and Scharpenberg (2014) introduced a new, population-based and generally non-linear measure of association called mean impact. The mean impact of an independent variable X on a target variable Y is defined as the maximum possible change in the mean of Y , when changing the density of X (in the population) in a suitably standardized way. Based on the mean impact further parameters, one of which is a non-linear measure for determination, were defined. There is also a natural extension to the case of multiple independent variables X1, ...,Xk, where we are interested in quantifying the association between Y and X1 corrected for possible associations driven by X2, ...,Xk (corresponding to multiple regression). However, Scharpenberg (2012) and Brannath and Scharpenberg (2014) point out that a restriction of the possible distributional disturbances is needed when estimating the mean impact in order to avoid overfitting problems. Therefore, they restrict themselves to functions linear in X. Doing so, they obtain conservative estimates for the mean impact and build conservative confidence intervals on their basis. Additionally, it is shown that this procedure leads to a new interpretation of linear regression coefficients under mean model miss specification. The restriction to linear distributional disturbances seems very strict and the resulting estimates are often very conservative. The goal of this thesis is to move from linear distributional disturbances to non-linear ones. Doing so we expect to obtain less conservative estimates of the mean impact. Estimates as well as confidence intervals for the mean impact based on different non-linear regression techniques will be derived and their (asymptotical) behavior will be investigated in the course of this thesis. We will do this for the single independent variable case, as well as for the case of multiple independent variable

Assessing consistency in clinical trials with two subgroups and binary endpoints: A new test within the logistic regression model

In late stage drug development, the experimental drug is tested in a diverse study population within the relevant indication. In order to receive marketing authorization, robust evidence for the therapeutic efficacy is crucial requiring investigation of treatment effects in well‐defined subgroups. Conventionally, consistency analyses in subgroups have been performed by means of interaction tests. However, the interaction test can only reject the null hypothesis of equivalence and not confirm consistency. Simulation studies suggest that the interaction test has low power but can also be oversensitive depending on sample size—leading in combination with the actually ill‐posed null hypothesis to findings regardless of clinical relevance. In order to overcome these disadvantages in the setup of binary endpoints, we propose to use a consistency test based on the interval inclusion principle, which is able to reject heterogeneity and confirm consistency of subgroup‐specific treatment effects while controlling the type I error. This homogeneity test is based upon the deviation between overall treatment effect and subgroup‐specific effects on the odds ratio scale and is compared with an equivalence test based on the ratio of both subgroup‐specific effects. Performance of these consistency tests is assessed in a simulation study. In addition, the consistency tests are outlined for the relative risk regression. The proposed homogeneity test reaches sufficient power in realistic scenarios with small interactions. As expected, power decreases for unbalanced subgroups, lower sample sizes, and narrower margins. Severe interactions are covered by the null hypothesis and are more likely to be rejected the stronger they are

Handreichung zur Patient*innenbeteiligung an klinischer Forschung

Die Handreichung zur Patient*innenbeteiligung an klinischer Forschung wurde entwickelt, um die Patient*innenbeteiligung an klinischen Studien im deutschsprachigen Raum zu stärken und Forschende bei der aktiven Beteiligung von Patient*innen zu unterstützen. Unter Beteiligung verstehen wir die aktive Einbindung von Patient*innen in die Planung, Durchführung und Translation klinischer Studien. Wir grenzen den Begriff ab von ähnlichen Konzepten wie bspw. der partizipativen Gesundheitsforschung – für diese treffen wir keine Aussagen. Der Fokus liegt auf Hinweisen und Anregungen für die praktische Umsetzung von Beteiligung an klinischen Studien. Je nach Situation sind ganz unterschiedliche Formen und Intensitäten der Beteiligung denkbar, die Inhalte der Handreichung sollten entsprechend an die eigene Situation angepasst werden. Die vorliegende Handreichung richtet sich unmittelbar an klinisch Forschende, die an einer aktiven Patient*innenbeteiligung interessiert sind und diese umsetzen möchten. Forschungsfördernde und Gutachter*innen können sie als Orientierung bei der Bewertung der Patient*innenbeteiligung in Forschungsanträgen nutzen. Darüber hinaus kann die Handreichung auch Patient*innen( vertreter*innen) unterstützen, die sich aktiv an klinischer Forschung beteiligen wollen. Die vorliegende Handreichung soll eine praktische Unterstützung für die Planung und Umsetzung von Patient*innenbeteiligung darstellen. Entsprechend ist sie aufgebaut: In Kapitel 1 wird in das Thema der Patient*innenbeteiligung eingeführt. Kapitel 2 gibt einen kurzen Einblick in die Erfahrungen zweier Patientinnen mit Patient*innenbeteiligung. Kapitel 3 widmet sich der Planung, Kapitel 4 der Durchführung von Patient*innenbeteiligung. Kapitel 5 gibt einen Ausblick auf Handlungsbedarfe und Entwicklungspotentiale

Handreichung zur Patient*innenbeteiligung an klinischer Forschung

Dies ist die aktualisierte Handreichung zur Patient*innenbeteiligung an klinischer Forschung. Die Handreichung wurde entwickelt, um die Patient*innenbeteiligung an klinischen Studien im deutschsprachigen Raum zu stärken und Forschende bei der aktiven Beteiligung von Patient*innen zu unterstützen. Unter Beteiligung verstehen wir die aktive Einbindung von Patient*innen in die Planung, Durchführung und Translation klinischer Studien. Wir grenzen den Begriff ab von ähnlichen Konzepten wie bspw. der partizipativen Gesundheitsforschung – für diese treffen wir keine Aussagen. Der Fokus liegt auf Hinweisen und Anregungen für die praktische Umsetzung von Beteiligung an klinischen Studien. Je nach Situation sind ganz unterschiedliche Formen und Intensitäten der Beteiligung denkbar, die Inhalte der Handreichung sollten entsprechend an die eigene Situation angepasst werden. Die vorliegende Handreichung richtet sich unmittelbar an klinisch Forschende, die an einer aktiven Patient*innenbeteiligung interessiert sind und diese umsetzen möchten. Forschungsfördernde und Gutachter*innen können sie als Orientierung bei der Bewertung der Patient*innenbeteiligung in Forschungsanträgen nutzen. Darüber hinaus kann die Handreichung auch Patient*innen( vertreter*innen) unterstützen, die sich aktiv an klinischer Forschung beteiligen wollen. Die vorliegende Handreichung soll eine praktische Unterstützung für die Planung und Umsetzung von Patient*innenbeteiligung darstellen. Entsprechend ist sie aufgebaut: In Kapitel 1 wird in das Thema der Patient*innenbeteiligung eingeführt. Kapitel 2 gibt einen kurzen Einblick in die Erfahrungen zweier Patientinnen mit Patient*innenbeteiligung. Kapitel 3 widmet sich der Planung, Kapitel 4 der Durchführung von Patient*innenbeteiligung. Kapitel 5 gibt einen Ausblick auf Handlungsbedarfe und Entwicklungspotentiale

Dopamine D2 Receptor Occupancy Estimated From Plasma Concentrations of Four Different Antipsychotics and the Subjective Experience of Physical and Mental Well-Being in Schizophrenia

Background Impaired subjective well-being in schizophrenia patients treated with antipsychotics has often been linked inter alia to the antidopaminergic effects of medication. Thus, it is important to capture the association between striatal dopamine D-2 receptor occupancy (D2-RO) and global subjective well-being. We examined this association using data from our multicenter, randomized, double-blind Neuroleptic Strategy Study (NeSSy). Methods An innovative double randomization process was used for allocation of patients to the specific treatment groups. Plasma drug concentrations were measured after 6 and 24 weeks of treatment to obtain the estimated D2-RO (eD2-RO) relative to literature values. We made an exploratory analysis of associations between eD2-RO and subjective well-being scores. One hundred two blood samples from 69 patients were available for the analysis. Because of the lack of a satisfactory occupancy model for quetiapine, only haloperidol, flupentixol, and olanzapine treatment groups were pooled, whereas aripiprazole data were analyzed separately, because of its partial agonistic properties. Results In the pooled antagonist group, eD2-RO correlated negatively with the summarized well-being score. In a more detailed analysis, this association could be confirmed for all first-generation antipsychotic-treated patients, but not for the separate second-generation antipsychotic groups. In the aripiprazole group, higher eD2-RO was associated with impaired physical well-being, but had no association with mental well-being. Conclusions Our results suggest that high plasma levels and consequently high occupancy at D-2 receptors are disadvantageous for subjective well-being, as distinct from the objective extrapyramidal side effects. To minimize patients' malaise, which disfavors adherence, implementation of therapeutic drug monitoring in the clinical routine may be useful

Effectiveness and costs of a low-threshold hearing screening programme (HörGeist) for individuals with intellectual disabilities: protocol for a screening study

Introduction Individuals with intellectual disabilities (ID) often suffer from hearing loss, in most cases undiagnosed or inappropriately treated. The implementation of a programme of systematic hearing screening, diagnostics, therapy initiation or allocation and long-term monitoring within the living environments of individuals with ID (nurseries, schools, workshops, homes), therefore, seems beneficial.Methods and analysis The study aims to assess the effectiveness and costs of a low-threshold screening programme for individuals with ID. Within this programme 1050 individuals with ID of all ages will undergo hearing screening and an immediate reference diagnosis in their living environment (outreach cohort). The recruitment of participants in the outreach group will take place within 158 institutions, for example, schools, kindergartens and places of living or work. If an individual fails the screening assessment, subsequent full audiometric diagnostics will follow and, if hearing loss is confirmed, initiation of therapy or referral to and monitoring of such therapy. A control cohort of 141 participants will receive an invitation from their health insurance provider via their family for the same procedure but within a clinic (clinical cohort). A second screening measurement will be performed with both cohorts 1 year later and the previous therapy outcome will be checked. It is hypothesised that this programme leads to a relevant reduction in the number of untreated or inadequately treated cases of hearing loss and strengthens the communication skills of the newly or better-treated individuals. Secondary outcomes include the age-dependent prevalence of hearing loss in individuals with ID, the costs associated with this programme, cost of illness before-and-after enrolment and modelling of the programme’s cost-effectiveness compared with regular care.Ethics and dissemination The study has been approved by the Institutional Ethics Review Board of the Medical Association of Westphalia-Lippe and the University of Münster (No. 2020-843 f-S). Participants or guardians will provide written informed consent. Findings will be disseminated through presentations, peer-reviewed journals and conferences.Trial registration number DRKS00024804