New approaches in network data analysis

This thesis introduces two extensions to statistical approaches improving modeling and estimation in the field of network data analysis. The first contributing publication focuses on cross-sectional networks based on Markov graphs, whereas the second takes the evolution of networks with dynamical structure into account. Analyzing network data is challenging in terms of modeling and computation due to large and dependent data sets. The dissertation starts with an overview of network data in general and gives an introduction to the well-known model framework of exponential random graphs models with its dependence assumptions, estimation routines, challenges, and solution approaches. At the end of the introduction, main ideas of dynamic network models, the profile likelihood approach for multivariate counting processes for network data, and the analogy of the Cox proportional hazards and Poisson model with semiparametric estimation are presented. The first part of this work proposes an extension for sampling Markov graphs as a subclass of exponential random graph models in parallel to accelerate computation time in simulation-based routines. The estimation of network models, especially of large networks, is demanding and requires Markov chain Monte Carlo simulations. This publication recommends to exploit the conditional independence structure in networks to make use of parallel draws. This idea is applied to a large ego network of Facebook friendships, where an additional log transformation of network statistics accounts for degeneracy problems. This extension is implemented in the open source R package pergm, available on GitHub and a short introduction to the main functionalities is elaborated on in the thesis. The second part of this work focuses on dynamic networks. In comparison to cross-sectional networks from the first part, the development and application of longitudinal network data concentrates on modeling changes of relations. Therefore, a profile likelihood approach to model time-stamped event data is combined with a semiparametric approach including covariates built from network history. This flexible semiparametric approach is applicable to large networks because standard software can be used for estimation due to the analogy of the Cox proportional hazards and Poisson model with artificial data structure. This extended method is applied to patent collaboration data of patents submitted jointly by inventors with German residency between 2000 and 2013. Based on penalized smoothing techniques, we include time dependent network statistics and exogenous covariates to capture internal and external effects

A smooth dynamic network model for patent collaboration data

The development and application of models, which take the evolution of network dynamics into account are receiving increasing attention. We contribute to this field and focus on a profile likelihood approach to model time-stamped event data for a large-scale dynamic network. We investigate the collaboration of inventors using EU patent data. As event we consider the submission of a joint patent and we explore the driving forces for collaboration between inventors. We propose a flexible semiparametric model, which includes external and internal covariates, where the latter are built from the network history.Comment: Major change: We had a discrepancy in the implementation and the notation in the paper of the covariate vector. Further changes: Wordings and combinations of some figure

New approaches in network data analysis

This thesis introduces two extensions to statistical approaches improving modeling and estimation in the field of network data analysis. The first contributing publication focuses on cross-sectional networks based on Markov graphs, whereas the second takes the evolution of networks with dynamical structure into account. Analyzing network data is challenging in terms of modeling and computation due to large and dependent data sets. The dissertation starts with an overview of network data in general and gives an introduction to the well-known model framework of exponential random graphs models with its dependence assumptions, estimation routines, challenges, and solution approaches. At the end of the introduction, main ideas of dynamic network models, the profile likelihood approach for multivariate counting processes for network data, and the analogy of the Cox proportional hazards and Poisson model with semiparametric estimation are presented. The first part of this work proposes an extension for sampling Markov graphs as a subclass of exponential random graph models in parallel to accelerate computation time in simulation-based routines. The estimation of network models, especially of large networks, is demanding and requires Markov chain Monte Carlo simulations. This publication recommends to exploit the conditional independence structure in networks to make use of parallel draws. This idea is applied to a large ego network of Facebook friendships, where an additional log transformation of network statistics accounts for degeneracy problems. This extension is implemented in the open source R package pergm, available on GitHub and a short introduction to the main functionalities is elaborated on in the thesis. The second part of this work focuses on dynamic networks. In comparison to cross-sectional networks from the first part, the development and application of longitudinal network data concentrates on modeling changes of relations. Therefore, a profile likelihood approach to model time-stamped event data is combined with a semiparametric approach including covariates built from network history. This flexible semiparametric approach is applicable to large networks because standard software can be used for estimation due to the analogy of the Cox proportional hazards and Poisson model with artificial data structure. This extended method is applied to patent collaboration data of patents submitted jointly by inventors with German residency between 2000 and 2013. Based on penalized smoothing techniques, we include time dependent network statistics and exogenous covariates to capture internal and external effects

Nutzen und Probleme des Lifestyle-Konzepts für das Business-to-Consumer Marketing

Im Rahmen der vorliegenden Arbeit werden konsumententypologische Ansätze aus Theorie und Praxis untersucht und bewertet. Zunächst wird das theoretische Grundkonzept der Konsumententypologisierung vorgestellt. Zur Bildung von Konsumententypologien werden die gleichen Variablen herangezogen, die in der Literatur im Zusammenhang mit der Marktsegmentierung Anwendung finden. Das sind im einzelnen die Merkmale des absatzwirtschaftlich relevanten Konsumentenverhaltens sowie geographische, demographische, sozioökonomische und psychographische Kriterien. Diese Kriterien werden hinsichtlich ihrer Tauglichkeit zur Typologisierung evaluiert. Bei der Bewertung stellt sich heraus, dass es keine optimale Typologisierungsart gibt, sondern dass jedes Kriterium gewisse Vor- und Nachteile aufweist. Konsumententypologien werden daher vorwiegend multidimensional, d.h. über eine Kombination aus mehreren Variablen entwickelt. In der wissenschaftlichen Literatur finden sich bis dato nur wenige Untersuchungen, welche die Unternehmenspraxis bei der Auswahl geeigneter typologischer Konzepte unterstützen. In einem weiteren Teil der Arbeit liegt der Schwerpunkt daher auf der Analyse von Studien aus der Marketingpraxis. Die Arbeit bietet erstmals eine umfassende Synopse der am Markt etablierten Modelle, die zudem einer Evaluation unterzogen werden. Die Beurteilung der Einsatzmöglichkeiten im Marketing genießt dabei eine besondere Bedeutung. Die Analyse der Studien bestätigt den Wert von Konsumententypologien für den gesamten Marketing-Mix, wobei insbesondere die Anwendungsvielfalt im Rahmen kommunikations- und produktpolitischer Entscheidungen hervorgehoben wird. Es zeigen sich aber auch entscheidende Problemfelder sowie alternative methodische Möglichkeiten auf, mit denen sich zukünftige Forschungsaktivitäten eingehend befassen sollten

Barriers to and facilitators of the implementation of multi-disciplinary care pathways in primary care: a systematic review

Background: Care pathways (CPWs) are complex interventions that have the potential to reduce treatment errors and optimize patient outcomes by translating evidence into local practice. To design an optimal implementation strategy, potential barriers to and facilitators of implementation must be considered. The objective of this systematic review is to identify barriers to and facilitators of the implementation of CPWs in primary care (PC). Methods: A systematic search via Cochrane Library, CINAHL, and MEDLINE via PubMed supplemented by hand searches and citation tracing was carried out. We considered articles reporting on CPWs targeting patients at least 65 years of age in outpatient settings that were written in the English or German language and were published between 2007 and 2019. We considered (non-)randomized controlled trials, controlled before-after studies, interrupted time series studies (main project reports) as well as associated process evaluation reports of either methodology. Two independent researchers performed the study selection; the data extraction and critical appraisal were duplicated until the point of perfect agreement between the two reviewers. Due to the heterogeneity of the included studies, a narrative synthesis was performed. Results: Fourteen studies (seven main project reports and seven process evaluation reports) of the identified 8154 records in the search update were included in the synthesis. The structure and content of the interventions as well as the quality of evidence of the studies varied. The identified barriers and facilitators were classified using the Context and Implementation of Complex Interventions framework. The identified barriers were inadequate staffing, insufficient education, lack of financial compensation, low motivation and lack of time. Adequate skills and knowledge through training activities for health professionals, good multi-disciplinary communication and individual tailored interventions were identified as facilitators. Conclusions: In the implementation of CPWs in PC, a multitude of barriers and facilitators must be considered, and most of them can be modified through the careful design of intervention and implementation strategies. Furthermore, process evaluations must become a standard component of implementing CPWs to enable other projects to build upon previous experience

Reproducibility of a new signal processing technique to assess joint sway during standing

Postural control strategies can be investigated by kinematic analysis of joint movements. However, current research is focussing mainly on the analysis of centre of pressure excursion and lacks consensus on how to assess joint movement during postural control tasks. This study introduces a new signal processing technique to comprehensively quantify joint sway during standing and evaluates its reproducibility. Fifteen patients with non-specific low back pain and ten asymptomatic participants performed three repetitions of a 60-second standing task on foam surface. This procedure was repeated on a second day. Lumbar spine movement was recorded using an inertial measurement system. The signal was temporally divided into six sections. Two outcome variables (mean absolute sway and sways per second) were calculated for each section. The reproducibility of single and averaged measurements was quantified with linear mixed-effects models and the generalizability theory. A single measurement of ten seconds duration revealed reliability coefficients of 0.75 for mean absolute sway and 0.76 for sways per second. Averaging a measurement of 40 seconds duration on two different days revealed reliability coefficients higher than 0.90 for both outcome variables. The outcome variables’ reliability compares favourably to previously published results using different signal processing techniques or centre of pressure excursion. The introduced signal processing technique with two outcome variables to quantify joint sway during standing proved to be a highly reliable method. Since different populations, tasks or measurement tools could influence reproducibility, further investigation in other settings is still necessary. Nevertheless, the presented method has been shown to be highly promising

Overlapping activator sequences determined for two oppositely oriented promoters in halophilic Archaea

Transcription of the genomic region involved in gas vesicle formation in Halobacterium salinarum (p-vac) and Haloferax mediterranei (mc-vac) is driven by two divergent promoters, PA and PD, separated by only 35 nt. Both promoters are activated by the transcription activator GvpE which in the case of PmcA requires a 20-nt sequence (UAS) consisting of two conserved 8-nt sequence portions located upstream of BRE. Here, we determined the two UAS elements in the promoter region of p-vac by scanning mutageneses using constructs containing PpD (without PpA) fused to the bgaH reporter gene encoding an enzyme with β-galactosidase activity, or the dual reporter construct pApD with PpD fused to bgaH and PpA to an altered version of gvpA. The two UAS elements found exhibited a similar extension and distance to BRE as previously determined for the UAS in PmcA. Their distal 8-nt portions almost completely overlapped in the centre of PpD–PpA, and mutations in this region negatively affected the GvpE-mediated activation of both promoters. Any alteration of the distance between BRE and UAS resulted in the loss of the GvpE activation, as did a complete substitution of the proximal 8-nt portion, underlining that a close location of UAS and BRE was very important

Chromosomal Rearrangements in Post-Chernobyl Papillary Thyroid Carcinomas: Evaluation by Spectral Karyotyping and Automated Interphase FISH

Structural genomic rearrangements are frequent findings in human cancers. Therefore, papillary thyroid carcinomas (PTCs) were investigated for chromosomal aberrations and rearrangements of the RET proto-oncogene. For this purpose, primary cultures from 23 PTC have been established and metaphase preparations were analysed by spectral karyotyping (SKY). In addition, interphase cell preparations of the same cases were investigated by fluorescence in situ hybridisation (FISH) for the presence of RET/PTC rearrangements using RET-specific DNA probes. SKY analysis of PTC revealed structural aberrations of chromosome 11 and several numerical aberrations with frequent loss of chromosomes 20, 21, and 22. FISH analysis for RET/PTC rearrangements showed prevalence of this rearrangement in 72% (16 out of 22) of cases. However, only subpopulations of tumour cells exhibited this rearrangement indicating genetic heterogeneity. The comparison of visual and automated scoring of FISH signals revealed concordant results in 19 out of 22 cases (87%) indicating reliable scoring results using the optimised scoring parameter for RET/PTC with the automated Metafer4 system. It can be concluded from this study that genomic rearrangements are frequent in PTC and therefore important events in thyroid carcinogenesis