TY - JOUR A1 - El-Helou, Sabine M. A1 - Biegner, Anika-Kerstin A1 - Bode, Sebastian A1 - Ehl, Stephan R. A1 - Heeg, Maximilian A1 - Maccari, Maria E. A1 - Ritterbusch, Henrike A1 - Speckmann, Carsten A1 - Rusch, Stephan A1 - Scheible, Raphael A1 - Warnatz, Klaus A1 - Atschekzei, Faranaz A1 - Beider, Renata A1 - Ernst, Diana A1 - Gerschmann, Stev A1 - Jablonka, Alexandra A1 - Mielke, Gudrun A1 - Schmidt, Reinhold E. A1 - Schürmann, Gesine A1 - Sogkas, Georgios A1 - Baumann, Ulrich H. A1 - Klemann, Christian A1 - Viemann, Dorothee A1 - Bernuth, Horst von A1 - Krüger, Renate A1 - Hanitsch, Leif G. A1 - Scheibenbogen, Carmen M. A1 - Wittke, Kirsten A1 - Albert, Michael H. A1 - Eichinger, Anna A1 - Hauck, Fabian A1 - Klein, Christoph A1 - Rack-Hoch, Anita A1 - Sollinger, Franz M. A1 - Avila, Anne A1 - Borte, Michael A1 - Borte, Stephan A1 - Fasshauer, Maria A1 - Hauenherm, Anja A1 - Kellner, Nils A1 - Müller, Anna H. A1 - Ülzen, Anett A1 - Bader, Peter A1 - Bakhtiar, Shahrzad A1 - Lee, Jae-Yun A1 - Heß, Ursula A1 - Schubert, Ralf A1 - Wölke, Sandra A1 - Zielen, Stefan A1 - Ghosh, Sujal A1 - Laws, Hans-Juergen A1 - Neubert, Jennifer A1 - Oommen, Prasad T. A1 - Hönig, Manfred A1 - Schulz, Ansgar A1 - Steinmann, Sandra A1 - Klaus, Schwarz A1 - Dückers, Gregor A1 - Lamers, Beate A1 - Langemeyer, Vanessa A1 - Niehues, Tim A1 - Shai, Sonu A1 - Graf, Dagmar A1 - Müglich, Carmen A1 - Schmalzing, Marc T. A1 - Schwaneck, Eva C. A1 - Tony, Hans-Peter A1 - Dirks, Johannes A1 - Haase, Gabriele A1 - Liese, Johannes G. A1 - Morbach, Henner A1 - Foell, Dirk A1 - Hellige, Antje A1 - Wittkowski, Helmut A1 - Masjosthusmann, Katja A1 - Mohr, Michael A1 - Geberzahn, Linda A1 - Hedrich, Christian M. A1 - Müller, Christiane A1 - Rösen-Wolff, Angela A1 - Roesler, Joachim A1 - Zimmermann, Antje A1 - Behrends, Uta A1 - Rieber, Nikolaus A1 - Schauer, Uwe A1 - Handgretinger, Rupert A1 - Holzer, Ursula A1 - Henes, Jörg A1 - Kanz, Lothar A1 - Boesecke, Christoph A1 - Rockstroh, Jürgen K. A1 - Schwarze-Zander, Carolynne A1 - Wasmuth, Jan-Christian A1 - Dilloo, Dagmar A1 - Hülsmann, Brigitte A1 - Schönberger, Stefan A1 - Schreiber, Stefan A1 - Zeuner, Rainald A1 - Ankermann, Tobias A1 - Bismarck, Philipp von A1 - Huppertz, Hans-Iko A1 - Kaiser-Labusch, Petra A1 - Greil, Johann A1 - Jakoby, Donate A1 - Kulozik, Andreas E. A1 - Metzler, Markus A1 - Naumann-Bartsch, Nora A1 - Sobik, Bettina A1 - Graf, Norbert A1 - Heine, Sabine A1 - Kobbe, Robin A1 - Lehmberg, Kai A1 - Müller, Ingo A1 - Herrmann, Friedrich A1 - Horneff, Gerd A1 - Klein, Ariane A1 - Peitz, Joachim A1 - Schmidt, Nadine A1 - Bielack, Stefan A1 - Groß-Wieltsch, Ute A1 - Classen, Carl F. A1 - Klasen, Jessica A1 - Deutz, Peter A1 - Kamitz, Dirk A1 - Lassy, Lisa A1 - Tenbrock, Klaus A1 - Wagner, Norbert A1 - Bernbeck, Benedikt A1 - Brummel, Bastian A1 - Lara-Villacanas, Eusebia A1 - Münstermann, Esther A1 - Schneider, Dominik T. A1 - Tietsch, Nadine A1 - Westkemper, Marco A1 - Weiß, Michael A1 - Kramm, Christof A1 - Kühnle, Ingrid A1 - Kullmann, Silke A1 - Girschick, Hermann A1 - Specker, Christof A1 - Vinnemeier-Laubenthal, Elisabeth A1 - Haenicke, Henriette A1 - Schulz, Claudia A1 - Schweigerer, Lothar A1 - Müller, Thomas G. A1 - Stiefel, Martina A1 - Belohradsky, Bernd H. A1 - Soetedjo, Veronika A1 - Kindle, Gerhard A1 - Grimbacher, Bodo T1 - The German national registry of primary immunodeficiencies (2012-2017) JF - Frontiers in Immunology N2 - Introduction: The German PID-NET registry was founded in 2009, serving as the first national registry of patients with primary immunodeficiencies (PID) in Germany. It is part of the European Society for Immunodeficiencies (ESID) registry. The primary purpose of the registry is to gather data on the epidemiology, diagnostic delay, diagnosis, and treatment of PIDs. Methods: Clinical and laboratory data was collected from 2,453 patients from 36 German PID centres in an online registry. Data was analysed with the software Stata® and Excel. Results: The minimum prevalence of PID in Germany is 2.72 per 100,000 inhabitants. Among patients aged 1-25, there was a clear predominance of males. The median age of living patients ranged between 7 and 40 years, depending on the respective PID. Predominantly antibody disorders were the most prevalent group with 57% of all 2,453 PID patients (including 728 CVID patients). A gene defect was identified in 36% of patients. Familial cases were observed in 21% of patients. The age of onset for presenting symptoms ranged from birth to late adulthood (range 0-88 years). Presenting symptoms comprised infections (74%) and immune dysregulation (22%). Ninety-three patients were diagnosed without prior clinical symptoms. Regarding the general and clinical diagnostic delay, no PID had undergone a slight decrease within the last decade. However, both, SCID and hyper IgE-syndrome showed a substantial improvement in shortening the time between onset of symptoms and genetic diagnosis. Regarding treatment, 49% of all patients received immunoglobulin G (IgG) substitution (70%-subcutaneous; 29%-intravenous; 1%-unknown). Three-hundred patients underwent at least one hematopoietic stem cell transplantation (HSCT). Five patients had gene therapy. Conclusion: The German PID-NET registry is a precious tool for physicians, researchers, the pharmaceutical industry, politicians, and ultimately the patients, for whom the outcomes will eventually lead to a more timely diagnosis and better treatment. KW - registry for primary immunodeficiency KW - primary immunodeficiency (PID) KW - German PID-NET registry KW - PID prevalence KW - European Society for Immunodeficiencies (ESID) KW - IgG substitution therapy KW - CVID Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-226629 VL - 10 ER - TY - JOUR A1 - Homburg, Annika A1 - Weiß, Christian H. A1 - Alwan, Layth C. A1 - Frahm, Gabriel A1 - Göb, Rainer T1 - Evaluating approximate point forecasting of count processes JF - Econometrics N2 - In forecasting count processes, practitioners often ignore the discreteness of counts and compute forecasts based on Gaussian approximations instead. For both central and non-central point forecasts, and for various types of count processes, the performance of such approximate point forecasts is analyzed. The considered data-generating processes include different autoregressive schemes with varying model orders, count models with overdispersion or zero inflation, counts with a bounded range, and counts exhibiting trend or seasonality. We conclude that Gaussian forecast approximations should be avoided. KW - count time series KW - estimation error KW - Gaussian approximation KW - predictive performance KW - quantile forecasts KW - Value at Risk Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-196929 SN - 2225-1146 VL - 7 IS - 3 ER - TY - JOUR A1 - Homburg, Annika A1 - Weiß, Christian H. A1 - Frahm, Gabriel A1 - Alwan, Layth C. A1 - Göb, Rainer T1 - Analysis and forecasting of risk in count processes JF - Journal of Risk and Financial Management N2 - Risk measures are commonly used to prepare for a prospective occurrence of an adverse event. If we are concerned with discrete risk phenomena such as counts of natural disasters, counts of infections by a serious disease, or counts of certain economic events, then the required risk forecasts are to be computed for an underlying count process. In practice, however, the discrete nature of count data is sometimes ignored and risk forecasts are calculated based on Gaussian time series models. But even if methods from count time series analysis are used in an adequate manner, the performance of risk forecasting is affected by estimation uncertainty as well as certain discreteness phenomena. To get a thorough overview of the aforementioned issues in risk forecasting of count processes, a comprehensive simulation study was done considering a broad variety of risk measures and count time series models. It becomes clear that Gaussian approximate risk forecasts substantially distort risk assessment and, thus, should be avoided. In order to account for the apparent estimation uncertainty in risk forecasting, we use bootstrap approaches for count time series. The relevance and the application of the proposed approaches are illustrated by real data examples about counts of storm surges and counts of financial transactions. KW - count time series KW - expected shortfall KW - expectiles KW - Gaussian approximation KW - mid quantiles KW - tail conditional expectation KW - value at risk Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-236692 SN - 1911-8074 VL - 14 IS - 4 ER - TY - JOUR A1 - Homburg, Annika A1 - Weiß, Christian H. A1 - Alwan, Layth C. A1 - Frahm, Gabriel A1 - Göb, Rainer T1 - A performance analysis of prediction intervals for count time series JF - Journal of Forecasting N2 - One of the major motivations for the analysis and modeling of time series data is the forecasting of future outcomes. The use of interval forecasts instead of point forecasts allows us to incorporate the apparent forecast uncertainty. When forecasting count time series, one also has to account for the discreteness of the range, which is done by using coherent prediction intervals (PIs) relying on a count model. We provide a comprehensive performance analysis of coherent PIs for diverse types of count processes. We also compare them to approximate PIs that are computed based on a Gaussian approximation. Our analyses rely on an extensive simulation study. It turns out that the Gaussian approximations do considerably worse than the coherent PIs. Furthermore, special characteristics such as overdispersion, zero inflation, or trend clearly affect the PIs' performance. We conclude by presenting two empirical applications of PIs for count time series: the demand for blood bags in a hospital and the number of company liquidations in Germany. KW - coherent forecasting KW - count time series KW - estimation error KW - Gaussian approximation KW - prediction interval Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-217906 VL - 40 IS - 4 SP - 603 EP - 609 ER -