@article{ElHelouBiegnerBodeetal.2019, author = {El-Helou, Sabine M. and Biegner, Anika-Kerstin and Bode, Sebastian and Ehl, Stephan R. and Heeg, Maximilian and Maccari, Maria E. and Ritterbusch, Henrike and Speckmann, Carsten and Rusch, Stephan and Scheible, Raphael and Warnatz, Klaus and Atschekzei, Faranaz and Beider, Renata and Ernst, Diana and Gerschmann, Stev and Jablonka, Alexandra and Mielke, Gudrun and Schmidt, Reinhold E. and Sch{\"u}rmann, Gesine and Sogkas, Georgios and Baumann, Ulrich H. and Klemann, Christian and Viemann, Dorothee and Bernuth, Horst von and Kr{\"u}ger, Renate and Hanitsch, Leif G. and Scheibenbogen, Carmen M. and Wittke, Kirsten and Albert, Michael H. and Eichinger, Anna and Hauck, Fabian and Klein, Christoph and Rack-Hoch, Anita and Sollinger, Franz M. and Avila, Anne and Borte, Michael and Borte, Stephan and Fasshauer, Maria and Hauenherm, Anja and Kellner, Nils and M{\"u}ller, Anna H. and {\"U}lzen, Anett and Bader, Peter and Bakhtiar, Shahrzad and Lee, Jae-Yun and Heß, Ursula and Schubert, Ralf and W{\"o}lke, Sandra and Zielen, Stefan and Ghosh, Sujal and Laws, Hans-Juergen and Neubert, Jennifer and Oommen, Prasad T. and H{\"o}nig, Manfred and Schulz, Ansgar and Steinmann, Sandra and Klaus, Schwarz and D{\"u}ckers, Gregor and Lamers, Beate and Langemeyer, Vanessa and Niehues, Tim and Shai, Sonu and Graf, Dagmar and M{\"u}glich, Carmen and Schmalzing, Marc T. and Schwaneck, Eva C. and Tony, Hans-Peter and Dirks, Johannes and Haase, Gabriele and Liese, Johannes G. and Morbach, Henner and Foell, Dirk and Hellige, Antje and Wittkowski, Helmut and Masjosthusmann, Katja and Mohr, Michael and Geberzahn, Linda and Hedrich, Christian M. and M{\"u}ller, Christiane and R{\"o}sen-Wolff, Angela and Roesler, Joachim and Zimmermann, Antje and Behrends, Uta and Rieber, Nikolaus and Schauer, Uwe and Handgretinger, Rupert and Holzer, Ursula and Henes, J{\"o}rg and Kanz, Lothar and Boesecke, Christoph and Rockstroh, J{\"u}rgen K. and Schwarze-Zander, Carolynne and Wasmuth, Jan-Christian and Dilloo, Dagmar and H{\"u}lsmann, Brigitte and Sch{\"o}nberger, Stefan and Schreiber, Stefan and Zeuner, Rainald and Ankermann, Tobias and Bismarck, Philipp von and Huppertz, Hans-Iko and Kaiser-Labusch, Petra and Greil, Johann and Jakoby, Donate and Kulozik, Andreas E. and Metzler, Markus and Naumann-Bartsch, Nora and Sobik, Bettina and Graf, Norbert and Heine, Sabine and Kobbe, Robin and Lehmberg, Kai and M{\"u}ller, Ingo and Herrmann, Friedrich and Horneff, Gerd and Klein, Ariane and Peitz, Joachim and Schmidt, Nadine and Bielack, Stefan and Groß-Wieltsch, Ute and Classen, Carl F. and Klasen, Jessica and Deutz, Peter and Kamitz, Dirk and Lassy, Lisa and Tenbrock, Klaus and Wagner, Norbert and Bernbeck, Benedikt and Brummel, Bastian and Lara-Villacanas, Eusebia and M{\"u}nstermann, Esther and Schneider, Dominik T. and Tietsch, Nadine and Westkemper, Marco and Weiß, Michael and Kramm, Christof and K{\"u}hnle, Ingrid and Kullmann, Silke and Girschick, Hermann and Specker, Christof and Vinnemeier-Laubenthal, Elisabeth and Haenicke, Henriette and Schulz, Claudia and Schweigerer, Lothar and M{\"u}ller, Thomas G. and Stiefel, Martina and Belohradsky, Bernd H. and Soetedjo, Veronika and Kindle, Gerhard and Grimbacher, Bodo}, title = {The German national registry of primary immunodeficiencies (2012-2017)}, series = {Frontiers in Immunology}, volume = {10}, journal = {Frontiers in Immunology}, doi = {10.3389/fimmu.2019.01272}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-226629}, year = {2019}, abstract = {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.}, language = {en} } @article{HomburgWeissAlwanetal.2019, author = {Homburg, Annika and Weiß, Christian H. and Alwan, Layth C. and Frahm, Gabriel and G{\"o}b, Rainer}, title = {Evaluating approximate point forecasting of count processes}, series = {Econometrics}, volume = {7}, journal = {Econometrics}, number = {3}, issn = {2225-1146}, doi = {10.3390/econometrics7030030}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-196929}, year = {2019}, abstract = {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.}, language = {en} } @article{HomburgWeissFrahmetal.2021, author = {Homburg, Annika and Weiß, Christian H. and Frahm, Gabriel and Alwan, Layth C. and G{\"o}b, Rainer}, title = {Analysis and forecasting of risk in count processes}, series = {Journal of Risk and Financial Management}, volume = {14}, journal = {Journal of Risk and Financial Management}, number = {4}, issn = {1911-8074}, doi = {10.3390/jrfm14040182}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-236692}, year = {2021}, abstract = {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.}, language = {en} } @article{HomburgWeissAlwanetal.2021, author = {Homburg, Annika and Weiß, Christian H. and Alwan, Layth C. and Frahm, Gabriel and G{\"o}b, Rainer}, title = {A performance analysis of prediction intervals for count time series}, series = {Journal of Forecasting}, volume = {40}, journal = {Journal of Forecasting}, number = {4}, doi = {10.1002/for.2729}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-217906}, pages = {603 -- 609}, year = {2021}, abstract = {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.}, language = {en} }