@article{HuangBelharazemLietal.2013,
  author    = {Huang, Bei and Belharazem, Djeda and Li, Li and Kneitz, Susanne and Schnabel, Philipp A. and Rieker, Ralf J. and K{\"o}rner, Daniel and Nix, Wilfried and Schalke, Berthold and M{\"u}ller-Hermelink, Hans Konrad and Ott, German and Rosenwald, Andreas and Str{\"o}bel, Philipp and Marx, Alexander},
  title     = {Anti-apoptotic signature in thymic squamous cell carcinomas - functional relevance of anti-apoptotic BIRC3 expression in the thymic carcinoma cell line 1889c},
  series = {Frontiers in Oncology},
  volume    = {3},
  journal   = {Frontiers in Oncology},
  number    = {316},
  doi       = {10.3389/fonc.2013.00316},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-132214},
  year      = {2013},
  abstract  = {The molecular pathogenesis of thymomas and thymic arcinomas (TCs) is poorly understood and results of adjuvant therapy are unsatisfactory in case of metastatic disease and tumor recurrence. For these clinical settings, novel therapeutic strategies are urgently needed. Recently, limited sequencing efforts revealed that a broad spectrum of genes that play key roles in various common cancers are rarely affected in thymomas and TCs, suggesting that other oncogenic principles might be important.This made us re-analyze historic expression data obtained in a spectrumof thymomas and thymic squamous cell carcinomas (TSCCs) with a custom-made cDNA microarray. By cluster analysis, different anti-apoptotic signatures were detected in type B3 thymoma and TSCC, including overexpression of BIRC3 in TSCCs. This was confirmed by qRT-PCR in the original and an independent validation set of tumors. In contrast to several other cancer cell lines, the BIRC3-positive TSCC cell line, 1889c showed spontaneous apoptosis after BIRC3 knock-down. Targeting apoptosis genes is worth testing as therapeutic principle in TSCC.},
  language  = {en}
}
@article{TrafimowAmrheinAreshenkoffetal.2018,
  author    = {Trafimow, David and Amrhein, Valentin and Areshenkoff, Corson N. and Barrera-Causil, Carlos J. and Beh, Eric J. and Bilgi{\c{c}}, Yusuf K. and Bono, Roser and Bradley, Michael T. and Briggs, William M. and Cepeda-Freyre, H{\´e}ctor A. and Chaigneau, Sergio E. and Ciocca, Daniel R. and Correa, Juan C. and Cousineau, Denis and de Boer, Michiel R. and Dhar, Subhra S. and Dolgov, Igor and G{\´o}mez-Benito, Juana and Grendar, Marian and Grice, James W. and Guerrero-Gimenez, Martin E. and Guti{\´e}rrez, Andr{\´e}s and Huedo-Medina, Tania B. and Jaffe, Klaus and Janyan, Armina and Karimnezhad, Ali and Korner-Nievergelt, Fr{\"a}nzi and Kosugi, Koji and Lachmair, Martin and Ledesma, Rub{\´e}n D. and Limongi, Roberto and Liuzza, Marco T. and Lombardo, Rosaria and Marks, Michael J. and Meinlschmidt, Gunther and Nalborczyk, Ladislas and Nguyen, Hung T. and Ospina, Raydonal and Perezgonzalez, Jose D. and Pfister, Roland and Rahona, Juan J. and Rodr{\´i}guez-Medina, David A. and Rom{\~a}o, Xavier and Ruiz-Fern{\´a}ndez, Susana and Suarez, Isabel and Tegethoff, Marion and Tejo, Mauricio and van de Schoot, Rens and Vankov, Ivan I. and Velasco-Forero, Santiago and Wang, Tonghui and Yamada, Yuki and Zoppino, Felipe C. M. and Marmolejo-Ramos, Fernando},
  title     = {Manipulating the Alpha Level Cannot Cure Significance Testing},
  series = {Frontiers in Psychology},
  volume    = {9},
  journal   = {Frontiers in Psychology},
  number    = {699},
  issn      = {1664-1078},
  doi       = {10.3389/fpsyg.2018.00699},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-189973},
  year      = {2018},
  abstract  = {We argue that making accept/reject decisions on scientific hypotheses, including a recent call for changing the canonical alpha level from p = 0.05 to p = 0.005, is deleterious for the finding of new discoveries and the progress of science. Given that blanket and variable alpha levels both are problematic, it is sensible to dispense with significance testing altogether. There are alternatives that address study design and sample size much more directly than significance testing does; but none of the statistical tools should be taken as the new magic method giving clear-cut mechanical answers. Inference should not be based on single studies at all, but on cumulative evidence from multiple independent studies. When evaluating the strength of the evidence, we should consider, for example, auxiliary assumptions, the strength of the experimental design, and implications for applications. To boil all this down to a binary decision based on a p-value threshold of 0.05, 0.01, 0.005, or anything else, is not acceptable.},
  language  = {en}
}