Innovative Statistical Methods: Development, Application, Consulting, Mediation
The RG Biometrics provides statistical and mathematical methods to support all stages of the research process and for all study designs (experimental, clinical, or epidemiological) within the DDZ. Recent methods are applied, adapted for special needs, translated into accessible software, and, if needed, also newly developed. DDZ researchers, especially younger ones, are accompanied through the whole research process from posing the initial research question, over to study design, data handling and statistical analysis to final publication of results. In terms of own methodical work, the RG Biometrics focusses on statistical methods for meta-analysis, that is, the quantitative summary of medical studies, and on differential equation modelling for describing the dynamics (prevalence, incidence, future burden) of diabetes.
Team
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Firyal Bajwa
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Dr. rer. nat. Pavel Bobrov, Dipl. Math.
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Univ.-Prof. Dr. rer. nat. Ralph Brinks, M. A.
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Nina Ebert, MPH
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Birgit Heinrich
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Katharina Kampmann
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Tjarven-Marit Käsbach
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Tim Mori, M. Sc.
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Abderrazzak Najib
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Katharina Piedboeuf, M. Sc.
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Anke Robert
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Anna Schaffstein-Günther
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Jeanette Smitmans
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Jasmin Stade
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Sabine Stolz
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Dr. PH Thaddäus Tönnies, M. Sc.
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Dr. Jiancong Wang
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Marisa Weiss
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Marielle Wirth, M. Sc.
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Yildiz Yorulmaz
Note that this list also includes the employees of the NAKO Study Center Düsseldorf.
Selected Publications
- Zaharia O-P, Strassburger K, Strom A, Bönhof G, Karusheva Y, Antoniou S, Bódis K, Markgraf D, Burkart V, Müssig K, Hwang JH, Asplund O, Groop L, Ahlqvist E, Seissler J, Nawroth P, Kopf S, Schmid SM, Stumvoll M, Pfeiffer A, Kabisch S, Tselmin S, Häring H-U, Ziegler D, Kuß O, Szendrödi J, Roden M for the GDS Group 2019. Risk of diabetes-associated diseases in subgroups of patients with recent-onset diabetes: a 5-year follow-up study. Lancet Diabetes Endocrinol. 7: 684-694. https://doi.org/10.1016/S2213-8587(19)30187-1
- Hoyer A, Hirt S, Kuss O 2018. Meta-analysis of full ROC curves using bivariate time-to-event models for interval-censored data. Res Synth Methods. 9: 62-72. https://doi.org/10.1002/jrsm.1273
- Hoyer A, Rathmann W, Kuss O 2018. Utility of HbA1c and fasting plasma glucose for screening of Type 2 diabetes: a meta-analysis of full ROC curves. Diabet Med. 35: 317-322. https://doi.org/10.1111/dme.13560
- Kuss O, Blettner M, Börgermann J 2016. Propensity Score: an Alternative Method of Analyzing Treatment Effects: Part 23 of a series on evaluation of scientific publications. Dtsch Arztebl Int. 113: 597-603. https://doi.org/10.3238/arztebl.2016.0597
- Tamayo T, Brinks R, Hoyer A, Kuss O, Rathmann W 2016. The Prevalence and Incidence of Diabetes in Germany. Dtsch Arztebl Int. 113: 177-82. https://doi.org/10.3238/arztebl.2016.0177
- Kuss O 2015. Statistical methods for meta-analyses including information from studies without any events-add nothing to nothing and succeed nevertheless. Stat Med. 34: 1097-1116. https://doi.org/10.1002/sim.6383
- Kuss O, Legler T, Börgermann J 2011. Treatments effects from randomized trials and propensity score analyses were similar in similar populations in an example from cardiac surgery. J Clin Epidemiol. 64: 1076–1084. https://doi.org/10.1016/j.jclinepi.2011.01.005
- Kuss O, von Salviati B, Börgermann J 2010. Off-pump versus on-pump coronary artery bypass grafting: A systematic review and meta-analysis of propensity score analyses. J Thorac Cardiovasc Surg. 140: 829-835. https://doi.org/10.1016/j.jtcvs.2009.12.022
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