Institut für Medizinische Statistik - UMG

Thomas Asendorf


AG Klinische Studien
Telefon:
0551-39-5836
E-Mail:
Thomas.Asendorf@med.uni-goettingen.de
Ort:
Humboldtallee 32, Etage 0, 163
Forschungsinteressen

Statistik in der Medizin, insbesondere Fallzahlplanung und nicht-parametrische statistische Verfahren

Kurzer Lebenslauf

[Seit 09/2013] Wissenschaftlicher Mitarbeiter im DFG Projekt: „Blinded sample size reestimation in clinical trials with recurrent event data and time-dependent event rates“, Institut für Medizinische Statistik.
[04/2013 -
09/2013]
Wissenschaftlicher Mitarbeiter im DFG Projekt: „Simultane Konfidenzintervalle für nichtparametrische Effekte in faktoriellen Modellen“, Institut für Medizinische Statistik.
[10/2008 -
09/2013]
Studium der Mathematik an der Georg-August-Universität Göttingen, Abschluss: Master of Science (M.Sc.) mit Schwerpunkt Mathematische Stochastik.
[10/2010 -
03/2013]
Studentische Hilfskraft in den Lehrveranstaltungen Grundlagen der Stochastik, Angewandte Statistik, Maß- und Wahrscheinlichkeitstheorie, Diskrete Stochastik an der Georg-August-Universität Göttingen.

Prüfungsarbeiten

  1. Master of Science (Georg-August-Universität Göttingen, 2013): Adjusting for Covariates in Non-Parametric Simultaneous Inference.
  2. Bachelor of Science (Georg-August-Universität Göttingen, 2011): Global Tests for structured high dimensional Repeated Measures in a two-factorial Model under the Assumption of equal Covariance Matrices and multivariate Normal Distribution.

Publikationen

  1. Asendorf T, Henderson R, Schmidli H and Friede T, (2017) Modelling and sample size reestimation for longitudinal count data with incomplete follow up. Stat Methods Med Res, In Press.
  2. Nörthen A, Asendorf T, Walson PD and Oellerich M, (2017) Diagnostic value of alpha-1-fetoprotein (AFP) as a biomarker for hepatocellular carcinoma recurrence after liver transplantation. Clin Biochem, In Press.
  3. Pauly M, Asendorf T and Konietschke F, (2016) Permutation-based inference for the AUC: A unified approach for continuous and discontinuous data. Biom J, 58(6):1319-1337.

Vorträge

  1. Adaptive Designs Workshop 2017, Cambridge: Blinded Sample Size Reestimationfor Time Dependent Negative Binomial Counts with Time Trends.
  2. Symposium on Blinded Sample Size Reestimation in Clinical Trials 2016, Göttingen: Blinded sample size reestimation for longitudinal count data,
  3. DagStat 2016, Göttingen: Blinded Sample Size Reestimation for Time Dependent Negative Binomial Counts.
  4. Adaptive Designs Workshop 2016, Padua: Blinded Sample Size Reestimation for Time Dependent Negative Binomial Counts with Incomplete Follow-up.
  5. Symposium on Blinded Sample Size Reestimation in Clinical Trials 2015, Göttingen: Sample Size Reestimation for Longitudinal Count Data.
  6. Biometrisches Kolloquium 2015, Dortmund: Blinded Sample Size Reestimation fo rTime Dependent Negative Binomial Observations.
  7. Adaptive Designs Workshop 2015, Köln: Blinded Sample Size Reestimation for Time Dependent Negative Binomial Observations.
  8. Sample Size Reestimation Workshop 2014, Göttingen: Longitudinal count data with negative binomial marginal distribution.
  9. Biometrisches Kolloquium 2014, Bremen: Non-Parametric Multiple Contrast Tests with Covariates.
  10. Adaptive Designs Workshop 2014, Basel: Sample Size Re-estimation for Poisson Counts in Randomized Controlled Clinical Trials.
  11. KSFE 2014, Göttingen: Non-Parametric Multiple Contrast Tests with Covariates.
  12. Sample Size Reestimation Workshop 2013, Göttingen: In fluence of a Poisson-LogNormal Mixture Distribution on Sample Size Reestimation in Clinical Trials with Count Data.
  13. DAGStat 2013, Freiburg: Permutation based Confidence Intervals for the Area under the ROC Curve.
  14. ICSI 2013, Hannover: Non-Parametric Multiple Contrast Tests with Covariates.