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Center for Data and Simulation Science

Innovative approaches to the design and analysis of medical research studies are required to derive both valid and timely conclusions based on data from medium sized to ever smaller experiments (preclinical to health services research). My interests include mixed models to be fitted to clustered outcome data over time, possibly incorporating information from various biomarkers (e.g. “omics”, PK/PD), as well as diagnostic and prognostic tools (e.g. risk scores).

The figure taken from reference 5 shows the graphical representation of a flexible two-stage design, typically for a clinical trial, in form of a chance tree.

Selected publications

  1. Kuhr K, Wirth D, Srivastava K, Lehmacher W, Hellmich M. First-line therapy for non-transplant eligible patients with multiple myeloma: direct and adjusted indirect comparison of treatment regimens on the existing market in Germany. Eur J Clin Pharmacol. 2016;72(3):257-65.
  2. Fuhr U, Hellmich M. Channeling the flood of meta-analyses. Eur J Clin Pharmacol. 2015;71(6):645-7.
  3. 3. Herich L, Lehmacher W, Hellmich M. Drop the Likelihood Ratio. A Novel Non-electronic Tool for Interpreting Diagnostic Test Results. Methods Inf Med. 2015;54(3):283-7.
  4. Weiß V, Schmidt M, Hellmich M. A novel nonparametric measure of explained variation for survival data with an easy graphical interpretation. GMS Ger Med Sci. 2015;13:Doc18. DOI: 10.3205/000222, URN: urn:nbn:de:0183-0002220
  5. Hellmich M. Flexible designs of clinical trials – a graphical representation in form of a chance tree. GMS Med Inform Biom Epidemiol. 2010;6(1):Doc02. DOI: 10.3205/mibe000102, URN: urn:nbn:de:0183-mibe0001024