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Professor Stefanie Biedermann

Profile summary

Professional biography

I am a Professor of Statistics in the School of Mathematics and Statistics.

I completed my PhD in mathematical Statistics at the Ruhr-Universität Bochum in Germany. After 3 years of postdoctoral research at Bochum, I moved to the University of Southampton as a Lecturer/Associate Professor. I joined the Open University in September 2020.

Research interests

My research focuses on enabling researchers in Medicine and Science to draw accurate conclusions from their experiments. The amount and quality of information gathered from experimental data depends sensitively on the experimental conditions at which the data have been collected, the design of the experiment.

I have also developed a research interest in informative censoring in survival analysis, where I have held a grant by the MRC to develop new modelling approaches for sensitivity analyses.

Here is a quick overview of my research interests.

  • Optimal Design of Experiments (methodology)
  • Medical applications, for example planning clinical trials for incomplete (missing or censored) data
  • Algorithms to find optimal designs numerically
  • Survival Analysis (methodology)
  • Medical applications, for example sensitivity analyses for potentially informatively censored data
  • Mixture experiments (modelling and design), as they occur, for example, in Formulation Chemistry

Here are several examples that showcase my current research plans in more detail:

  • Missing data can cause considerable problems in experiments. A particularly problematic situation arises when the missingness is related to the (unobserved) value of the missing response, which may lead to biased conclusions. I am interested in investigating scenarios where some of the missing responses can be “recovered” e.g. through home visits to patients who missed their clinical appointments. This opens up a whole host of opportunities for statistical research, including modelling, inference and experimental design.
  • Experiments involving mixtures are conducted in a variety of areas, for example in food processing or in chemical research. The experimental region is constrained naturally, as the proportions of all ingredients sum to one. Khashab, Gilmour and Biedermann (2020) propose a new - parsimonious but flexible - class of non-linear models, based on fractional polynomials (Royston and Altman, 1994), to fit the data from constrained mixture experiments. I am interested in collaborations with researchers in Chemistry to develop these models further, and to test them in realistic scenarios.
  • For many years, the field of optimal design of experiments was mostly concerned with theoretical advances. However, in practice, most optimal design problems are too complicated to be solved analytically, and efficient algorithms for numerical design search are required. While the benefits of optimal designs have been well established in various application areas, practitioners cannot use optimal designs unless they are readily available to them. Therefore, it is essential to develop efficient algorithms and to incorporate them into an easy-to-use software package, which can find optimal designs quickly and to a good accuracy. I am interested in collaborations with researchers in Operational Research and Computer Science to do this, and in finding potential users of this research in Science and Industry.

Teaching interests

I am currently on the production team for M348 Applied Statistics, deputy chair for M249 Practical Modern Statistics, and module team member for M140 Introducing Statistics.

External collaborations

I have enjoyed/am enjoying fruitful collaborations with many colleagues in the UK and abroad, for example in the US (Prof Min Yang, UIC), Singapore (Dr Selin Damla Ahipasaoglu, SUTD), Germany (Prof Holger Dette, Dr Nico Bissantz, RUB) and the UK (Dr Robin Mitra, Lancaster/Cardiff, Prof Steve Gilmour, KCL, Prof Dave Woods, Dr Alan Kimber, Southampton).

Publications

D-optimal designs for multiarm trials with dropouts (2019-06-06)
Lee, Kim May; Biedermann, Stefanie and Mitra, Robin
Statistics in Medicine, 38(15) (pp. 2749-2766)


Optimal design when outcome values are not missing at random (2018-10)
Lee, Kim May; Mitra, Robin and Biedermann, Stefanie
Statistica Sinica, 28(4) (pp. 1821-1838)


Simultaneous confidence sets for several effective doses (2018-07-03)
Tompsett, Daniel M.; Biedermann, Stefanie and Liu, Wei
Biometrical Journal, 60(4) (pp. 703-720)


Optimal design for experiments with possibly incomplete observations (2018-07)
Lee, Kim May; Biedermann, Stefanie and Mitra, Robin
Statistica Sinica, 28(3) (pp. 1611-1632)


Model robust designs for survival trials (2017-09)
Konstantinou, Maria; Biedermann, Stefanie and Kimber, Alan
Computational Statistics & Data Analysis, 113 (pp. 239-250)


Optimal designs for full and partial likelihood information — With application to survival models (2015-10)
Konstantinou, Maria; Biedermann, Stefanie and Kimber, Alan C.
Journal of Statistical Planning and Inference, 165 (pp. 27-37)


Optimal designs for two-parameter nonlinear models with application to survival models (2014-01)
Konstantinou, Maria; Biedermann, Stefanie and Kimber, Alan
Statistica Sinica (pp. 415-428)


On Optimal Designs for Nonlinear Models: A General and Efficient Algorithm (2013)
Yang, Min; Biedermann, Stefanie and Tang, Elina
Journal of the American Statistical Association, 108(504) (pp. 1411-1420)


Optimal designs for generalized non-linear models with application to second-harmonic generation experiments (2011-03)
Biedermann, Stefanie and Woods, David C.
Journal of the Royal Statistical Society: Series C (Applied Statistics), 60(2) (pp. 281-299)


Optimal designs for indirect regression (2011)
Biedermann, Stefanie; Bissantz, Nicolai; Dette, Holger and Jones, Edmund
Inverse Problems, 27(10) (p 105003)


Designs for selected non-linear models (2015-06-26)
Biedermann, Stefanie and Yang, Min
In: Dean, Angela; Morris, Max; Stufken, John and Bingham, Derek eds. Handbook of Design and Analysis of Experiments. Handbooks of Modern Statistical Methods (pp. 515-548)
ISBN : 978-1-4665-0433-2 | Publisher : Chapman and Hall/CRC | Published : New York