The University of Southampton
Mathematical Sciences
Phone:
(023) 8059 3672
Email:
S.Biedermann@soton.ac.uk

Dr Stefanie Biedermann 

Associate Professor

Dr Stefanie Biedermann's photo
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Dr Stefanie Biedermann is an Associate Professor within Mathematical Sciences at the University of Southampton.

Background

Since 2014 Senior Lecturer in Statistics, University of Southampton, UK

Since 2006 Lecturer in Statistics, Mathematics, University of Southampton, UK

2003-2006 Post Doctoral Research Worker in two parallel projects:

Optimal design of experiments and Statistical Genetics, Department of Mathematics, Ruhr-Universität Bochum, Germany

2003 Dr. rer. nat., Department of Mathematics, Ruhr-Universität Bochum, Germany

2000 Diploma in Mathematics with Economics, Department of Mathematics, Ruhr-Universität Bochum, Germany

Research

Responsibilities

Publications

Teaching

Contact

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 sampled, the design of the experiment. My approach to designing experiments is rather theoretical, aiming to provide a general structure of optimal designs for broad classes of statistical models. This will facilitate the determination of optimal designs for specific applications. I recently developed a research interest in informative censoring in survival analysis, where I have been awarded a grant by the MRC to develop new modelling approaches for sensitivity analyses.A representative selection of examples is given below.

Research Projects

Informative censoring in survival statistics - new modelling approaches to improve sensitivity analysis (joint with A. Kimber, funded by the MRC). In survival studies, usually some of the observations are censored. That means the event of interest, e.g. the death of a patient, is not observed. There can be many different reasons for censoring. For example, the study may end before all patients have died.

In some situations, the fact an observation was censored may in itself provide further information about the potential
survival times. Consider for example the waiting list for an organ transplant. If a donor organ becomes available, usually the sickest eligible patient on the list will be prioritised for transplantation. Hence knowing an observation was censored due to transplantation tells us that this patient - if he had remained on the waiting list - would have been more likely to die within a short period of time than the average patient on the list. Ignoring this extra information when analysing the data may lead to incorrect conclusions. For example, survival probabilities for patients on the waiting list may be seriously overestimated since the sickest patients have been removed from the waiting list for transplantation.

This is where our research comes in. Our aim is to provide a sensitivity analysis, which is as realistic as necessary to assess the impact of informative censoring, while still retaining computational simplicity. This includes a general investigation into how complex a model needs to be in order to result in a powerful sensitivity analysis for a broad range of realistic scenarios. This in itself contains many interesting and challenging statistical problems, but our main motivation to pursue this research stems from the potential impact it can have on medical research. We want to encourage practitioners to use sensitivity analyses, and thus prevent them from drawing the wrong conclusions from their data due to informative censoring.

Optimal designs for experiments with potentially censored data (joint with A. Kimber and M. Konstantinou)
Censored data are observed in many industrial or biomedical applications, e.g. survival times in cancer trials. We find efficient designs for such experiments for various censoring mechanisms, which will, for example, enhance the understanding and assessment of the benefits of a new cancer treatment by Medical researchers.

Optimal design of experiments for non-linear models derived from chemical applications (joint with S. Lewis, K. Martin, D. Woods and GlaxoSmithKline)
We find efficient designs for experiments in chemical kinetics to enable reaction processes to be better understood and their yields to be optimised, whilst also allocating experimental resources cost-effectively.

Optimal design of experiments for second harmonic generation (joint with J. Frey (Chemistry) and D. Woods)
Second harmonic generation is an optical process for characterising surfaces and interfaces through counting the number of molecules at the interface of the two phases and determining molecule orientation. The designs found enable our collaborators from Chemistry to accurately determine properties of the chemicals studied.

Research group

Statistics

Research project(s)

Design and Analysis of Experiments

Optimal design of experiments for second harmonic generation in chemistry

  • Programme coordinator for MSc Statistics/Statistics with applications in Medicine
  • Staff representative for PGT programmes on Mathematical Sciences Programmes Board
  • Staff representative for PGT programmes on Mathematical Sciences staff-student liaison group
  • Academic unit representative on Mathematical Sciences UG exam boards and scaling boards

Articles

Conferences

Working Papers

MATH3014 Design & Analysis of Experiments

MATH3044 Statistical Inference

Dr Stefanie Biedermann
School of Mathematics Building 54 University of Southampton Highfield Campus Southampton SO17 1BJ Phone: 023 8059 3672 Email: S.Biedermann@southampton.ac.uk

Room Number:54/9027

Telephone:(023) 8059 3672
Email:S.Biedermann@soton.ac.uk

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