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Prior estimation and Bayesian inference from large cohort data sets

Please always quote using this URN: urn:nbn:de:0297-zib-57475
  • One of the main goals of mathematical modelling in systems biology related to medical applications is to obtain patient-specific parameterisations and model predictions. In clinical practice, however, the number of available measurements for single patients is usually limited due to time and cost restrictions. This hampers the process of making patient-specific predictions about the outcome of a treatment. On the other hand, data are often available for many patients, in particular if extensive clinical studies have been performed. Using these population data, we propose an iterative algorithm for contructing an informative prior distribution, which then serves as the basis for computing patient-specific posteriors and obtaining individual predictions. We demonsrate the performance of our method by applying it to a low-dimensional parameter estimation problem in a toy model as well as to a high-dimensional ODE model of the human menstrual cycle, which represents a typical example from systems biology modelling.

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Author:Ilja Klebanov, Alexander SikorskiORCiD, Christof Schütte, Susanna RöblitzORCiD
Document Type:ZIB-Report
MSC-Classification:62-XX STATISTICS
Date of first Publication:2016/02/16
Series (Serial Number):ZIB-Report (16-09)
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