Bayesian Data Analysis for Sets of Data Sets: Towards Populations of Virtual Chimeras for In-Silico Trials

点击翻译按钮获取中文摘要

This project proposes to innovate development of medical treatments that mainly relies on small-scale traditional clinical trials. The trials may have high cost and low success rate preventing the development required to face the growing demand on healthcare. Under-representation of natural variability of subjects undermines the outcome and credibility of the trials. The project goal is to substitute human participants with a diverse and large-scale population of virtual subjects or surrogates carrying out relatively inexpensive computer-based simulations corresponding to in-silico trials combining research expertise of the project investigators of statistical modelling and domain knowledge. The University of Leeds and its associated research institutes are among the UK forerunners on developing in-silico trials for human health and wellbeing providing an excellent research environment for sharing knowledge and advancing state-of-the-art developments. Successful in-silico trials rely on the quality of the virtual population and the main challenge is to build a model for generating synthetic data (initial values) for the simulations. The project proposes to build realistic generative population models that capture natural and rich heterogeneity/variability in a meaningful manner, preserve the properties of the real population and, importantly, generate/predict high-quality data conditioning on descriptive statistics of the virtual subjects. Based on the data provided by the UK Biobank, already accessible to the project investigators, this project focuses on building such models in a data-driven manner delivering necessary research for carrying out in-silico trials by combining information from multiple data sets over a diverse population. The key idea is that the multiple data sets may complement each other and enforce potentially weakly shared information between them to build collective and holistic statistical population models providing plausible and realistic surrogates of real subjects. The project is validated by the ability to replicate the outcome of existing clinical trials with real populations creating the basis for future in-silico trials.