Showing 1-12 of funded research projects

In this project, we develop innovative approaches to the synthesis and analysis of clinical-epidemiological (CE) and high dimensional laboratory (HDL) data, and modify governance models for cloud-based repositories elaborated by and for scientists in high-income countries to meet the specific challenges of synthesizing CE and HDL data and sharing data across international cohorts and with the Open Science community.

Using big data analytics to develop flexible and improved cardiovascular risk prediction algorithms integrated and 'live' in routine clinical practice in order to improve cardiovascular prevention.

We will use individual participant data to develop and validate a prognostic model in women predicting the outcome of uncomplicated urinary tract infection given current signs, symptoms, point of care test results, and use of antibiotic treatment.

We propose a series of five systematic reviews to identify and appraise prognostic studies of established biomarkers for stratification of heart failure patients.

This project aims to develop a statistical framework that utilizes big datasets to identify and account for heterogeneity in predictor effects. Hereto, existing meta-analysis methods will be improved upon and a new penalization algorithm will be derived to enhance the estimation and inclusion of relevant predictors during the adaptation and development of prediction models. Ultimately, this will reduce the need for further validation and adaptation of prediction models.

This project aims to develop and disseminate training and guidance tailored to Cochrane authors, editors and methodologists, addressing the necessary skills for systematic reviews of prognostic studies.

The overall aim of this project is to evaluate and improve prevailing approaches, and to develop novel methods where needed, for investigating and interpreting subgroup effects in treatment response when multiple IPD sets are available. It should lead to more credible evidence about whether a relevant subgroup effect exists.

This project aims to compare systematically review and meta-analyse prognostic factors and prediction models for end stage renal disease, cardiovascular outcome and mortality in chronic kidney disease.

This project aims to facilitate the conduct of systematic reviews in prognostic research by providing methodological guidance to meta-analyze the performance of existing prediction models, and to meta-analyze the incremental value of certain prognostic factors.

The GETREAL project aims to develop new approaches for incorporating real life data into drug development, and pave the way for a greater consensus on this issue.

This project aims to develop a framework for obtaining valid and generalisable clinical prediction models for situations where individual patient data are available, and for situations where only published prediction models are available.

The plan for this research is to create a regional facility at the University of Birmingham in which to investigate the best ways to design, conduct and analyse trials, providing advice, support and training to those carrying out trials and to the public.