DrCosetta Minelli
Emeritus Reader in Medical Statistics
National Heart & Lung Institute - Faculty of Medicine
Orcid identifier0000-0001-9166-3958 (opens in a new tab)
- Emeritus Reader in Medical StatisticsNational Heart & Lung Institute - Faculty of Medicine
- G 49, Emmanuel Kaye Building, Royal Brompton Campus, United Kingdom
BIO
Respiratory Epidemiology, Occupational Medicine & Public Health
Department info at http://goo.gl/opezWG
Cosetta Minelli is Reader in Medical Statistics at the National Heart and Lung Institute, Imperial College London. She qualified in Medicine at the University of Brescia, Italy, where she specialised in Intensive Care Medicine. She left clinical practice in 1999 and later moved to the UK, where she obtained an MSc in Medical Statistics in 2002 and a PhD in Medical Statistics in 2006, from the University of Leicester.
Her main interest is in evidence synthesis using Bayesian methods, with current research focusing on: Bayesian hierarchical models to incorporate evidence from different sources in the meta-analysis of candidate-gene and genome-wide association studies; Bayesian variable selection methods for high dimensional models for the prediction of childhood asthma; Bayesian methods for fitting regression models to a combination of studies with different sets of variables (including clinical, environmental and genetic data) for COPD prediction. She is also interested in methods for Mendelian randomisation, where genes are used as instruments to investigate causality in the relationship between modifiable risk factors and diseases.
Department info at http://goo.gl/opezWG
Cosetta Minelli is Reader in Medical Statistics at the National Heart and Lung Institute, Imperial College London. She qualified in Medicine at the University of Brescia, Italy, where she specialised in Intensive Care Medicine. She left clinical practice in 1999 and later moved to the UK, where she obtained an MSc in Medical Statistics in 2002 and a PhD in Medical Statistics in 2006, from the University of Leicester.
Her main interest is in evidence synthesis using Bayesian methods, with current research focusing on: Bayesian hierarchical models to incorporate evidence from different sources in the meta-analysis of candidate-gene and genome-wide association studies; Bayesian variable selection methods for high dimensional models for the prediction of childhood asthma; Bayesian methods for fitting regression models to a combination of studies with different sets of variables (including clinical, environmental and genetic data) for COPD prediction. She is also interested in methods for Mendelian randomisation, where genes are used as instruments to investigate causality in the relationship between modifiable risk factors and diseases.
FACULTY
- Faculty of Medicine
POSITION NAME
- Emeritus Reader in Medical Statistics