Mendelian randomization (MR) uses genetic instrumental variables to make inferences about causal effects based on observational data. It, therefore, can be a reliable way of assessing the causal nature of risk factors, such as biomarkers, for a wide range of disease outcomes.
Mendelian randomization (MR) uses genetic instrumental variables to make inferences about causal effects based on observational data. It, therefore, can be a reliable way of assessing the causal nature of risk factors, such as biomarkers, for a wide range of disease outcomes.
Dr Stephen Burgess is an MRC Investigator at the MRC Biostatistics Unit in Cambridge, an internationally acclaimed research institute in medical statistics. He holds a Wellcome Trust Sir Henry Dale Fellowship, and leads a research group which aims to develop statistical methods that use genetic variation to answer clinically relevant questions about disease aetiology and prevention. He was previously located at the Cardiovascular Epidemiology Unit in the University of Cambridge, where he held a Sir Henry Wellcome Postdoctoral Fellowship. His main research interests are in causal inference and evidence synthesis. Professor Simon Thompson was Director of Research in Biostatistics at the Cardiovascular Epidemiology Unit in the University of Cambridge until his retirement in 2018. He is a Fellow of the Academy of Medical Sciences. From 2000-2011, he was Director of the MRC Biostatistics Unit in Cambridge. He held previous academic appointments at the London School of Hygiene and Tropical Medicine, and as the first Professor of Medical Statistics and Epidemiology at Imperial College London. In retirement, he has cut down his research activities substantially, and is not getting involved in new research projects.
Inhaltsangabe
I Understanding and Performing Mendelian Randomization1. Introduction and Motivation 2. What is Mendelian Randomization? 3. Assumptions for Causal Inference 4. Estimating a Causal Effect from Individual-level Data 5. Estimating a Causal Effect from Summarized Data 6. Interpretation of Estimates from Mendelian Randomization II Advanced Methods for Mendelian Randomization7. Robust Methods using Variants from Multiple Gene Regions 8. Other Statistical Issues for Mendelian Randomization 9. Extensions to Mendelian Randomization 10. How to Perform a Mendelian Randomization Investigation III Prospects for Mendelian Randomization11. Future Directions
I Understanding and Performing Mendelian Randomization1. Introduction and Motivation 2. What is Mendelian Randomization? 3. Assumptions for Causal Inference 4. Estimating a Causal Effect from Individual-level Data 5. Estimating a Causal Effect from Summarized Data 6. Interpretation of Estimates from Mendelian Randomization II Advanced Methods for Mendelian Randomization7. Robust Methods using Variants from Multiple Gene Regions 8. Other Statistical Issues for Mendelian Randomization 9. Extensions to Mendelian Randomization 10. How to Perform a Mendelian Randomization Investigation III Prospects for Mendelian Randomization11. Future Directions
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