دانلود کتاب Mendelian Randomization Methods for Causal Inference Using Genetic Variants

فهرست مطالب :

Cover Half Title Series Page Title Page Copyright Page Contents Preface to the second edition Abbreviations Notation I: Understanding and performing Mendelian randomization 1. Introduction and motivation 1.1. Shortcomings of classical epidemiology 1.2. The rise of genetic epidemiology 1.3. Motivating example: The inflammation hypothesis 1.4. Other examples of Mendelian randomization 1.5. Overview of book 1.6. Summary 2. What is Mendelian randomization? 2.1. What is Mendelian randomization? 2.2. Why use Mendelian randomization? 2.3. A brief overview of genetics 2.4. Classification of Mendelian randomization investigations 2.5. Summary 3. Assumptions for causal inference 3.1. Observational and causal relationships 3.2. Finding a valid instrumental variable 3.3. Testing for a causal relationship 3.4. Example: Lp-PLA2 and coronary heart disease 3.5. Estimating a causal effect 3.6. Summary 4. Estimating a causal effect from individual-level data 4.1. Ratio of coefficients method 4.2. Two-stage methods 4.3. Example: Body mass index and smoking intensity 4.4. Computer implementation 4.5. Summary 5. Estimating a causal effect from summarized data 5.1. Motivating example: interleukin-1 and cardiovascular diseases 5.2. Inverse-variance weighted method 5.3. Heterogeneity and pleiotropy 5.4. Computer implementation 5.5. Example: Body mass index and smoking intensity reprised 5.6. Summary 6. Interpretation of estimates from Mendelian randomization 6.1. Internal and external validity 6.2. Comparison of estimates 6.3. Example: Lipoprotein(a) and coronary heart disease 6.4. Discussion 6.5. Recap of examples considered so far 6.6. Summary II: Advanced methods for Mendelian randomization 7. Robust methods using variants from multiple gene regions 7.1. Motivating example: LDL- and HDL-cholesterol and coronary heart disease 7.2. Consensus methods 7.3. Outlier-robust methods 7.4. Modelling methods 7.5. Other methods and comparison 7.6. Example: LDL- and HDL-cholesterol and coronary heart disease reprised 7.7. Computer implementation 7.8. Summary 8. Other statistical issues for Mendelian randomization 8.1. Weak instrument bias 8.2. Allele scores 8.3. Sample overlap 8.4. Winner's curse 8.5. Selection and collider bias 8.6. Covariate adjustment 8.7. Non-collapsibility 8.8. Time and time-varying effects 8.9. Power to detect a causal effect 8.10. Choosing variants from a single gene region 8.11. Binary exposure 8.12. Alternative estimation methods 8.13. Summary 9. Extensions to Mendelian randomization 9.1. Multivariable Mendelian randomization 9.2. Network Mendelian randomization 9.3. Non-linear Mendelian randomization 9.4. Factorial Mendelian randomization 9.5. Bidirectional Mendelian randomization 9.6. Mendelian randomization and meta-analysis 9.7. Summary 10. How to perform a Mendelian randomization investigation 10.1. Motivation and scope 10.2. Data sources 10.3. Selection of genetic variants 10.4. Variant harmonization 10.5. Primary analysis 10.6. Robust methods for sensitivity analysis 10.7. Other approaches for sensitivity analysis 10.8. Data presentation 10.9. Interpretation 10.10. Summary III: Prospects for Mendelian randomization 11. Future directions 11.1. GWAS: large numbers of genetic variants 11.2. -omics: Large numbers of risk factors 11.3. Hypothesis-free: Large numbers of outcomes 11.4. Biobanks: Large numbers of participants 11.5. Clever designs: The role of epidemiologists 11.6. Conclusion Bibliography Index