Modern Inference Based on Health Related Markers: Biomarkers and Statistical Decision Making provides a compendium of biomarkers based methodologies for respective health related fields and health related marker-specific biostatistical techniques. These methodologies may be applied to various problems encountered in medical and epidemiological studies.
This book introduces correct and efficient testing mechanisms including procedures based on bootstrap and permutation methods with the aim of making these techniques assessable to practical researchers. In the biostatistical aspect, it describes how to correctly state testing problems, but it also includes novel results, which have appeared in current statistical publications. The book discusses also modern applied statistical developments that consider data-driven techniques, including empirical likelihood methods and other simple and efficient methods to derive statistical tools for use in health related studies.
The title is a valuable source for biostaticians, practitioners, theoretical and applied investigators, and several members of the biomedical field who are interested in learning more about efficient evidence-based inference incorporating several forms of markers measurements.
1. Chapter 5. An introduction to biomarkers in translational research (2023) - p. 163 2. Chapter 6. Collection and handling of biomarkers of inorganic arsenic exposure in statistical analyses - p. 187 3. Chapter 7. Efficient sample pooling strategies for COVID-19 data gathering - p. 213 4. Chapter 8. Implications of childhood neighborhood quality for young adult parasympathetic reactivity - p. 219 5. Chapter 9. Application of adaptive designs in clinical research - p. 229 6. Chapter 10. Comparison of multivariate pooling strategies based on skewed data in light of the receiver operating characteristic curve analysis - p. 245 7. Abstract - p. 245 8. 1 Introduction - p. 246 9. 2 Estimations of parameters based on pooled and unpooled data - p. 250 10. 3 ROC curves - p. 254 11. 4 Bivariate markers: Parameters' estimation based on pooled and unpooled data - p. 255 12. 5 Best combination of biomarkers - p. 260 13. 6 Monte Carlo simulations - p. 262 14. 7 Real data analysis - p. 268 15. 8 Discussion - p. 270 16. Appendix - p. 271 17. References - p. 280 18. Chapter 11. ROC methods in biomarker development - p. 283 19. Chapter 12. Introduction of diffusion tensor imaging data: An overview for novice users - p. 315 20. Chapter 13. Genome-driven cancer site characterization: An overview of the hidden genome model - p. 355 21. Chapter 14. Thalamic volumetry via deep learning as an imaging biomarker in multiple sclerosis - p. 377 22. Index - p. 399
Modern Inference Based on Health Related Markers: Biomarkers and Statistical Decision Making provides a compendium of biomarkers based methodologies for respective health related fields and health related marker-specific biostatistical techniques. These methodologies may be applied to various problems encountered in medical and epidemiological studies.
This book introduces correct and efficient testing mechanisms including procedures based on bootstrap and permutation methods with the aim of making these techniques assessable to practical researchers. In the biostatistical aspect, it describes how to correctly state testing problems, but it also includes novel results, which have appeared in current statistical publications. The book discusses also modern applied statistical developments that consider data-driven techniques, including empirical likelihood methods and other simple and efficient methods to derive statistical tools for use in health related studies.
The title is a valuable source for biostaticians, practitioners, theoretical and applied investigators, and several members of the biomedical field who are interested in learning more about efficient evidence-based inference incorporating several forms of markers measurements.
1. Chapter 5. An introduction to biomarkers in translational research (2023) - p. 163 2. Chapter 6. Collection and handling of biomarkers of inorganic arsenic exposure in statistical analyses - p. 187 3. Chapter 7. Efficient sample pooling strategies for COVID-19 data gathering - p. 213 4. Chapter 8. Implications of childhood neighborhood quality for young adult parasympathetic reactivity - p. 219 5. Chapter 9. Application of adaptive designs in clinical research - p. 229 6. Chapter 10. Comparison of multivariate pooling strategies based on skewed data in light of the receiver operating characteristic curve analysis - p. 245 7. Abstract - p. 245 8. 1 Introduction - p. 246 9. 2 Estimations of parameters based on pooled and unpooled data - p. 250 10. 3 ROC curves - p. 254 11. 4 Bivariate markers: Parameters' estimation based on pooled and unpooled data - p. 255 12. 5 Best combination of biomarkers - p. 260 13. 6 Monte Carlo simulations - p. 262 14. 7 Real data analysis - p. 268 15. 8 Discussion - p. 270 16. Appendix - p. 271 17. References - p. 280 18. Chapter 11. ROC methods in biomarker development - p. 283 19. Chapter 12. Introduction of diffusion tensor imaging data: An overview for novice users - p. 315 20. Chapter 13. Genome-driven cancer site characterization: An overview of the hidden genome model - p. 355 21. Chapter 14. Thalamic volumetry via deep learning as an imaging biomarker in multiple sclerosis - p. 377 22. Index - p. 399
