Modern Inference Based on Health-Related Markers

Biomarkers and Statistical Decision Making

1st Edition - March 18, 2024
Editors: Albert Vexler, Jihnhee Yu, Jiaojiao Zhou
Language: English
Paperback ISBN:
9 7 8 - 0 - 1 2 - 8 1 5 2 4 7 - 8
eBook ISBN:
9 7 8 - 0 - 1 2 - 8 1 5 2 4 8 - 5

Modern Inference Based on Health Related Markers: Biomarkers and Statistical Decision Making provides a compendium of biomarkers based methodologies for respective health related f… Read more

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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. The 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.

In addition, 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.

Cover image
Title page
Table of Contents
Copyright
Contributors
Preface
Chapter 1. An array of statistical concepts and tools for handling challenging data
1. Preliminaries and basic components of relevant statistical instrument assortment
2. Statistical approaches for problems of biomarker measurements
3. A maximum likelihood approach to analyzing incomplete longitudinal data exemplified by mice tumor development
4. Evaluating the effectiveness of interventions based on unbalanced data
Appendix
Chapter 2. A review of expected P-values and their applications in biomarkers studies
1. Introduction
2. The EPV in the context of an ROC curve analysis
3. Multiple testing problems
4. Examples
5. Monte Carlo study
6. Real data example
7. Discussion
Appendix
Chapter 3. Latent class modeling approaches for studying the effects of chemical mixtures on disease risk
1. Introduction
2. Latent class model
3. Latent class model for bivariate chemical patterns
4. A latent function approach
5. Discussion
Chapter 4. Incomplete data in health studies
1. Introduction
2. Types of missing data
3. Methods
4. Statistical results
5. Discussion
Chapter 5. An introduction to biomarkers in translational research (2023)
1. Introduction
2. Biomarker discovery
3. Biomarker validation
4. Clinical research involving biomarker-derived targeted therapies
5. Guidelines and conclusions
Chapter 6. Collection and handling of biomarkers of inorganic arsenic exposure in statistical analyses
1. Introduction
2. Types of biomarkers of inorganic arsenic exposure
3. Adjustments of urine dilution for urinary inorganic arsenic concentrations
4. Data analysis
5. Conclusion
Chapter 7. Efficient sample pooling strategies for COVID-19 data gathering
1. Introduction
2. The Fisher information of pooled sampling
3. Optimization
4. Conclusions and discussion
Chapter 8. Implications of childhood neighborhood quality for young adult parasympathetic reactivity
1. Introduction
2. The current study
3. Method
4. Results
5. Discussion
Chapter 9. Application of adaptive designs in clinical research
1. Introduction
2. Covariate adaptive randomization
3. Response adaptive randomization
4. Discussion
Chapter 10. Comparison of multivariate pooling strategies based on skewed data in light of the receiver operating characteristic curve analysis
1. Introduction
2. Estimations of parameters based on pooled and unpooled data
3. ROC curves
4. Bivariate markers: Parameters' estimation based on pooled and unpooled data
5. Best combination of biomarkers
6. Monte Carlo simulations
7. Real data analysis
8. Discussion
Appendix
Chapter 11. ROC methods in biomarker development
1. Introduction
2. The bivariate-ROC model
3. Other ROC models
Chapter 12. Introduction of diffusion tensor imaging data: An overview for novice users
1. Introduction
2. Topics
3. Concluding remarks
Appendix
Chapter 13. Genome-driven cancer site characterization: An overview of the hidden genome model
1. Introduction
2. The hidden genome model: An overview
3. Data analysis
4. Discussion
Appendix
Chapter 14. Thalamic volumetry via deep learning as an imaging biomarker in multiple sclerosis
1. The thalamus as a potential biomarker
2. Deep learning system and training data
3. Validation of the proposed biomarker
4. Implications and discussion
Index

Albert Vexler

Dr. Albert Vexler’s PhD degree in Statistics and Probability Theory was obtained from the Hebrew University of Jerusalem in 2003. His PhD advisor was Marcy Bogen, Professor, Fellow of the American Statistical Association. Dr. Vexler was a postdoctoral research fellow in the Biometry and Mathematical Statistics Branch at the National Institute of Child Health and Human Development. Currently, Dr. Vexler is Professor at the State University of New York at Buffalo, Department of Biostatistics. Dr. Vexler has authored and co-authored various publications that contribute to both the theoretical and applied aspects of statistics. His papers and statistical software developments have appeared in statistical and biostatistical journals, which have the top rated impact factors and are historically recognized as the leading scientific journals. Dr. Vexler was awarded a National Institute of Health (NIH) grant to develop novel nonparametric data analysis and statistical methodology. The results of this effort can be found via a public access resource housed by the US National Library of Medicine. Dr. Albert Vexler has belonged to the first cohort of investigators that proposed and discovered novel density-based empirical likelihood methodology. He has introduced the density-based empirical likelihood approach for creating nonparametric test statistics that efficiently approximate optimal parametric Neyman-Pearson statistics using minimum distribution assumptions on data. Recently, several statistical academic books referred the density-based empirical likelihood methodology to classical statistical procedures.

Affiliations and expertise

PhD Professor, The State University of New York at Buffalo, Department of Biostatistics, USA

Jihnhee Yu

Dr. Jihnhee Yu obtained her PhD in Statistics from Texas A&M University in 2003. Currently Dr. Yu is Associate Professor at the State University of New York at Buffalo, Department of Biostatistics, and Director, Population Health and Health Observatory, School of Public Health and Health Profession, SUNY at Buffalo. She has authored and coauthored scientific papers published in several peer-reviewed journals throughout her career. Dr. Yu main research interests are clinical trials designs, parametric and nonparametric likehood approach.

Affiliations and expertise

PhD Associate Professor, The State University of New York at Buffalo, Department of Biostatistics, USA

Jiaojiao Zhou

SUNY Distinguished Professor, Dept. of Biostatistics, SPHHP, Assistant Director, IHI at University at Buffalo, Adjunct Professor, Computer Science

Affiliations and expertise

SUNY Distinguished Professor, Dept. of Biostatistics, SPHHP, Assistant Director, IHI at University, USA