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Principles of Clinical Cancer Research provides comprehensive coverage of the fundamentals of clinical cancer research, including the full spectrum of methodologies used in the field. For those involved in research or considering research careers, this book offers a mix of practical advice and analytical tools for effective training in theoretical principles as well as specific, usable teaching examples. The clinical oncologist or trainee will find a high-yield, practical guide to the interpretation of the oncology literature and the application of data to real-world settings. Valuable for…mehr
Principles of Clinical Cancer Research provides comprehensive coverage of the fundamentals of clinical cancer research, including the full spectrum of methodologies used in the field. For those involved in research or considering research careers, this book offers a mix of practical advice and analytical tools for effective training in theoretical principles as well as specific, usable teaching examples. The clinical oncologist or trainee will find a high-yield, practical guide to the interpretation of the oncology literature and the application of data to real-world settings. Valuable for both researchers and clinicians who wish to sharpen their skills, this book contains all of the cornerstones and explanations needed to produce and recognize quality clinical science in oncology.
Written from the physician-scientist's perspective, the book lays a strong foundation in preclinical sciences that is highly relevant to careers in translational oncology research along with coverage of population and outcomes research and clinical trials. It brings together fundamental principles in oncology with the statistical concepts one needs to know to design and interpret studies successfully. With each chapter including perspectives of both clinicians and scientists or biostatisticians, Principles of Clinical Cancer Research provides balanced, instructive, and high-quality topic overviews and applications that are accessible and thorough for anyone in the field.
KEY FEATURES:
Gives real-world examples and rationales behind which research methods to use when and why
Includes numerous tables featuring key statistical methods and programming commands used in everyday clinical research
Contains illustrative practical examples and figures in each chapter to help the reader master concepts
Provides tips and pointers for structuring a career, avoiding pitfalls, and achieving success in the field of clinical cancer research
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Inhaltsangabe
Contents Contributors Foreword Ralph R. Weichselbaum Preface I. INTRODUCTION 1. Introduction to Clinical Cancer Research Loren K. Mell 2. Bias and Pitfalls in Cancer Research Mak Djulbegovic, Mia Djulbegovic, and Benjamin Djulbegovic II. TRANSLATIONAL CANCER RESEARCH 3. Principles of Molecular Biology Reem A. Malek and Phuoc T. Tran 4. The Cell Cycle, Cellular Death, and Metabolism Jacqueline Douglass, Andrew Sharabi, and Phuoc T. Tran 5. Metastasis and the Tumor Microenvironment Jacqueline Douglass, Andrew Sharabi, and Phuoc T. Tran 6. Preclinical Methods Hailun Wang and Phuoc T. Tran 7. Cancer Therapeutic Strategies and Treatment Resistance Kekoa Taparra and Phuoc T. Tran 8. Prognostic and Predictive Biomarkers Ariel E. Marciscano and Phuoc T. Tran 9. Working With Industry Swan Lin, Yazdi K. Pithavala, and Sandip Pravin Patel III. POPULATION AND OUTCOMES RESEARCH 10. Study Designs Michael Milligan and Aileen Chen 11. Basic Statistics for Clinical Cancer Research Loren K. Mell, Hanjie Shen, and Benjamin E. Leiby 12. Statistical Modeling for Clinical Cancer Research Sanjay Aneja and James B. Yu 13. Cancer Epidemiology: Measuring Exposures, Outcomes, and Risk Rishi Deka and Loren K. Mell 14. Survivorship: Effects of Cancer Treatment on Long-Term Morbidity Zorimar Rivera-Núñez, Kaveh Zakeri, and Sharad Goyal 15. Longitudinal and Observational Data Jeff Burkeen, Scott Keith, and Jona Hattangadi-Gluth 16. Time-to-Event Analysis Loren K. Mell, Kaveh Zakeri, and Hanjie Shen 17. Machine Learning and High-Dimensional Data Analysis Sanjay Aneja and James B. Yu 18. Health Outcomes and Disparities Research Paige Sheridan and James Murphy 19. Cost-Effectiveness Analysis Reith Roy Sarkar and James Murphy IV. CLINICAL TRIALS 20. Introduction to Clinical Trials Loren K. Mell 21. Early Phase Clinical Trials Ying Yuan, Yanhong Zhou, and Jack J. Lee 22. Late Phase Clinical Trials Karla V. Ballman 23. Quality of Life and Patient-Reported Outcome Analysis Minh Tam Truong and Michael A. Dyer 24. Trials in Cancer Screening, Prevention, and Public Health Rishi Deka and Loren K. Mell 25. Imaging and Technology Trials Aaron B. Simon, Daniel R. Simpson, and Brent S. Rose 26. Adaptive and Innovative Clinical Trial Designs Mark Chang, Xuan Deng, and Qiang (Ed) Zhang 27. Noninferiority and Equivalence Trials Tie-Hua Ng 28. Systematic Reviews and Meta-Analyses Enoch Chang, Nicholas G. Zaorsky, and Henry S. Park V. CONCLUSION 29. Future Directions in Clinical Cancer Research Brandon E. Turner and Aadel A. Chaudhuri Index
Contents Contributors Foreword Ralph R. Weichselbaum Preface I. INTRODUCTION 1. Introduction to Clinical Cancer Research Loren K. Mell 2. Bias and Pitfalls in Cancer Research Mak Djulbegovic, Mia Djulbegovic, and Benjamin Djulbegovic II. TRANSLATIONAL CANCER RESEARCH 3. Principles of Molecular Biology Reem A. Malek and Phuoc T. Tran 4. The Cell Cycle, Cellular Death, and Metabolism Jacqueline Douglass, Andrew Sharabi, and Phuoc T. Tran 5. Metastasis and the Tumor Microenvironment Jacqueline Douglass, Andrew Sharabi, and Phuoc T. Tran 6. Preclinical Methods Hailun Wang and Phuoc T. Tran 7. Cancer Therapeutic Strategies and Treatment Resistance Kekoa Taparra and Phuoc T. Tran 8. Prognostic and Predictive Biomarkers Ariel E. Marciscano and Phuoc T. Tran 9. Working With Industry Swan Lin, Yazdi K. Pithavala, and Sandip Pravin Patel III. POPULATION AND OUTCOMES RESEARCH 10. Study Designs Michael Milligan and Aileen Chen 11. Basic Statistics for Clinical Cancer Research Loren K. Mell, Hanjie Shen, and Benjamin E. Leiby 12. Statistical Modeling for Clinical Cancer Research Sanjay Aneja and James B. Yu 13. Cancer Epidemiology: Measuring Exposures, Outcomes, and Risk Rishi Deka and Loren K. Mell 14. Survivorship: Effects of Cancer Treatment on Long-Term Morbidity Zorimar Rivera-Núñez, Kaveh Zakeri, and Sharad Goyal 15. Longitudinal and Observational Data Jeff Burkeen, Scott Keith, and Jona Hattangadi-Gluth 16. Time-to-Event Analysis Loren K. Mell, Kaveh Zakeri, and Hanjie Shen 17. Machine Learning and High-Dimensional Data Analysis Sanjay Aneja and James B. Yu 18. Health Outcomes and Disparities Research Paige Sheridan and James Murphy 19. Cost-Effectiveness Analysis Reith Roy Sarkar and James Murphy IV. CLINICAL TRIALS 20. Introduction to Clinical Trials Loren K. Mell 21. Early Phase Clinical Trials Ying Yuan, Yanhong Zhou, and Jack J. Lee 22. Late Phase Clinical Trials Karla V. Ballman 23. Quality of Life and Patient-Reported Outcome Analysis Minh Tam Truong and Michael A. Dyer 24. Trials in Cancer Screening, Prevention, and Public Health Rishi Deka and Loren K. Mell 25. Imaging and Technology Trials Aaron B. Simon, Daniel R. Simpson, and Brent S. Rose 26. Adaptive and Innovative Clinical Trial Designs Mark Chang, Xuan Deng, and Qiang (Ed) Zhang 27. Noninferiority and Equivalence Trials Tie-Hua Ng 28. Systematic Reviews and Meta-Analyses Enoch Chang, Nicholas G. Zaorsky, and Henry S. Park V. CONCLUSION 29. Future Directions in Clinical Cancer Research Brandon E. Turner and Aadel A. Chaudhuri Index
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