Signal Detection for Medical Scientists
Likelihood Ratio Test-based Methodology
- Available for pre-order. Item will ship after June 25, 2021
Signal Detection for Medical Scientists: Likelihood Ratio Based Test-Based Methodology presents the data mining techniques with focus on likelihood ratio test (LRT) based methods for signal detection. It emphasizes computational aspect of LRT methodology and is pertinent for first-time researchers and graduate students venturing into this interesting field.
The book is written as a reference book for professionals in pharmaceutical industry, manufactures of medical devices, and regulatory agencies. The book deals with the signal detection in drug/device evaluation, which is important in the post-market evaluation of medical products, and in the pre-market signal detection during clinical trials for monitoring procedures.
It should also appeal to academic researchers, and faculty members in mathematics, statistics, biostatistics, data science, pharmacology, engineering, epidemiology, and public health. Therefore, this book is well suited for both research and teaching.
- Includes a balanced discussion of art of data structure, issues in signal detection, statistical methods and analytics, and implementation of the methods.
- Provides a comprehensive summary of the LRT methods for signal detection including the basic theory and extensions for varying datasets that may be large post-market data or pre-market clinical trial data.
- Contains details of scientific background, statistical methods, and associated algorithms that a reader can quickly master the materials and apply methods in the book on one’s own problems
Table of Contents
1. Introduction 2. Data Mining Methods for Signal Detection 3. Basic LRT Method 4. LRT Methods for Drug Classes 5. ZIP-LRT Method for Modeling Extra-zeros 6. LRT Method for Active Safety Surveillance with Exposure Information 7. LRT-based Methodologies for Analysis of Multiple Studies 8. LRT Methods in Medical Device Safety Evaluation 9. LRT Method for Multiple-Site Device Data with Continuous Outcomes 10. Use of LRT in Site Selection
Ram C. Tiwari, Ph.D. is the Director for Division of Biostatistics, CDRH, since 2016. He joined FDA in April 2008 as Associate Director for Statistical Science and Policy in the Immediate Office, Office of Biostatistics, CDER. Prior to joining FDA, he served as Program Director and Mathematical Statistician in the Division of Cancer Control and Population Sciences at National Cancer Institute, NIH; and as Professor and Chair, Department of Mathematics, University of North Carolina at Charlotte. Dr. Tiwari received his MS and PhD degrees from Florida State University in Mathematical Statistics. He is a Fellow of the American Statistical Association and a past President of the International Indian Statistical Association. Dr. Tiwari has over 200 publications covering a wide range of topics using both Frequentist and Bayesian methods. His methodological work on Likelihood Ratio Test (LRT) Method for signal detection from large drug/device safety databases, Benefit-Risk Analysis, and Leveraging RWD/RWE in regulatory decision-making, has been recognized by many NCI and FDA Scientific Awards.
Dr. Jyoti Zalkikar is in the Office of Biostatistics at the Food and Drug Administration (FDA)’s Center for Drug Evaluation and Research (CDER). Her team supports the Division of Imaging and Radiation Medicine in CDER’s Office of New Drugs. Dr. Zalkikar received her PhD in Mathematics (with Statistics track) from the University of California at Santa Barbara in 1988. She subsequently joined the faculty of the Department of Statistics at Florida International University in Miami, Florida, and worked there until she joined FDA/CDER in 2001. Dr. Zalkikar also served in this Division of Biostatistics in Center for Devices and Radiological Health, and Office of Science in Center for Tobacco Products during her delegations. She has published about 40 papers in the statistical/clinical literature covering areas in Bayesian statistics, Reliability Theory, and Statistical Applications in Medical Research. Dr. Zalkikar‘ s recent research interests are in the areas of Translational Bioinformatics and Real-world Evidence.
Dr. Lan Huang received her Ph.D. in Statistics from University of Connecticut in 2004. From 2004 to 2009, Dr. Huang worked on cancer surveillance at national cancer institute (NCI). Dr. Huang joined FDA in 2009 as a statistical reviewer. She has reviewed submissions for both therapeutic and diagnostic products/devices and has participated in regulatory research for methodologies to improve the quality of review in statistical analysis in clinical trials and safety surveillance in CDER and CDRH.