Statistical Methods in Biomarker and Early Clinical Development

This contributed volume offers a much-needed overview of the statistical methods in early clinical drug and biomarker development. Chapters are written by expert statisticians with extensive experience in the pharmaceutical industry and regulatory agencies. Because of this, the data presented is often accompanied by real world case studies, which will help make examples more tangible for readers. The many applications of statistics in drug development are covered in detail, making this volume a must-have reference.
Biomarker development and early clinical development are the two critical areas on which the book focuses. By having the two sections of the book dedicated to each of these topics, readers will have a more complete understanding of how applying statistical methods to early drug development can help identify the right drug for the right patient at the right dose. Also presented are exciting applications of machine learning and statistical modeling, along with innovative methods and state-of-the-art advances, making this a timely and practical resource.
This volume is ideal for statisticians, researchers, and professionals interested in pharmaceutical research and development. Readers should be familiar with the fundamentals of statistics and clinical trials.

Presents statistical methods in early clinical drug and biomarker development in one convenient volume Includes chapters written by experts in the pharmaceutical industry Emphasizes practical applications by highlighting real-world examples, state-of-the-art developments, and innovative methods

Auteur

Liang Fang is an Executive Director and Head of Biostatistics in MyoKardia Inc. His research interests include statistical applications in drug development, precision medicine, and digital health.
Cheng Su is an Executive Director of Data Science & Analytics at BioMarin, Inc. His research interests include statistical applications and tool development in drug discovery, biomarkers, clinical trials design, risk based monitoring, mobile health and big data.

Contenu
Predictive Subgroup/Biomarker Identification and Machine Learning Methods.- Characterize and Dichotomize a Continuous Biomarker.- Surrogate Biomarkers.- Innovative Designs for Biomarker Guided Trials.- Statistical Considerations in the Development of Companion Diagnostic Device.- Biomarker Assay Development, Qualification and Validation.- Validation of Genomic Based Assay.- Clinical Application of Molecular Features in Therapeutic Selection and Drug Development.- Big data, real-world data, and machine learning.- Design and Analysis of Clinical Pharmacology Studies.- Statistical Considerations in Proof of Concept Studies.- Safety in Early Phase Studies.- Statistical Evaluation of QT/QTc Interval Prolongation.- Phase II Dose Finding.- Pharmacometrics.