Contemporary Biostatistics with Biopharmaceutical Applications (eBook) von Lanju Zhang

Part I Biostatistical Methodology.- Dimension Reduction in High Dimensional Multivariate Time Series Analysis.- Multi-Panel Kendall Plot Applied to Measuring Dependence.- Flexible Optimal Design Strategies.- A Multivariate Spatial Modelling Approach with Nonparametric Cross-covariogram.- A Deterministic Global Optimization Method for Variational Inference.- Part II Statistical Genetics and Bioinformatics.- Subgroup identification with latent Dirichlet allocation.- Dictionary learning based genotype imputation to improve power for association testing.- Integrating Transcriptional Time Lag Information into Gene Regulatory Network Construction.- Optimal experimental designs for fMRI when the model matrix is uncertain.- On Exact and Approximate Distributions of K-homopolymer for iid and Markov Dependent DNA Sequences.- Part III Regulatory Statistics.- Utilizing Seamless Adaptive Designs for NASH Clinical Trials.- A Bayesian Non-inferiority Design with Companion Constancy Test in Active Controlled Trials.- A Study Design for Utilizing External Data to Augment the Control in a Randomized Controlled Trial.- Some thoughts in designing a Bayesian study: From a statistical reviewers perspective.- On Weighted Performance Goals in Medical Device Single-Arm Clinical Studies.- Part IV Biopharmaceutical Research and Applications.-Current Status Data in the Presence of a Terminal Event.- Seamless Phase 2/3 Study Design with an Oncology Example.- A Bayesian meta-analysis method for estimating risk difference of rare events.- Comparison of multi-arm multi-stage design and adaptive randomization in platform clinical trials.- A Calibrated Power Prior Approach to Borrow Information from Historical Data with Application.- A Gatekeeping Test in a Group Sequential Design with Multiple Interim Looks.- Application of Bayesian Methods in Oncology Dose Escalation Studies with Late Onset Toxicity.- Bayesian hierarchical model estimation and comparison of immunogenicity assay cut-points.-Inference for Two-Stage Dynamic Treatment Regimes in the Presence of Drop.- Comparison of different approaches for dynamic prediction of survival using longitudinal data.- Update on progress of ASA Biopharm Safety Monitoring Working Group.- Options for implementing pattern-mixture-based sensitivity analyses.

This edited volume presents current research in biostatistics with emphasis on biopharmaceutical applications. Featuring contributions presented at the 2017 ICSA Applied Statistics Symposium held in Chicago, IL on June 25 to 28, 2017, this book explores timely topics that have a high potential impact on statistical methodology and future research in biostatistics and biopharmaceuticals. The theme of this conference was Statistics for a New Generation: Challenges and Opportunities, in recognition of the advent of a new generation of statisticians. The conference attracted statisticians working in academia, government, and industry; domestic and international statisticians. From the conference, the editors selected 28 high-quality presentations and invited the speakers to prepare full chapters for this book. These contributions are divided into four parts: Part I Biostatistical Methodology, Part II Statistical Genetics and Bioinformatics, Part III Regulatory Statistics, and Part IV Biopharmaceutical Research and Applications.

Featuring contributions on topics such as statistics in genetics, bioinformatics, biostatistical methodology, and statistical computing, this book is beneficial to researchers, academics, practitioners and policy makers in biostatistics and biopharmaceuticals.  

About the Editors:

Lanju Zhang is Director in Statistics and Research Fellow at the Department of Data and Statistical Sciences at AbbVie. He is leading a group providing statistical support to emerging immunology clinical programs. Prior to that, he was Head of Nonclinical Statistical Group at Abbvie and MedImmune. His research interests include adaptive design, multi-region clinical trials, real world evidence, and nonclinical statistics. He has published two books (both with Springer) and more than 30 papers. He is an associate editor ofJournal of Biopharmaceutical Statistics. He received his PhD in Statistics in 2005 from University of Maryland Baltimore County.

Ding-Geng Chen is a Fellow of American Statistical Association and currently the Wallace H. Kuralt Distinguished Professor at the University of North Carolina at Chapel Hill. He was a professor at the University of Rochester and the Karl E. Peace Endowed Eminent Scholar Chairin Biostatistics at Georgia Southern University. He is also a senior statistics consultant for biopharmaceuticals and government agencies with extensive expertise in Monte-Carlo simulations, clinical trial biostatistics and public health statistics. Professor Chen has more than 150 referred professional publications and co-authored and co-edited 18 books on clinical trial methodology, meta-analysis and public health applications and he has been invited nationally and internationally to give speeches on his research.

Hongmei Jiang is an associate professor of statistics at Northwestern University. Her current research focuses on developing statistical methods and computational algorithms to analyze and understand the massive amount of data generated by high throughput biological technologies, such as microarray and next generation sequencing technologies. She has published over 40 research articles in statistics and biostatistics research fields. She was the Co-Chair for the 26th ICSA Applied Statistics Symposium, which was successfully held in Chicago in June 2017.

Gang Li is a scientific director, RWE Analytics at Janssen R&D US. He is an elected fellow of the American Statistical Association. Dr. Li received his PhD from the State University of New York at Binghamton. Gang has genuine interest in statistical research and has published a number of papers on multi-regional clinical trials, adaptive design and dose finding, and non-inferiority study design in leading statistical journals.

Hui Quan received his PhD in statistics from Columbia University in 1990. He is currently an associate VP and global head of the methodology group at the Biostatistics and Programming Department of Sanofi. He has 26 years of pharmaceutical industry experience in many therapeutic areas ranging from early phase to Phase IV studies. He has published 93 papers including 69 statistical papers. He is a co-author/co-editor of two books. He has also served as an associate editor for two journals. His research interests include multivariate analysis, safety analysis, multiplicity adjustment, missing data handling, adaptive design, integrated data analysis, modeling and simulation, benefit/risk assessment and multi-regional clinical trials. He was elected as a fellow of the American Statistical Association in 2008.