Statistical Modeling for Biological Systems

1. Front Matter

   Pages i-xxii

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2. Research Articles

   1. Front Matter

      Pages 1-1

      PDF

   2. Stochastic Models of Cell Proliferation Kinetics Based on Branching Processes

      • Nikolay M. Yanev

      Pages 3-20

   3. Age-Dependent Branching Processes with Non-homogeneous Poisson Immigration as Models of Cell Kinetics

      • Ollivier Hyrien, Nikolay M. Yanev

      Pages 21-46

   4. A Study of the Correlation Structure of Microarray Gene Expression Data Based on Mechanistic Modeling of Cell Population Kinetics

      • Linlin Chen, Lev Klebanov, Anthony Almudevar, Christoph Proschel, Andrei Yakovlev

      Pages 47-61

   5. Correlation Between the True and False Discoveries in a Positively Dependent Multiple Comparison Problem

      • Xing Qiu, Rui Hu

      Pages 63-79

   6. Multiple Testing Procedures: Monotonicity and Some of Its Implications

      • Alexander Y. Gordon

      Pages 81-96

   7. Applications of Sequential Methods in Multiple Hypothesis Testing

      • Anthony Almudevar

      Pages 97-115

   8. Multistage Carcinogenesis: A Unified Framework for Cancer Data Analysis

      • Suresh Moolgavkar, Georg Luebeck

      Pages 117-136

   9. A Machine-Learning Algorithm for Estimating and Ranking the Impact of Environmental Risk Factors in Exploratory Epidemiological Studies

      • Jessica G. Young, Alan E. Hubbard, Brenda Eskenazi, Nicholas P. Jewell

      Pages 137-156

   10. A Latent Time Distribution Model for the Analysis of Tumor Recurrence Data: Application to the Role of Age in Breast Cancer

       • Yann De Rycke, Bernard Asselain

       Pages 157-167

   11. Estimation of Mean Residual Life

       • W. J. Hall, Jon A. Wellner

       Pages 169-189

   12. Likelihood Transformations and Artificial Mixtures

       • Alex Tsodikov, Lyrica Xiaohong Liu, Carol Tseng

       Pages 191-209

   13. On the Application of Flexible Designs When Searching for the Better of Two Anticancer Treatments

       • Christina Kunz, Lutz Edler

       Pages 211-243

   14. Parameter Estimation for Multivariate Nonlinear Stochastic Differential Equation Models: A Comparison Study

       • Wei Gu, Hulin Wu, Hongqi Xue

       Pages 245-258

   15. On Frailties, Archimedean Copulas and Semi-Invariance Under Truncation

       • David Oakes

       Pages 259-278

3. Short Communications

   1. Front Matter

      Pages 279-279

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   2. The Generalized ANOVA: A Classic Song Sung with Modern Lyrics

      • Hui Zhang, Xin Tu

      Pages 281-287

   3. Analyzing Gene Pathways from Microarrays to Sequencing Platforms

      • Jeffrey Miecznikowski, Dan Wang, Xing Ren, Jianmin Wang, Song Liu

      Pages 289-296

   4. A New Approach for Quantifying Uncertainty in Epidemiology

      • Elart von Collani

      Pages 297-309

This book commemorates the scientific contributions of distinguished statistician, Andrei Yakovlev. It reflects upon Dr. Yakovlev’s many research interests including stochastic modeling and the analysis of micro-array data, and throughout the book it emphasizes applications of the theory in biology, medicine and public health. The contributions to this volume are divided into two parts. Part A consists of original research articles, which can be roughly grouped into four thematic areas: (i) branching processes, especially as models for cell kinetics, (ii) multiple testing issues as they arise in the analysis of biologic data, (iii) applications of mathematical models and of new inferential techniques in epidemiology, and (iv) contributions to statistical methodology, with an emphasis on the modeling and analysis of survival time data. 
Part B consists of methodological research reported as a short communication, ending with some personal reflections on researchfields associated with Andrei and on his approach to science. The Appendix contains an abbreviated vitae and a list of Andrei’s publications, complete as far as we know. 
The contributions in this book are written by Dr. Yakovlev’s collaborators and notable statisticians including former presidents of the Institute of Mathematical Statistics and of the Statistics Section of the AAAS. Dr. Yakovlev’s research appeared in four books and almost 200 scientific papers, in mathematics, statistics, biomathematics and biology journals. Ultimately this book offers a tribute to Dr. Yakovlev’s work and recognizes the legacy of his contributions in the biostatistics community.

• Statistical Modeling
• Biological Systems
• Biostatistics
• Computational Biology
• Andrei Yakovlev
• Branching Processes
• Cancer
• Cell Kinetics
• Machine Learning
• Multiple Comparisons
• Survivial Analysis

• Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, USA

  Anthony Almudevar, David Oakes

• Rochester, USA

  Jack Hall

​Anthony Almudevar is an Associate Professor of Biostatistics and Computational Biology at the University of Rochester. He received his Ph.D from the Department of Statistics at the University of Toronto. His research interests include genetics, bioinformatics, optimization and control theory, particularly in the area of Markov Decision Processes. 

David Oakes is a Professor of Biostatistics and Statistics at the University of Rochester. He received his Ph.D from London University. His major research interests are in the area of survival analysis, especially models for the effect of explanatory variables on survival and for multivariate survival data. More recent work has largely focused on the models for bivariate survival models generated by unobserved random effects, often called frailties.


W. Jackson “Jack” Hall, Ph.D., was Emeritus Professor of Statistics and Professor in the Department of Biostatistics and Computational Biology at the University of Rochester. He was an international authority on statistical designs for modern clinical trials. Throughout a distinguished career that spanned more than 40 years in Rochester, Dr. Hall made numerous contributions to the field of mathematical statistics.

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