This book gives an overview on recent development on biostatistics and bioinformatics. It is written by active researchers in these emerging areas. It is intended to give graduate students and new researchers an idea where the frontiers of biostatistics and bioinformatics are, to learn common techniques in use so that they can advance the fields via developing new techniques and new results. It is also intended to provide extensive references so that researchers can follow the threads to learn more comprehensively what the literature is and to conduct their own research. It covers three important topics in biostatistics: Analysis of Survival and Longitudinal Data, Statistical Methods for Epidemiology, and Bioinformatics, where statistics is still advancing rapidly today.

This book presents an overview of recent developments in biostatistics and bioinformatics. Written by active researchers in these emerging areas, it is intended to give graduate students and new researchers an idea of where the frontiers of biostatistics and bioinformatics are as well as a forum to learn common techniques in use, so that they can advance the fields via developing new techniques and new results. Extensive references are provided so that researchers can follow the threads to learn more comprehensively what the literature is and to conduct their own research. In particulars, the book covers three important and rapidly advancing topics in biostatistics: analysis of survival and longitudinal data, statistical methods for epidemiology, and bioinformatics.

Preface

Part Ⅰ Analysis of Survival and Longitudinal Data

Chapter 1 Non- and Semi- Parametric Modeling in Survival Analysis

 1 Introduction

 2 Cox's type of models

 3 Multivariate Cox's type of models

 4 Model selection on Cox's models

 5 Validating Cox's type of models

 6 Transformation models

 7 Concluding remarks

 References

Chapter 2 Additive-Accelerated Rate Model for Recurrent Event

 1 Introduction

 2 Inference procedure and asymptotic properties

 3 Assessing additive and accelerated covariates

 4 Simulation studies

 5 Application

 6 Remarks 

 Acknowledgements

 Appendix

 References

Chapter 3 An Overview on Quadratic Inference Function Approaches for Longitudinal Data

 1 Introduction

 2 The quadratic inference function approach

 3 Penalized quadratic inference function

 4 Some applications of QIF

 5 Further research and concluding remarks

 Acknowledgements

 References

Chapter 4 Modeling and Analysis of Spatially Correlated Data

 1 Introduction

 2 Basic concepts of spatial process

 3 Spatial models for non-normal/discrete data

 4 Spatial models for censored outcome data

 5 Concluding remarks

 References

Part Ⅱ Statistical Methods for Epidemiology

Chapter 5 Study Designs for Biomarker-Based Treatment Selection

 1 Introduction

 2 Definition of study designs

 3 Test of hypotheses and sample size calculation

 4 Sample size calculation

 5 Numerical comparisons of efficiency

 6 Conclusions

 Acknowledgements

 Appendix

 References

Chapter 6 Statistical Methods for Analyzing Two-Phase Studies

 1 Introduction

 2 Two-phase case-control or cross-sectional studies

 3 Two-phase designs in cohort studies

 4 Conclusions

 References

Part Ⅲ Bioinformatics

Chapter 7 Protein Interaction Predictions from Diverse Sources

 1 Introduction

 2 Data sources useful for protein interaction predictions

 3 Domain-based methods

 4 Classification methods

 5 Complex detection methods

 6 Conclusions

 Acknowledgements

 References

Chapter 8 Regulatory Motif Discovery" From Decoding to Meta-Analysis

 1 Introduction

 2 A Bayesian approach to motif discovery

 3 Discovery of regulatory modules

 4 Motif discovery in multiple species

 5 Motif learning on ChiP-chip data

 6 Using nucleosome positioning information in motif discovery

 7 Conclusion

 References

Chapter 9 Analysis of Cancer Genome Alterations Using Singk Nucleotide Polymorphism (SNP) Microarrays

 1 Background

 2 Loss of heterozygosity analysis using SNP arrays

 3 Copy number analysis using SNP arrays

 4 High-level analysis using LOH and copy number data

 5 Software for cancer alteration analysis using SNP arrays

 6 Prospects

 Acknowledgements

 References

Chapter 10 Analysis of ChiP-chip Data on Genome Tiling Microarrays

 1 Background molecular biology

 2 A ChiP-chip experiment

 3 Data description and analysis

 4 Follow-up analysis

 5 Conclusion

 References

Subject Index

Author Index