Part Ⅰ Analysis of Survival and Longitudinal Data 3
Chapter 1 Non-and Semi-Parametric Modeling in Survival Analysis Jianqing Fan,Jiancheng Jiang 3
1 Introduction 3
2 Cox's type of models 4
3 Multivariate Cox's type of models 14
4 Model selection on Cox's models 24
5 Validating Cox's type of models 27
6 Transformation models 28
7 Concluding remarks 30
References 30
Chapter 2 Additive-Accelerated Rate Model for Recurrent Event Donglin Zeng,Jianwen Cai 35
1 Introduction 35
2 Inference procedure and asymptotic properties 37
3 Assessing additive and accelerated covariates 40
4 Simulation studies 41
5 Application 42
6 Remarks 43
Acknowledgements 44
Appendix 44
References 48
Chapter 3 An Overview on Quadratic Inference Function Approaches for Longitudinal Data John J.Dziak,Runze Li,Annie Qu 49
1 Introduction 49
2 The quadratic inference function approach 51
3 Penalized quadratic inference function 56
4 Some applications of QIF 60
5 Further research and concluding remarks 65
Acknowledgements 68
References 68
Chapter 4 Modeling and Analysis of Spatially Correlated Data Yi Li 73
1 Introduction 73
2 Basic concepts of spatial process 76
3 Spatial models for non-normal/discrete data 82
4 Spatial models for censored outcome data 88
5 Concluding remarks 96
References 96
Part Ⅱ Statistical Methods for Epidemiology 103
Chapter 5 Study Designs for Biomarker-Based Treatment Selection Amy Laird,Xiao-Hua Zhou 103
1 Introduction 103
2 Definition of study designs 104
3 Test of hypotheses and sample size calculation 108
4 Sample size calculation 111
5 Numerical comparisons of efficiency 116
6 Conclusions 118
Acknowledgements 121
Appendix 122
References 126
Chapter 6 Statistical Methods for Analyzing Two-Phase Studies Jinbo Chen 127
1 Introduction 127
2 Two-phase case-control or cross-sectional studies 130
3 Two-phase designs in cohort studies 136
4 Conclusions 149
References 151
Part Ⅲ Bioinformatics 159
Chapter 7 Protein Interaction Predictions from Diverse Sources Yin Liu,Inyoung Kim,Hongyu Zhao 159
1 Introduction 159
2 Data sources useful for protein interaction predictions 161
3 Domain-based methods 163
4 Classification methods 169
5 Complex detection methods 172
6 Conclusions 175
Acknowledgements 175
References 175
Chapter 8 Regulatory Motif Discovery:From Decoding to Meta-Analysis Qing Zhou,Mayetri Gupta 179
1 Introduction 179
2 A Bayesian approach to motif discovery 181
3 Discovery of regulatory modules 184
4 Motif discovery in multiple species 189
5 Motif learning on ChIP-chip data 195
6 Using nucleosome positioning information in motif discovery 201
7 Conclusion 204
References 205
Chapter 9 Analysis of Cancer Genome Alterations Using Single Nucleotide Polymorphism(SNP)Microarrays Cheng Li,Samir Amin 209
1 Background 209
2 Loss of heterozygosity analysis using SNP arrays 212
3 Copy number analysis using SNP arrays 216
4 High-level analysis using LOH and copy number data 224
5 Software for cancer alteration analysis using SNP arrays 229
6 Prospects 231
Acknowledgements 231
References 231
Chapter 10 Analysis of ChIP-chip Data on Genome Tiling Microarrays W.Evan Johnson,Jun S.Liu,X.Shirley Liu 239
1 Background molecular biology 239
2 A ChIP-chip experiment 241
3 Data description and analysis 244
4 Follow-up analysis 249
5 Conclusion 254
References 254
Subject Index 259
Author Index 261