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New developments in biostatistics and bioinformatics / editors: Jianqing Fan, Xihong Lin, Jun S. Liu. — Beijing : Higher Education Press ; New Jersey : World Scientific, c2009. – (58.1057/N542) |
Contents
Contents
Part I Analysis of Survival and Longitudinal Data
Chapter 1 Non- and Semi- Parametric Modeling in Survival Analysis
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
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
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
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
Chapter 5 Study Designs for Biomarker-Based Treatment Selection
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
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
Chapter 7 Protein Interaction Predictions from Diverse Sources
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
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
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
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
