【正版稀缺】生物统计学和生物信息学最新进展（英文版）
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作者范剑青
出版社高等教育出版社
ISBN9787040247558
出版时间2009-01
版次1
印刷时间2009-01
印数1千册
装帧平装
开本16开
定价49元
货号17-2-19
上书时间2017-09-07
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【内容简介】:
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.
【目录】:
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
【文摘】:
We assume that patients can be divided into twogroups based on an assay of a biomarker. This biomarker could be a compositeof hundreds of molecular and genetic factors, for example, but in this case wesuppose that a cutoff value has been determined that dichotomizes these values.In our example the biomarker is the expression of guanylyl cyclase C （GCC） in thelymph nodes of patients. We assume that we have an estimate of the sensitivityand specificity of the biomarker assay. The variable of patient response is takento be continuous-valued; it could represent a measure of toxicity to the patient,quality of life, uncensored survival time, or a composite of several measures. Inour example we take the endpoint to be three-year disease recurrence.
We consider five study designs, each addressing its own set of scientific ques-tions, to study how patients in each marker group fare with each treatment. Al-though consideration of which scientific questions are to be addressed by the studyshould supersede consideration of necessary sample size, we give efficiency com-parisons here for those cases in which more than one design would be appropriate.One potential goal is to investigate how treatment assignment and patient markerstatus affect outcome, both separately and interactively. The marker under con-sideration is supposedly predictive: it modifies the treatment effect. We may wantto verify its predictive value and to assess its prognostic value, that is, how wellit divides patients receiving the same treatment into different risk groups. Eachstudy design addresses different aspects of these overarching goals.
This paper is organized as follows:
1. Definition of study designs
2. Test of hypotheses
3. Sample size calculation
4. Numerical comparison of efficiency
5. Conclusions
2 Definition of study designs
The individual study designs are as follows.
2.1 Traditional design
To assess the safety and efficacy of the novel treatment, the standard design （Fig. 1）is to register patients, then randomize thorn with ratio ~~ to receive treatment Aor B. We compare the response variable across the two arms of the trial withoutregard for the marker status of the patients.
In our example, we would utilize this design if we wanted only to compare therecurrence rates of colorectal cancer in the two treatment groups independent ofeach patient's biomarker status.
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