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Preface Table of contents SECTION I. CLINICAL TRIALS 1. Phase I Clinical Trials in Oncology Anastasia Ivanova and Nancy Flournoy 1.1 Introduction 1 1.2 Phase I Trials in Healthy Volunteers 1 1.3 Phase I Trials With Toxic Outcomes Enrolling Patients 3 1.3.1 Parametric versus non-parametric designs 4 1.3.2 Markovian-motivated up-and-down designs 4 1.3.3 Isotonic designs 7 1.3.4 Bayesian designs 8 1.3.5 Time-to-event design modifications 9 1.4 Other Design Problems in Dose Finding 10 1.5 Concluding Remarks 11 References 11 2. Phase II Clinical Trials Nigel Stallard 2.1 Introduction 14 2.1.1 Background 14 2.1.2 The role of phase II clinical trials in the clinical evaluation of a novel therapeutic agent 15 2.1.3 Phase II clinical trial designs 16 2.2 Frequentist methods in phase II clinical trials 17 2.2.1 Review of frequentist methods and their application in phase II trials 17 2.2.2 Frequentist methods for single-treatment pilot studies 19 2.2.3 Frequentist methods for comparative studies 21 2.2.4 Frequentist methods for screening studies 22 2.3 Bayesian methods in phase II clinical trials 23 2.3.1 Review of Bayesian methods and their application in phase II trials 23 2.3.2 Bayesian methods for single-treatment pilot studies, comparative studies and selection screens 25 2.4 Decision theoretic methods in phase II clinical trials 26 2.4 Analysis of multiple endpoints in phase II clinical trials 27 2.5 Clinical trials combining phases II and III 2.6 Outstanding issues in phase II clinical trials 29 References 30 3. Response Adaptive Designs in Phase III Clinical Trials Atanu Biswas, Uttam Bandyopadhyay and Rahul Bhattacharya 1.3 Introduction 35 3.2 Adaptive Designs for Binary Treatment Responses 36 3.2.1 Play-the-winner design 36 3.2.2 Randomized play-the-winner design 36 3.2.3 Generalized Polya?s urn (GPU) 37 3.2.4 Randomized Polya urn design 39 3.2.5 Birth and death urn design 39 3.2.6 Birth and death urn with immigration design 40 3.2.7 Drop-the-loser urn design 40 3.2.8 Sequential estimation adjusted urn design 41 3.2.9 Doubly adaptive biased coin design 42 3.3 Adaptive Designs for Binary Treatment Responses Incorporating Covariates 43 3.3.1 Covariate-adaptive randomized play-the-winner design 43 3.3.2 Treatment effect mappings 44 3.3.3 Drop-the-loser design with covariate 44 3.4 Adaptive Designs for Categorical Responses 45 3.5 Adaptive Designs for Continuous Responses 45 3.5.1 Nonparametric score based allocation designs 45 3.5.2 Link function based allocation designs 46 3.5.3 Continuous drop-the-loser design 47 3.6 Optimal Adaptive Designs 47 3.7 Delayed Responses in Adaptive Designs 48 3.8 Biased Coin Designs 49 3.9 Real Adaptive Clinical Trials 49 3.10 Data Study for Different Adaptive Scheme 50 3.10.1 Fluoxetine trial 50 3.10.2 Pregabalin trial 51 3.10.3 Simulated trial 52 3.11 Concluding Remarks 53 References 54 4. Inverse Sampling for Clinical Trials: A Brief Review of Theory and Practice Atanu Biswas and Uttam Bandyopadhyay 1.4 Introduction 59 4.1.1 Inverse binomial sampling 60 4.1.2 Partial sequential sampling 62 4.2 Two-Sample Randomized Inverse Sampling for Clinical Trials 62 4.2.1 Use of Mann-Whitney statistics 63 4.2.2 Fixed-width confidence interval estimation 64 4.2.3 Fixed-width confidence interval for partial sequential sampling 65 4.3 An Example of Inverse Sampling: Boston ECMO 66 4.4 Inverse Sampling in Adaptive Designs 66 4.5 Concluding Remarks 68 References 68 5. The Design and Analysis Aspects of Cluster Randomized Trials Hrishikesh Chakraborty 1.5 Introduction: Cluster Randomized Trials 71 1.6 Intra-Cluster Correlation Coefficient and Confidence Interval 74 1.7 Sample Size Calculation for Cluster Randomized Trials 76 1.8 Analysis of Cluster Randomized Trial Data 78 1.9 Concluding Remarks 80 References 81 SECTION II. EPIDEMIOLOGY 6. HIV Dynamics Modeling and Prediction of Clinical Outcomes in AIDS Clinical Research Yangxin Huang and Hulin Wu 6.1 Introduction 84 6.2 HIV Dynamic Model and Treatment Effects Models 85 6.2.1 HIV dynamic model 86 6.2.2 Treatment effect models 86 6.3 Statistical Methods for Predictions of Clinical Outcomes 88 6.3.1 Bayesian nonlinear mixed-effects model 88 6.3.2 Predictions using the Bayesian mixed-effects modelling approach 90 6.4 Simulation Study 91 6.6 Clinical Data Analysis 92 6.7 Concluding Remarks 93 References 97 7. Spatial Epidemiology Lance A. Waller 7.1 Space and Disease 103 7.2 Basic Spatial Questions and Related Data 104 7.3 Quantifying Pattern in Point Data 105 7.4 Predicting Spatial Observations 116 7.5 Concluding Remarks 129 References 130 8. Modeling Disease Dynamics: Cholera as a Case Study Edward L. Ionides, Carles Breto and Aaron A. King 8.1 Introduction 135 8.2 Data Analysis via Population Models 137 8.3 Sequential Monte Carlo 139 8.4 Modeling Cholera 146 8.4.1 Fitting structural models to cholera data 149 8.5 Concluding Remarks 154 References 156 9. Misclassification and Measurement Error Models in Epidemiological Studies Surupa Roy and Tathagata Banerjee 9.1 Introduction 162 9.2 A Few Examples 164 9.2.1 Atom bomb survivors? data (Sposto et al., 1992) 164 9.2.2 Coal miners data (Ashford and Sowden, 1970) 164 9.2.3 Dietary habits of mothers on low birth weights of babies (Jeffrey B. Gould et al., 1984) 165 9.3 Binary Regression Models with Two Types of Errors 165 9.4 Bivariate Binary Regression Models with Two Types of Errors 167 9.5 Models for Analyzing Mixed Misclassified Binary and Continuous Responses 170 9.6 Atom Bomb Data Analysis 173 9.7 Concluding Remarks 174 References 174 SECTION III. SURVIVAL ANALYSIS 10. Semiparametric Maximum Likelihood Inference in Survival Analysis Michael R. Kosorok 10.1 Introduction 183 10.2 Examples of Survival Models 184 10.3 Basic Estimation and Limit Theory 186 10.4 The Bootstrap 188 10.4.1 The regular case 10.4.2 When slowly converging nuisance parameters are present 190 10.5 The Profile Sampler 191 10.6 The Piggyback Bootstrap 194 10.7 Other Approaches 196 10.8 Concluding Remarks 197 References 198 11. An Overview of the Semi-Competing Risks Problem Limin Peng, Hongyu Jiang, Richard J. Chappell and Jason P. Fine 11.1 Introduction 205 11.2 Nonparametric Inferences 208 11.3 Semiparmetric One-Sample Inference 210 11.4 Semiparametric Regression Method 214 11.4.1 Functional regression modeling 215 11.4.2 A bivariate accelerated lifetime model 217 11.5 Concluding Remarks 220 References 220 12. Tests for Time-Varying Covariate Effects within Aalen?s Additive Hazards Model Thomas H. Scheike and Torben Martinussen 12.1 Introduction 225 12.2 Model Specification and Inferential Procedures 226 12.2.1 A pseudo-score test 230 12.3 Numerical Results 231 12.3.1 Simulation studies 231 12.3.2 Trace data 235 12.4 Concluding Remarks 237 12.5 Summary 237 References 238 13. Analysis of Outcomes Subject to Induced Dependent Censoring: A Marked Point Process Perspective Eugene Huang 13.1 Introduction 241 13.2 Induced Dependent Censoring and Associated Identifiability Issues 242 13.3 Marked Point Process 244 13.3.1 Hazard functions with marked point process 245 13.3.2 Identifiability 245 13.3.3 Nonparametric estimation 246 13.3.4 Martingales 246 13.4 Modeling Strategy for Testing and Regression 248 13.4.1 Two-sample test for lifetime utility or cost 248 13.4.2 Calibration regression for lifetime medical cost 249 13.4.3 Two-sample multi-state accelerated sojourn times model 250 13.5 Concluding Remarks 251 References 253 14. Analysis of Dependence in Multivariate Failure-Time Data Zoe Moodie and Li Hsu 14.1 Introduction 255 14.2 Nonparametric Bivariate Survivor Function Estimation 257 14.2.1 Path-dependent estimators 257 14.2.2 Inverse censoring probability weighted estimators 258 14.2.3 NPMLE-type estimators 259 14.2.4 Data application to Danish Twin Data 261 14.3 Non- and Semi-Parametric Estimation of Dependence Measures 262 14.3.1 Nonparametric dependence estimation 262 14.3.2 Semiparametric dependence estimation 264 14.3.3 An application to a case-control family study of breast cancer 269 14.4 Concluding Remarks 272 References 274 15. Robust Estimation for Analyzing Recurrent Events Data in the Presence of Terminal Events Rajeshwari Sundaram 15.1 Introduction 279 15.2 Inference Procedures 281 15.2.1 Estimation in presence of only independent censoring (censoring variables are all observable) 281 15.2.2 Estimation in presence of terminal events 283 15.3 Large Sample Properties 283 15.4 Numerical Results 287 15.4.1 Simulation studies 287 15.4.2 rhDNase data 291 15.4.3 Bladder tumor data 293 15.5 Concluding Remarks 295 References 296 16. Tree-Based Methods for Survival Data Mousumi Banerjee and Anne-Michelle Noone 16.1 Introduction 302 16.2 Review of CART 303 16.3 Trees for Survival Data 306 16.3.1 Methods based on measure of within-node homogeneity 306 16.3.2 Methods based on between-node separation 309 16.3.3 Pruning and tree selection 309 16.4 Simulations to Compare Different Splitting Methods 310 16.5 Example: Breast Cancer Prognostic Study 313 16.6 Random forest for Survival Data 320 16.6.1 Breast cancer study: Results from random forest analysis 322 16.7 Concluding Remarks 323 References 326 17. Bayesian Estimation of the Hazard Function with Randomly Right-Censored Data Jean-Francois Angers and Brenda MacGibbon 17.1 Introduction 330 17.1.1 The random right censorship model 332 17.1.2 The Bayesian model 333 17.2 Bayesian Functional Model Using Monotone Wavelet Approximation 335 17.3 Estimation of the Sub-Density F* 338 17.4 Simulations 340 17.5 Example 342 17.6 Concluding Remarks 345 References 347 SECTION IV. GENOMICS AND PROTEOMICS 18. The Effects of Inter-Gene Associations on Statistical Inferences From Microarray Data Kerby Shedden 18.1 Introduction 352 18.2 Inter-Gene Correlation 353 18.3 Differential Expression 356 18.4 Time Course Experiments 359 18.5 Meta-Analysis 362 18.6 Concluding Remarks 364 References 365 19. A Comparison of Methods for Meta-Analysis of Gene Expression Data Hyungwon Choi and Debashis Ghosh 19.1 Introduction 367 19.2 Background 368 19.2.1 Technology details and gene identification 368 19.2.2 Analysis methods 369 19.3 Example 371 19.4 Cross Comparison of Gene Signatures 371 19.5 Best Common Mean Difference Method 372 19.6 Effect Size Method 373 19.7 Probability of Expression (POE) Assimilation Method 374 19.8 Comparison of Three Methods 375 19.8.1 Signatures 375 19.8.2 Classification Performance 376 19.8.3 Directionality of Differential Expression 376 19.9 Conclusions 377 References 377 20. Statistical Methods for Identifying Differentially Expressed Genes in Replicated Microarray Experiments: A Review Lynn Kuo, Fang Yu and Yifang Zhao 20.1 Introduction 384 20.2 Normalization 387 20.3 Methods for Selecting Differentially Expressed Genes 389 20.3.1 BH-T 389 20.3.2 SAM 390 20.3.3 SPH 391 20.3.4 LIMMA 394 20.3.5 MAANOVA 395 20.4 Simulation Study 397 20.4.1 Results of simulation studies 399 20.4.2 Other considerations 400 20.5 Concluding Remarks 400 References 401 21. Clustering of Microarray Data via Mixture Models Geoffrey McLachlan, Richard W. Bean and Angus Ng 21.1 Introduction 414 21.2 Clustering of Microarray Data 416 21.3 Notation 417 21.4 Clustering of Tissue Samples 418 21.5 The EMMIX-GENE Clustering Procedure 419 21.5.1 Step 1: Screening of genes 420 21.5.2 Step 2: Clustering of genes: Formation of metagenes 421 21.5.3 Step 3: Clustering of Tissues 422 21.6 Clustering of gene profile 423 21.7 EMMIX-WIRE 424 21.8 ML Estimation via the EM Algorithm 425 21.9 Model Selection 427 21.10 Example: Clustering of Time-Course Data 427 21.11 Concluding Remarks 430 References 431 22. Censored Data Regression in High-Dimension and Low-Sample-Size Settings for Genomic Applications Hongzhe Li 22.1 Introduction 435 22.2 Censored Data Regression Models 436 22.2.1 The Cox proportional hazards model 437 22.2.2 Accelerated failure time model 437 22.2.3 Additive hazard regression models 438 22.3 Regularized Estimation for Censored Data Regression Models 439 22.3.1penalized estimation of the Cox model using kernels 439 22.3.2penalized estimation of the Cox model using least-angle regression 440 22.3.3 Threshold gradient descent procedure for the Cox model 441 22.3.4 Regularized Buckley-James estimation for the AFT model 442 22.3.5 Regularization based on inverse probability of censoring weighted loss function for the AFT model 443 22.3.6 Penalized estimation for the additive hazard model 444 22.3.7 Use of other penalty functions 444 22.4 Survival Ensemble Methods 445 22.4.1 The smoothing spline-based boosting algorithm for the non- parametric additive Cox model 445 22.4.2 Random forests and gradient boosting procedure for the AFT model 446 22.5 Nonparametric Pathway-Based Regression Models 446 22.6 Dimension-Reduction-Based Methods and Bayesian Variable Selection Methods 448 22.7 Criteria for Evaluating Different Procedures 448 22.8 Application to a Real Data Set and Comparisons 449 22.9 Discussion and Future Research Topics 450 22.9.1 Test of treatment effect adjusting for high-dimensional genomic data 451 22.9.2 Development of flexible models for gene-gene and gene-environment interactions 451 22.9.3 Methods for other types of genomic data 451 22.9.4 Development of pathway- and network-based regression models for censored survival phenotypes 452 22.10 Concluding Remarks 452 References 453 23. Analysis of Case-Control Studies in Genetic Epidemiology Nilanjan Chatterjee 23.1 Introduction 457 23.2 Maximum Likelihood Analysis of Case-Control Data with Complete Information 459 23.2.1 Background 459 23.2.2 Maximum likelihood estimation under HWE and gene-environment independence 460 23.2.3 An example 462 23.3 Haplotype-Based Genetic Analysis with Missing Phase Information 463 23.3.1 Background 463 23.3.2 Methods 465 23.3.3 Application 467 23.4 Concluding Remarks 467 References 469 24. Assessing Network Structure in the Presence of Measurement Error Denise Scholtens, Raji Balasubramanian and Robert Gentleman 24.1 Introduction 472 24.2 Graphs of Biological Data 473 24.2.1 Integrating multiple data types 474 24.3 Statistics on Graphs 475 24.4 Graph Theoretic Models 476 24.5 Types of Measurement Error 478 24.5.1 Stochastic error 479 24.5.2 Systematic error 479 24.5.3 Sampling 480 24.6 Exploratory Data Analysis 480 24.7 Influence of Measurement Error on Graph Statistics 483 24.7.1 Path length: L 484 24.7.2 Clustering coefficient: C 485 24.7.3 Node degree distribution 488 24.8 Biological Implications 492 24.8.1 Experimental data 492 24.8.2 Simulation data 494 24.9 Conclusions 495 References 496 25. Prediction of RNA Splicing Signals Mark Segal 25.1 Introduction 499 25.1.1 Biologic overview of splicing 500 25.2 Existing Approaches to Splice Site Identification 501 25.2.1 Maximum entropy models 502 25.2.2 Permuted variable length Markov models 503 25.2.3 Bayesian network approaches 505 25.3 Splice Site Recognition Contemporary Classifiers 507 25.3.1 Random forests 508 25.3.2 Support vector machines 511 25.3.3 Boosting 512 25.4 Results 25.4.1 Data generation 25.4.2 Predictive performance 25.4.3 Interpretative yield 25.4.4 Computational considerations 25.5 Concluding Remarks References 26. Statistical Methods for Biomarker Discovery Using Mass Spectrometry Bradley M. Broom and Kim-Anh Do 26.1 Introduction 26.1.1 Sample ionization 26.1.2 Mass analysis 26.2 Biomarker Discovery 26.3 Statistical Methods for Pre-Processing 26.4 Statistical Methods for Multiple Testing, Classification and Applications 26.4.1 Multiple testing and identifying differentially expressed peaks 26.4.2 A peak probability contrast (PPC) procedure for sample classification 26.4.3 A semi-parametric model for protein mass spectroscopy 26.4.4 Smoother principal component analysis (PCA) for proteomic spectra 26.4.5 Wavelet-based functional mixed model and application 26.4.6 A nonparametric Bayesian model based on kernel functions 26.5 Potential Statistical Developments 26.6 Concluding Remarks References 27. Genetic Mapping of Quantitative Traits: Model-Free Sib-Pair Linkage Approaches Saurabh Ghosh and Parthe P. Majumder 27.1 Introduction 27.2 The Basic QTL Framework for Sib-Pairs 27.3 The Haseman-Elston Regression Framework 27.4 Nonparametric Alternatives 27.5 The Modified Nonparametric Regression 27.5.1 Evaluation of significance levels 27.6 Comparison with Linear Regression Methods 27.7 Significance Levels and Empirical Power 27.8 An Application to Real Data 27.9 Concluding Remarks References SECTION V. MISCELLANEOUS TOPICS 28. Robustness Issues in Biomedical Studies Ayanendranath Basu 28.1 Introduction: The Need for Robust Procedures 28.2 Standard Tools for Robustness 28.2.1 M-estimators 28.2.2 Influence function 28.2.3 Breakdown point 28.2.4 Basic miscellaneous procedures 28.2.5 Alternative approaches 28.3 The Robustness Question in Biomedical Studies 28.4 Robust Estimation in the Logistic Regression Model 28.5 Robust Estimation for Censored Survival Data 28.6 Adaptive Robust Methods in Clinical Trials 28.7 Concluding Remarks References 29. Recent Advances in the Analysis of Episodic Hormone Data Timothy D. Johnson and Yuedong Wang 29.1 Introduction 29.2 A General Biophysical Model 29.3 Bayesian Deconvolution Model (BDM) 29.3.1 Posterior processing 29.3.2 An example 29.4 Nonlinear Mixed Effects Partial Splines Models 29.5 Concluding Remarks References 30. Models for Carcinogenesis Anup Dewanji 30.1 Introduction 30.2 Statistical Models 30.3 Multistage Models 30.4 Two-Stage Clonal Expansion Model 30.5 Physiologically Based Pharmacokinetic Models 30.6 Statistical Methods 30.7 Concluding Remarks References Author Index Subject Index
Library of Congress Subject Headings for this publication:
Medicine -- Research -- Statistical methods.
Biology -- Research -- Statistical methods.
Clinical trials -- Statistical methods.
Epidemiology -- Statistical methods.
Survival analysis (Biometry).
Bioinformatics.
Models, Statistical.
Biomedical Research.
Clinical Trials.
Computational Biology -- methods.
Epidemiologic Methods.
Survival Analysis.