Statistical Advances in the Biomedical Sciences

Atanu Biswas, PhD, is Assistant Professor in the Applied Statistics Unit at the Indian Statistical Institute, Kolkata in India. Dr. Biswas has authored more than eighty published articles and also serves as Associate Editor of several journals, including Sequential Analysis and Communications in Statistics. He is the recipient of the M.N. Murthy Award for his research in applied statistics. Sujay Datta, PhD, is Associate Professor in the Department of Mathematics and Computer Science at Northern Michigan University and Visiting Research Scientist in the Department of Statistics at TexasA&M University, where he is part of a bioinformatics research program sponsored by the National Institutes of Health. Dr. Datta's research interests include high-throughput data, genomics, and models based on graphs/networks. Jason P. Fine, PhD, is Associate Professor in the Department of Statistics at the University of Wisconsin-Madison and also serves as Associate Editor of several journals, including Biometrics, Biostatistics, and the Scandinavian Journal of Statistics. Mark R. Segal, PhD, is Professor in the Department of Epidemiology and Biostatistics at the University of California, San Francisco. A Fellow of the American Statistical Association, Dr. Segal has published extensively and currently focuses his research in the area of bioinformatics.

"Statistical Advances in the Biomedical Sciences provides vital statistical guidance to practioners in the biomedical sciences while also introducing statisticians to new, multidisciplinary frontiers of application. This text is an excellent reference for graduate - and Ph.D.-level courses in various areas of biostatistics and the medical sciences and also serves as a valuable tool for medical researchers, statisticians, public health professionals, and biostatisticians." (Mathematical Reviews, Issue 2009f) "Statistical Advances in the Biomedical Sciences provides vital statistical guidance to practioners in the biomedical sciences while also introducing statisticians to new, multidisciplinary frontiers of application.  This text is an excellent reference for graduate - and Ph.D.-level courses in various areas of biostatistics and the medical sciences and also serves as a valuable tool for medical researchers, statisticians, public health professionals, and biostatisticians." (Mathematical Reviews, Issue 2009f)"The authors have done an excellent job of meeting the objective they put forward in the preface.  They have produced an authoritative volume of readable chapters … The chapters are written well and will be understandable to graduate students in biostatistics and statistics.  The book will have an important place as a reference book on the shelf of many professional biostatisticians working in a biomedical research environment.  Additionally, it should be useful as a special topics text for graduate students in biostatistics and statistics graduate programs." (Biometrics, Dec 2008)

• Preface xxiAcknowledgments xxvContributors xxviiPart I Clinical Trials 11. Phase I Clinical Trials 3Anastasis Ivanova and Nancy Flournoy1.1 Introduction, 31.2 Phase I Trials in Healthy Volunteers, 31.3 Phase I Trials with Toxic Outcomes Enrolling Patients, 51.4 Other Design Problems in Dose Finding, 111.5 Concluding Remarks, 122. Phase II Clinical Trials 15Nigel Stallard2.1 Introduction, 152.2 Frequentist Methods in Phase II Clinical Trials, 182.3 Bayesian Methods in Phase II Clinical Trials, 222.4 Decision-Theoretic Methods in Phase II Clinical Trials, 252.5 Analysis of Multiple Endpoints in Phase II Clinical Trials, 262.6 Outstanding Issues in Phase II Clinical Trials, 273. Response-Adaptive Designs in Phase III Clinical Trials 33Atanu Biswas, Uttam Bandyopadhyay, and Rahul Bhattacharya3.1 Introduction, 333.2 Adaptive Designs for Binary Treatment Responses, 343.3 Adaptive Designs for Binary Treatment Responses Incorporating Covariates, 403.4 Adaptive Designs for Categorical Responses, 413.5 Adaptive Designs for Continuous Responses, 423.6 Optimal Adaptive Designs, 433.7 Delayed Responses in Adaptive Designs, 443.8 Biased Coin Designs, 453.9 Real Adaptive Clinical Trials, 453.10 Data Study for Different Adaptive Schemes, 463.11 Concluding Remarks, 494. Inverse Sampling for Clinical Trials: A Brief Review of Theory and Practice 55Atanu Biswas and Uttam Bandyopadhyay4.1 Introduction, 554.2 Two-Sample Randomized Inverse Sampling for Clinical Trials, 594.3 An Example of Inverse Sampling: Boston ECMO, 624.4 Inverse Sampling in Adaptive Designs, 624.5 Concluding Remarks, 635. The Design and Analysis Aspects of Cluster Randomized Trials 67Hrishikesh Chakraborty5.1 Introduction: Cluster Randomized Trials, 675.2 Intracluster Correlation Coefficient and Confidence Interval, 695.3 Sample Size Calculation for Cluster Randomized Trials, 715.4 Analysis of Cluster Randomized Trial Data, 735.5 Concluding Remarks, 75Part II Epidemiology 816. HIV Dynamics Modeling and Prediction of Clinical Outcomes in AIDS Clinical Research 83Yangxin Huang and Hulin Wu6.1 Introduction, 836.2 HIV Dynamic Model and Treatment Effect Models, 846.3 Statistical Methods for Predictions of Clinical Outcomes, 876.4 Simulation Study, 906.5 Clinical Data Analysis, 916.6 Concluding remarks, 927. Spatial Epidemiology 97Lance A. Waller7.1 Space and Disease, 977.2 Basic Spatial Questions and Related Data, 987.3 Quantifying Pattern in Point Data, 997.4 Predicting Spatial Observations, 1077.5 Concluding Remarks, 1188. Modeling Disease Dynamics: Cholera as a Case Study 123Edward L. Ionides, Carles Bretó, and Aaron A. King8.1 Introduction, 1238.2 Data Analysis via Population Models, 1248.3 Sequential Monte Carlo, 1268.4 Modeling Cholera, 1308.5 Concluding Remarks, 1369. Misclassification and Measurement Error Models in Epidemiologic Studies 141Surupa Roy and Tathagata Banerjee9.1 Introduction, 1419.2 A Few Examples, 1439.3 Binary Regression Models with Two Types of Error, 1449.4 Bivariate Binary Regression Models with Two Types of Error, 1469.5 Models for Analyzing Mixed Misclassified Binary and Continuous Responses, 1499.6 Atom Bomb Data Analysis, 1519.7 Concluding Remarks, 152Part III Survival Analysis 15710. Semiparametric Maximum-Likelihood Inference in Survival Analysis 159Michael R. Kosorok10.1 Introduction, 15910.2 Examples of Survival Models, 16010.3 Basic Estimation and Limit Theory, 16210.4 The Bootstrap, 16310.5 The Profile Sampler, 16610.6 The Piggyback Bootstrap, 16810.7 Other Approaches, 17010.8 Concluding Remarks, 17111. An Overview of the Semi–Competing Risks Problem 177Limin Peng, Hongyu Jiang, Rick J. Chappell, and Jason P. Fine11.1 Introduction, 17711.2 Nonparametric Inferences, 17911.3 Semiparametric One-Sample Inference, 18111.4 Semiparametric Regression Method, 18411.5 Concluding Remarks, 18912. Tests for Time-Varying Covariate Effects within Aalen’s Additive Hazards Model 193Torben Martinussen and Thomas H. Scheike12.1 Introduction, 19312.2 Model Specification and Inferential Procedures, 19412.3 Numerical Results, 19912.4 Concluding Remarks, 20412.5 Summary, 204Appendix 12A, 20513. Analysis of Outcomes Subject to Induced Dependent Censoring: A Marked Point Process Perspective 209Yijian Huang13.1 Introduction, 20913.2 Induced Dependent Censoring and Associated Identifiability Issues, 21013.3 Marked Point Process, 21213.4 Modeling Strategy for Testing and Regression, 21513.5 Concluding Remarks, 21814. Analysis of Dependence in Multivariate Failure-Time Data 221li Hsu and Zoe Moodie14.1 Introduction, 22114.2 Nonparametric Bivariate Survivor Function Estimation, 22314.3 Non- and Semiparametric Estimation of Dependence Measures, 23014.4 Concluding Remarks, 23915. Robust Estimation for Analyzing Recurrent-Event Data in the Presence of Terminal Events 245Rajeshwari Sundaram15.1 Introduction, 24515.2 Inference Procedures, 24715.3 Large-Sample Properties, 24915.4 Numerical Results, 25215.5 Concluding Remarks, 259Appendix 15A, 26016. Tree-Based Methods for Survival Data 265Mousumi Banerjee and Anne-Michelle Noone16.1 Introduction, 26516.2 Review of CART, 26616.3 Trees for Survival Data, 26816.4 Simulations for Comparison of Different Splitting Methods, 27216.5 Example: Breast Cancer Prognostic Study, 27416.6 Random Forest for Survival Data, 27816.7 Concluding Remarks, 28117. Bayesian Estimation of the Hazard Function with Randomly Right-Censored Data 287Jean-François Angers and Brenda MacGibbon17.1 Introduction, 28717.2 Bayesian Functional Model Using Monotone Wavelet Approximation, 29217.3 Estimation of the Subdensity F*, 29517.4 Simulations, 29617.5 Examples, 29817.6 Concluding Remarks, 300Appendix 17A, 301Part IV Bioinformatics 30718. The Effects of Intergene Associations on Statistical Inferences from Microarray Data 309Kerby Shedden18.1 Introduction, 30918.2 Intergene Correlation, 31018.3 Differential Expression, 31418.4 Timecourse Experiments, 31518.5 Meta-Analysis, 31918.6 Concluding Remarks, 32119. A Comparison of Methods for Meta-Analysis of Gene Expression Data 325Hyungwon Choi and Debashis Ghosh19.1 Introduction, 32519.2 Background, 32619.3 Example, 32819.4 Cross-Comparison of Gene Signatures, 32919.5 Best Common Mean Difference Method, 32919.6 Effect Size Method, 33119.7 POE Assimilation Method, 33219.8 Comparison of Three Methods, 33419.9 Conclusions, 33620. Statistical Methods for Identifying Differentially Expressed Genes in Replicated Microarray Experiments: A Review 341Lynn kuo, Fang Yu, and Yifang Zhao20.1 Introduction, 34120.2 Normalization, 34420.3 Methods for Selecting Differentially Expressed Genes, 34920.4 Simulation Study, 35720.5 Concluding Remarks, 36021. Clustering of Microarray Data via Mixture Models 365Geoffrey J. McLachlan, Richard W. Bean, and Angus Ng21.1 Introduction, 36521.2 Clustering of Microarray Data, 36721.3 Notation, 36721.4 Clustering of Tissue Samples, 36921.5 The EMMIX-GENE Clustering Procedure, 37021.6 Clustering of Gene Profiles, 37221.7 Emmix-wire, 37321.8 Maximum-Likelihood Estimation via the EM Algorithm, 37421.9 Model Selection, 37621.10 Example: Clustering of Timecourse Data, 37721.11 Concluding Remarks, 37922. Censored Data Regression in High-Dimensional and Low-Sample-Size Settings for Genomic Applications 385Hongzhe li22.1 Introduction, 38522.2 Censored Data Regression Models, 38622.3 Regularized Estimation for Censored Data Regression Models, 38822.4 Survival Ensemble Methods, 39422.5 Nonparametric-Pathway-Based Regression Models, 39522.6 Dimension-Reduction-Based Methods and Bayesian Variable Selection Methods, 39622.7 Criteria for Evaluating Different Procedures, 39722.8 Application to a Real Dataset and Comparisons, 39722.9 Discussion and Future Research Topics, 39822.10 Concluding Remarks, 40023. Analysis of Case–Control Studies in Genetic Epidemiology 405Nilanjan Chatterjee23.1 Introduction, 40523.2 Maximum-Likelihood Analysis of Case–Control Data with Complete Information, 40623.3 Haplotype-based Genetic Analysis with Missing Phase Information, 41023.4 Concluding Remarks, 41524. Assessing Network Structure in the Presence of Measurement Error 419Denise Scholtens, Raji Balasubramanian, and Robert Gentleman24.1 Introduction, 41924.2 Graphs of Biological Data, 42024.3 Statistics on Graphs, 42124.4 Graph-Theoretic Models, 42224.5 Types of Measurement Error, 42524.6 Exploratory Data Analysis, 42624.7 Influence of Measurement Error on Graph Statistics, 42924.8 Biological Implications, 43624.9 Conclusions, 43925. Prediction of RNA Splicing Signals 443Mark R. Segal25.1 Introduction, 44325.2 Existing Approaches to Splice Site Identification, 44525.3 Splice Site Recognition via Contemporary Classifiers, 45025.4 Results, 45525.5 Concluding Remarks, 45926. Statistical Methods for Biomarker Discovery Using Mass Spectrometry 465Bradley M. Broom and Kim-Anh Do26.1. Introduction, 46526.2 Biomarker Discovery, 47026.3 Statistical Methods for Preprocessing, 47026.4 Statistical Methods for Multiple Testing, Classification, and Applications, 47326.5 Potential Statistical Developments, 48126.6 Concluding Remarks, 48327. Genetic Mapping of Quantitative Traits: Model-Free Sib-Pair Linkage Approaches 487Saurabh Ghosh and Partha P. Majumder27.1 Introduction, 48727.2 The Basic QTL Framework For Sib-Pairs, 48827.3 The Haseman–Elston Regression Framework, 48927.4 Nonparametric Alternatives, 48927.5 The Modified Nonparametric Regression, 49027.6 Comparison With Linear Regression Methods, 49227.7 Significance Levels and Empirical Power, 49327.8 An Application to Real Data, 49527.9 Concluding Remarks, 496Part V Miscellaneous Topics 49928. Robustness Issues in Biomedical Studies 501Ayanendranath Basu28.1 Introduction: The Need for Robust Procedures, 50128.2 Standard Tools for Robustness, 50228.3 The Robustness Question in Biomedical Studies, 50628.4 Robust Estimation in the Logistic Regression Model, 50828.5 Robust Estimation for Censored Survival Data, 51328.6 Adaptive Robust Methods in Clinical Trials, 51828.7 Concluding Remarks, 52129. Recent Advances in the Analysis of Episodic Hormone Data 527Timothy D. Johnson and Yuedong Wang29.1 Introduction, 52729.2 A General Biophysical Model, 53029.3 Bayesian deconvolution model (BDM), 53129.4 Nonlinear Mixed-Effects Partial-Splines Models, 53729.5 Concluding Remarks, 54230. Models for Carcinogenesis 547Anup Dewanji30.1 Introduction, 54730.2 Statistical Models, 54930.3 Multistage Models, 55230.4 Two-Stage Clonal Expansion Model, 55530.5 Physiologically Based Pharmacokinetic Models, 56030.6 Statistical Methods, 56230.7 Concluding Remarks, 564Index 569