Biswas A., Datta S., Fine J.P., Segal M.R. (eds.) Statistical Advances in the Biomedical Sciences: Clinical Trials, Epidemiology, Survival Analysis, and Bioinformatics

N.-Y.: Wiley, 2008. — 583 p.Clinical Trials
Phase I Trials in Healthy Volunteers
Phase I Trials with Toxic Outcomes Enrolling Patients
Markovian-Motivated Up-and-Down Designs
Bayesian Designs
Time-to-Event Design Modifications
Other Design Problems in Dose Finding
Concluding RemarksThe Role of Phase II Clinical Trials in Clinical Evaluation of a Novel Therapeutic Agent
Phase II Clinical Trial Designs
Review of Frequentist Methods and Their Applications in Phase II Clinical Trials
Frequentist Methods for Single-Treatment Pilot Studies
Frequentist Methods for Comparative Studies
Review of Bayesian Methods and Their Application in Phase II Clinical Trials
Bayesian Methods for Single-Treatment Pilot Studies, Comparative Studies and Selection Screens
Decision-Theoretic Methods in Phase II Clinical Trials
Analysis of Multiple Endpoints in Phase II Clinical Trials
Outstanding Issues in Phase II Clinical TrialsRandomized Play-the-Winner Design
Generalized Pólya’s Urn (GPU)
Randomized Pólya Urn Design
Drop-the-Loser Urn Design
Sequential Estimation-Adjusted Urn Design
Doubly Adaptive Biased Coin Design
Covariate-Adaptive Randomized Play-the-Winner Design
Adaptive Designs for Categorical Responses
Nonparametric-Score-Based Allocation Designs
Optimal Adaptive Designs
Delayed Responses in Adaptive Designs
Real Adaptive Clinical Trials
Fluoxetine Trial
Pregabalin Trial
Simulated Trial
Concluding RemarksInverse Binomial Sampling
Partial Sequential Sampling
Use of Mann–Whitney Statistics
Fixed-Width Confidence Interval Estimation
Fixed-Width Confidence Interval for Partial Sequential Sampling
Inverse Sampling in Adaptive Designs
Concluding Remarks
Introduction: Cluster Randomized Trials
Intracluster Correlation Coefficient and Confidence Interval
Sample Size Calculation for Cluster Randomized Trials
Analysis of Cluster Randomized Trial Data
Concluding RemarksEpidemiologyHIV Dynamic Model
Treatment Effect Models
Bayesian Nonlinear Mixed-Effects Model
Predictions Using the Bayesian Mixed-Effects Modeling Approach
Simulation Study
Clinical Data Analysis
Concluding remarks
Space and Disease
Basic Spatial Questions and Related Data
Quantifying Pattern in Point Data
Predicting Spatial Observations
Concluding RemarksData Analysis via Population Models
Sequential Monte Carlo
Modeling Cholera
Fitting Structural Models to Cholera Data
Concluding RemarksEffect of Maternal Dietary Habits on Low Birth Weight in Babies
Binary Regression Models with Two Types of Error
Bivariate Binary Regression Models with Two Types of Error
Models for Analyzing Mixed Misclassified Binary and Continuous Responses
Atom Bomb Data Analysis
Concluding Remarks
PART III SURVIVAL ANALYSISExamples of Survival Models
Basic Estimation and Limit Theory
The Bootstrap
The Regular Case
The Profile Sampler
The Piggyback Bootstrap
Other Approaches
Concluding RemarksNonparametric Inferences
Semiparametric One-Sample Inference
Semiparametric Regression Method
Functional Regression Modeling
A Bivariate Accelerated Lifetime Model
Concluding RemarksModel Specification and Inferential Procedures
A Pseudo–Score Test
Simulation Studies
Trace DataInduced Dependent Censoring and Associated Identifiability Issues
Hazard Functions with Marked Point Process
Nonparametric Estimation
Martingales
Modeling Strategy for Testing and Regression
Calibration Regression for Lifetime Medical Cost
Two-Sample Multistate Accelerated Sojourn-Time Model
Concluding RemarksInverse Censoring Probability Weighted Estimators
NPMLE-Type Estimators
Data Application to Danish Twin Data
Nonparametric Dependence Estimation
Semiparametric Dependence Estimation
An Application to a Case–Control Family Study of Breast Cancer
Concluding RemarksEstimation in the Presence of Only Independent Censoring (with All Censoring Variables Observable)
Estimation in the Presence of Terminal Events
Large-Sample Properties
Simulation Studies
rhDNase Data
Concluding RemarksReview of CART
Methods Based on Measure of Within-Node Homogeneity
Methods Based on Between-Node Separation
Simulations for Comparison of Different Splitting Methods
Example: Breast Cancer Prognostic Study
Random Forest for Survival Data
Breast Cancer Study: Results from Random Forest Analysis
Concluding RemarksThe Random Right-Censorship Model
The Bayesian Model
Bayesian Functional Model Using Monotone Wavelet Approximation
Estimation of the Subdensity F*
Simulations
Examples
Concluding RemarksBioinformaticsIntergene Correlation
Differential Expression
Timecourse Experiments
Meta-Analysis
Concluding RemarksTechnology Details and Gene Identification
Analysis Methods
Example
Best Common Mean Difference Method
Effect Size Method
POE Assimilation Method
Comparison of Three Methods
Classification PerformanceNormalization
Methods for Selecting Differentially Expressed Genes
BH-T
SAM
SPH
LIMMA
MAANOVA
Simulation Study
Results of Simulation Studies
Concluding RemarksClustering of Tissue Samples
Step Screening of Genes
Step Clustering of Genes: Formation of Metagenes
Clustering of Gene Profiles
EMMIX-WIRE
Maximum-Likelihood Estimation via the EM Algorithm
Model Selection
Example: Clustering of Timecourse Data
Concluding RemarksThe Cox Proportional Hazards Model
Accelerated Failure-Time Model
Regularized Estimation for Censored Data Regression Models
L() Penalized Estimation of the Cox Model Using Kernels
L() Penalized Estimation of the Cox Model Using Least-Angle Regression
Regularized Buckley–James Estimation for the AFT Model
Regularization Based on Inverse Probability of Censoring Weighted Loss Function for the AFT Model
Penalized Estimation for the Additive Hazard Model
The Smoothing-Spline-Based Boosting Algorithm for the Nonparametric Additive Cox Model
Nonparametric-Pathway-Based Regression Models
Dimension-Reduction-Based Methods and Bayesian Variable Selection Methods
Application to a Real Dataset and Comparisons
Discussion and Future Research Topics
Development of Flexible Models for Gene–Gene and Gene–Environment Interactions
Concluding RemarksMaximum-Likelihood Analysis of Case–Control Data with Complete InformationMaximum-Likelihood Estimation under HWE and Gene–Environment IndependenceMethods
Application
Concluding RemarksGraphs of Biological Data
Statistics on Graphs
Graph-Theoretic Models
Stochastic Error
Exploratory Data Analysis
Sampling
Underlying Network Structure
Path Length: L
Clustering Coefficient: C
Experimental DataBiologic Overview of Splicing
Maximum-Entropy Models
Permuted Variable-Length Markov Models
Bayesian Network Approaches
Splice Site Recognition via Contemporary Classifiers
Random Forests
Boosting
Predictive Performance
Interpretational Yield
Computational Considerations
Concluding RemarksSample Ionization
Mass Analysis
Statistical Methods for Preprocessing
Multiple Testing and Identification of Differentially Expressed Peaks
A Semiparametric Model for Protein Mass Spectroscopy
Smoothed Principal-Component Analysis (PCA) for Proteomic Spectra
Wavelet-Based Functional Mixed Model and Application
A Nonparametric Bayesian Model Based on Kernel Functions
Potential Statistical Developments
Concluding RemarksThe Basic QTL Framework For Sib-Pairs
Nonparametric Alternatives
The Modified Nonparametric Regression
Evaluation of Significance Levels
Comparison With Linear Regression Methods
Significance Levels and Empirical Power
An Application to Real Data
Concluding RemarksMiscellaneous Topics
Introduction: The Need for Robust Procedures
Standard Tools for Robustness
Influence Function
Basic Miscellaneous Procedures
Alternative Approaches
The Robustness Question in Biomedical Studies
Robust Estimation in the Logistic Regression Model
Robust Estimation for Censored Survival Data
Adaptive Robust Methods in Clinical Trials
Concluding RemarksA General Biophysical Model
Bayesian deconvolution model (BDM)
Posterior Processing
An Example
Nonlinear Mixed-Effects Partial-Splines Models
Concluding RemarksStatistical Models
Multistage Models
Two-Stage Clonal Expansion Model
Physiologically Based Pharmacokinetic Models
Statistical Methods
Concluding Remarks