Cover	1
	Title Page	5
	Copyright Page	6
	Contents	7
	Contributors	21
	Preface	25
	1 Analysis of Over- and Underdispersed Data	27
	1.1 Introduction	27
	1.2 Overdispersed Binomial and Count Models	28
	1.2.1 Overdispersed Binomial Model	28
	1.2.2 Overdispersed Poisson Model	28
	1.2.3 Example	30
	1.3 Other Approaches to Account for Overdispersion	30
	1.3.1 Generalized Linear Mixed Model	30
	1.3.2 Zero-Inflated Models	32
	1.4 Underdispersion	32
	1.5 Software Notes	33
	References	33
	2 Analysis of Variance (ANOVA)	36
	2.1 Introduction	36
	2.2 Factors, Levels, Effects, and Cells	37
	2.3 Cell Means Model	38
	2.4 One-Way Classification	38
	2.4.1 Example 1	38
	2.5 Parameter Estimation	39
	2.6 The R(.) Notation—Partitioning Sum of Squares	39
	2.7 ANOVA—Hypothesis of Equal Means	41
	2.8 Multiple Comparisons	42
	2.9 Two-Way Crossed Classification	43
	2.10 Balanced and Unbalanced Data	43
	2.11 Interaction Between Rows and Columns	46
	2.12 Analysis of Variance Table	46
	References	50
	3 Assessment of Health-Related Quality of Life	52
	3.1 Introduction	52
	3.2 Choice of HRQOL Instruments	53
	3.3 Establishment of Clear Objectives in HRQOL Assessments	53
	3.4 Methods for HRQOL Assessment	55
	3.5 HRQOL as the Primary End Point	57
	3.6 Interpretation of HRQOL Results	58
	3.7 Examples	58
	3.7.1 HRQOL in Asthma	58
	3.7.2 HRQOL in Seasonal Allergy Rhinitis	60
	3.7,3 Symptom Relief for Late-Stage Cancers	61
	3.8 Conclusion	62
	Acknowledgment	62
	References	62
	Further Reading	65
	4 Bandit Processes and Response-Adaptive Clinical Trials: The Art of Exploration Versus Exploitation	66
	4.1 Introduction	66
	4.2 Exploration Versus Exploitation with Complete Observations	67
	4.2.1 The Model of Markov Decision Processes	68
	4.2.2 The Bandit Processes Under the Bayesian Approach	68
	4.2.3 The Response-Adaptive Clinical Trials	71
	4.3 Exploration Versus Exploitation with Censored Observations	72
	4.4 Conclusion	74
	Acknowledgments.	75
	References	75
	5 Bayesian Dose-Finding Designs in Healthy Volunteers	77
	5.1 Introduction	77
	5.2 A Bayesian Decision-Theoretic Design	78
	5.3 An Example of Dose Escalation in Healthy Volunteer Studies	80
	5.4 Discussion	85
	References	87
	6 Bootstrap	88
	6.1 Introduction	88
	6.2 Plug-In Principle	90
	6.2.1 How Useful is the Bootstrap Distribution?	90
	6.2.2 Other Population Estimates	91
	6.2.3 Other Sampling Procedures	91
	6.2.4 Other Statistics	91
	6.3 Monte Carlo Sampling—The "Second Bootstrap Principle"	92
	6.4 Bias and Standard Error	92
	6.5 Examples	93
	6.5.1 Relative Risk	93
	6.5.2 Linear Regression	94
	6.6 Model Stability	98
	6.6.1 Logistic Regression	101
	6.7 Accuracy of Bootstrap Distributions	103
	6.7.1 Systematic Errors in Bootstrap Distributions	108
	6.7.2 Bootstrap Distributions Are Too Narrow	108
	6.8 Bootstrap Confidence Intervals	109
	6.8.1 t Intervals	109
	6.8.2 Percentile Intervals	110
	6.8.3 Bootstrap t	110
	6.8.4 BCa Intervals	112
	6.8.5 Bootstrap Tilting Intervals	113
	6.8.6 Importance Sampling Implementation	113
	6.8.7 Confidence Intervals for Mean Arsenic Concentration	115
	6.8.8 Implications for Other Situations	116
	6.8.9 Comparing Intervals	116
	6.9 Hypothesis Testing	117
	6.10 Planning Clinical Trials	118
	6.10.1 "What If ' Analyses— Alternate Population Estimates	119
	6.11 How Many Bootstrap Samples Are Needed	121
	6.11.1 Assessing Monte Carlo Variation	124
	6.11.2 Variance Reduction	124
	6.12 Additional References	125
	References	125
	7 Conditional Power in Clinical Trial Monitoring	128
	7.1 Introduction	128
	7.2 Conditional Power	128
	7.3 Weight-Averaged Conditional Power or Bayesian Predictive Power	131
	7.4 Conditional Power of a Different Kind: Discordance Probability	132
	7.5 Analysis of a Randomized Trial	133
	7.6 Conditional Power: Pros and Cons	134
	References	135
	8 Cost-Effectiveness Analysis	137
	8.1 Introduction	137
	8.2 Definitions and Design Issues	137
	8.2.1 The Various Types of Analysis used in Economic Evaluation	137
	8.2.2 The Economic Perspective	138
	8.2.3 Choice of Comparator	138
	8.2.4 Setting and Timescale	139
	8.2.5 Sample Size	139
	8.2.6 Economic Analysis Plans	139
	8.3 Cost and Effectiveness Data	140
	8.3.1 The Measurement of Costs	140
	8.3.2 The Measurement of Effectiveness	140
	8.3.3 Quality-of-Life Scales	141
	8.4 The Analysis of Costs and Outcomes	141
	8.4.1 The Comparison of Costs	141
	8.4.2 The Incremental Cost- Effectiveness Ratio	141
	8.4.3 The Incremental Net Benefit	142
	8.4.4 The Cost-Effectiveness Acceptability Curve	144
	8.5 Robustness and Generalizability in Cost-Effectiveness Analysis	146
	8.5.1 Missing Data	146
	8.5.2 Censored Data	146
	8.5.3 Treatment Switches	146
	8.5.4 Multicenter Trials and Pooling	147
	8.5.5 Classic Sensitivity Analysis	147
	8.5.6 Regression Models	147
	8.5.7 Markov Models to Extrapolate Over Time	148
	8.5.8 Examples of Modeling and Sensitivity Analysis	148
	References	149
	Further Reading	151
	9 Cox-Type Proportional Hazards Models	152
	9.1 Introduction	152
	9.2 Cox Model for Univariate Failure Time Data Analysis	152
	9.2.1 Hazard Rate Function	152
	9.2.2 Model and Parameter Interpretation	153
	9.2.3 Parameter Estimation and Inference	153
	9.2.4 Stratified Population	154
	9.3 Marginal Models for Multivariate Failure Time Data Analysis	155
	9.3.1 Multiple Event Data	155
	9.3.2 Clustered Failure Time Data	156
	9.4 Practical Issues in Using the Cox Model	157
	9.4.1 Tied Data	157
	9.4.2 Time-Dependent Covariates	158
	9.4.3 Censoring Mechanism	159
	9.4.4 Assessing the Proportional Hazards Assumption	159
	9.4.5 Sample Size Calculation for Time-to-Event Data	160
	9.5 Examples	162
	9.5.1 Application to Lung Study	162
	9.5.2 Application to Framingham Heart Study	163
	9.5.3 Application to Diabetes Study	163
	9.6 Extensions	167
	9.7 Softwares and Codes	167
	9.7.1 R Code for the Sample Size Calculation	167
	9.7.2 SAS and R Codes for Fitting Proportional Hazards Models	169
	References	170
	Further Reading	171
	10 Empirical Likelihood Methods in Clinical Experiments	172
	10.1 Introduction	172
	10.2 Classical EL: Several Ingredients for Theoretical Evaluations	178
	10.3 The Relationship Between Empirical Likelihood and Bootstrap Methodologies	180
	10.4 Bayes Methods Based on Empirical Likelihoods	182
	10.5 Mixtures of Likelihoods	182
	10.6 An Example: ROC Curve Analyses Based on Empirical Likelihoods	183
	10.7 Applications of Empirical Likelihood Methodology in Clinical Trials or Other Data Analyses	184
	10.8 Concluding Remarks	184
	Appendix	187
	References	188
	11 Frailty Models	192
	11.1 Introduction	192
	11.2 Univariate Frailty Models	193
	11.3 Multivariate Frailty Models	196
	11.3.1 Shared Frailty Model	196
	11.3.2 Correlated Frailty Model	197
	11.4 Software	197
	References	198
	12 Futility Analysis	200
	12.1 Introduction	200
	12.2 Common Statistical Approaches to Futility Monitoring	201
	12.2.1 Statistical Background	201
	12.2.2 Conditional Power and Stochastic Curtailment	201
	12.2.3 Group Sequential Formulation of Futility Boundary	203
	12.2.4 Other Statistical Approaches to Constructing Futility Boundaries	203
	12.3 Examples	204
	12.3.1 Optimal Duration of Tamoxifen in Breast Cancer	204
	12.3.2 A Randomized Study of Antenatal Corticosteroids	205
	12.4 Discussion	206
	References	210
	Further Reading	212
	13 Imaging Science in Medicine I: Overview	213
	13.1 Introduction	213
	13.2 Advances in Medical Imaging	215
	13.3 Evolutionary Developments in Imaging	216
	13.3.1 Molecular Medicine	217
	13.3.2 Human Vision	218
	13.3.3 Image Quality	222
	13.3.4 Image Display/ Processing	232
	13.4 Conclusion	237
	References	238
	14 Imaging Science in Medicine, II: Basics of X-Ray Imaging	239
	14.1 Introduction to Medical Imaging: Different Ways of Creating Visible Contrast Among Tissues	239
	14.1.1 "On a New Kind of Ray"	240
	14.1.2 Contrast from Differential Interaction of Imaging Probes with Tissues	241
	14.1.3 Different Probes Interact with Different Tissues in Different Ways, Yielding Different Kinds of Contrast Information	242
	14.1.4 X Rays, Light, and Other Forms of Electromagnetic Radiation	243
	14.1.5 The Principal Concern in Medicine Is Usually "Good Enough" Contrast	246
	14.2 What the Body Does to the X-Ray Beam: Subject Contrast From Differential Attenuation of the X-Ray Beam by Various Tissues	248
	14.2.1 X-Ray Film of a Cracked Phalange	248
	14.2.2 Generating the Beam at the Anode/Target of the X-Ray Tube	249
	14.2.3 Exponential Attenuation of a Narrow Monochromatic Beam by a Homogeneous Medium	253
	14.2.4 Both of the Two Principal Mechanisms for the Interaction of X-Ray Photons with Atomic Electrons, Photoelectric Absorption (PA) and Compton Scatter (CS), are Important, and for Different Reasons	255
	14.2.5 What the Body Does to a Flat X-Ray Beam: Subject Contrast from Differential Attenuation	259
	14.3 What the X-Ray Beam Does to the Body: Known Medical Benefits Versus Possible Radiogenic Risks	261
	14.3.1 Dose of Ionizing Radiation, in Gray	262
	14.3.2 Radiogenic Risk Comes from Damage to DNA	264
	14.3.3 Deterministic and Teratogenic Radiation Health Effects	266
	14.3.4 Stochastic Effects and the Ubiquitous Linear No-Threshold Dose- Risk Assumption	266
	14.3.5 In Medicine, 1 Sievert = 1 Gray	267
	14.3.6 Effective Dose Is Expressed in Sieverts, Even in Medicine	268
	14.3.7 Special Considerations for Children	269
	14.3.8 The Radiation Safety Component of the Quality Assurance Program	271
	14.3.9 Dose to the Image Receptor	274
	14.4 Capturing the Visual Image: Analog (20th Century) X-Ray Image Receptors	274
	14.4.1 Screen-Film Radiography: What the Primary X-Ray Image Does to the Image Receptor	274
	14.4.2 Fluoroscopy with an Image Intensifier Tube and Electronic Optical Camera	279
	14.4.3 Image Quality	279
	14.4.4 The Image Quality Component of the QA Program	286
	15 Imaging Science in Medicine, III: Digital (21st Century) X-Ray Imaging	290
	15.1 The Computer in Medical Imaging	290
	15.1.1 A Bit About Bytes, etc.	291
	15.1.2 Digital Images	292
	15.1.3 Digital Image Processing: Enhancing Tissue Contrast, Signal-to- Noise, Edge Sharpness, etc.	297
	15.1.4 Picture Archiving and Communication Systems	300
	15.1.5 Image Analysis and Interpretation: Computer-Assisted Detection	302
	15.1.6 Computer and Computer-Network Security	303
	15.1.7 Liquid Crystal and Other Digital Displays	304
	15.1.8 The Joy of Digital	304
	15.2 The Digital Planar X-Ray Modalities: Computed Radiography and Digital Radiography and Fluoroscopy	305
	15.2.1 Computed Radiography	307
	15.2.2 Digital Radiography with an Active Matrix Flat-Panel Imager	308
	15.3 Digital Fluoroscopy and Digital Subtraction Angiography	313
	15.4 Digital Tomosynthesis: Planar Imaging in Three Dimensions	316
	15.5 Computed Tomography: Superior Contrast in Three-Dimensional X-Ray Attenuation Maps	318
	15.5.1 Raw Data: The Ray- Projection/Ray-Sum Measures the Total Attenuation Along a Geometric Ray	319
	15.5.2 Filtered Back- Projection Reconstruction	323
	15.5.3 Generations of CT Devices	327
	15.5.4 CT Dose and QA	334
	16 Intention-to-Treat Analysis	339
	16.1 Introduction	339
	16.2 Missing Information	339
	16.2.1 Background	339
	16.2.2 Ignorable Missing Data	340
	16.2.3 Conditionally Ignorable Missing Data	341
	16.2.4 Potential for Bias	341
	16.3 The Intention-to-Treat Design	342
	16.3.1 Withdrawal from Treatment Versus Withdrawal from Follow-Up	343
	16.3.2 Investigator and Subject Training/ Education	343
	16.3.3 The Intent-to-Treat Analysis	344
	16.3.4 Intent-to-Treat Subset Analysis	344
	16.3.5 LOCF Analysis	344
	16.3.6 Structurally Missing Data	345
	16.3.7 Worst Rank Analyses	345
	16.4 Efficiency of the Intent-to- Treat Analysis	345
	16.4.1 Power	345
	16.4.2 Sample Size	346
	16.5 Compliance-Adjusted Analyses	346
	16.6 Conclusion	346
	References	347
	Further Reading	347
	17 Interim Analyses	349
	17.1 Introduction	349
	17.2 Opportunities and Dangers of Interim Analyses	350
	17.3 The Development of Techniques for Conducting Interim Analyses	351
	17.4 Methodology for Interim Analyses	351
	17.4.1 The Treatment Effect Parameter	352
	17.4.2 Test Statistics for Use at Interim Analyses	352
	17.4.3 Stopping Rules at Interim Analyses	353
	17.4.4 Analysis Following a Sequential Trial	354
	17.5 An Example: Statistics for Lamivudine	354
	17.6 Interim Analyses in Practice	355
	17.7 Conclusions	357
	References	357
	18 Interrater Reliability	360
	18.1 Definition	360
	18.2 The Importance of Reliability in Clinical Trials	360
	18.3 How Large a Reliability Coefficient Is Large Enough?	361
	18.4 Design and Analysis of Reliability Studies	361
	18.5 Estimate of the Reliability Coefficient—Parametric	362
	18.6 Estimation of the Reliability Coefficient—Nonparametric	362
	18.7 Estimation of the Reliability Coefficient—Binary	363
	18.8 Estimation of the Reliability Coefficient—Categorical	363
	18.9 Strategies to Increase Reliability (Spearman–Brown Projection)	363
	18.10 Other Types of Reliabilities	364
	References	364
	19 Intrarater Reliability	366
	19.1 Introduction	366
	19.2 Intrarater Reliability for Continuous Scores	366
	19.2.1 Defining Intrarater Reliability	367
	19.2.2 Statistical Inference	368
	19.2.3 Optimizing the Design of the Intrarater Reliability Study	370
	19.3 Nominal Scale Score Data	374
	19.3.1 Intrarater Reliability: Single Rater and Two Replications	375
	19.3.2 Intrarater Reliability: Single Rater and Multiple Replications	377
	19.3.3 Statistical Inference	378
	19.4 Ordinal and Interval Score Data	379
	19.5 Concluding Remarks	380
	References	381
	Further Reading	382
	20 Kaplan–Meier Plot	383
	20.1 Introduction	383
	20.2 Estimation of Survival Function	384
	20.2.1 An Example	385
	20.2.2 Practical Notes	385
	20.2.3 Median Survival Time	388
	20.2.4 More Practical Notes	388
	20.3 Additional Topics	389
	References	390
	21 Logistic Regression	391
	21.1 Introduction	391
	21.2 Fitting the Logistic Regression Model	392
	21.3 The Multiple Logistic Regression Model	394
	21.4 Fitting the Multiple Logistic Regression Model	395
	21.5 Example	395
	21.6 Testing for the Significance of the Model	397
	21.7 Interpretation of the Coefficients of the Logistic Regression Model	399
	21.8 Dichotomous Independent Variable	399
	21.9 Polytomous Independent Variable	401
	21.10 Continuous Independent Variable	401
	21.11 Multivariate Case	403
	References	405
	22 Metadata	406
	22.1 Introduction	406
	22.2 History/Background	406
	22.2.1 A Metadata Example	406
	22.2.2 Geospatial Data	407
	22.2.3 Research Data and Statistical Software	408
	22.2.4 Electronic Regulatory Submission	408
	22.3 Data Set Metadata	409
	22.3.1 Data Set-Level Metadata	409
	22.3.2 Variable-Level Metadata	410
	22.3.3 Value-Level Metadata	412
	22.3.4 Item-Level Metadata	412
	22.4 Analysis Results Metadata	414
	22.5 Regulatory Submission Metadata	415
	22.5.1 ICH Electronic Common Technical Document	415
	22.5.2 FDA Guidance on eCTD Submissions	415
	References	416
	23 Microarray	418
	23.1 Introduction	418
	23.1.1 MammaPrint	419
	23.2 What is a Microarray?	419
	23.2.1 Types of Expression Microarrays	420
	23.2.2 Microarrays Can Generate Reproducible Results	421
	23.3 Other Array Technologies	421
	23.3.1 Genotyping Using Expression Microarrays	421
	23.3.2 Splicing Arrays	422
	23.3.3 Exon Array	422
	23.3.4 Tiling Array— Including Methylation Arrays	423
	23.3.5 SNP Chip	423
	23.3.6 ChlP-on-Chip	423
	23.3.7 Protein Arrays	424
	23.4 Define Objectives of the Study	424
	23.5 Experimental Design for Microarray	425
	23.5.1 Avoidance of Experimental Artifacts	425
	23.5.2 Randomization, Blocking, and Blinding	425
	23.5.3 Replication	425
	23.5.4 Practice, Practice, Practice	426
	23.5.5 Strict Experimental Practices	426
	23.6 Data Extraction	427
	23.6.1 Image Processing from cDNA and Long Oligo Arrays	427
	23.6.2 Image Analysis of Affymetrix GeneChip Microarrays	427
	23.6.3 Normalization of DNA Data	428
	23.7 Microarray Informatics	428
	23.8 Statistical Analysis	428
	23.8.1 Class Prediction Analysis	428
	23.8.2 Class Discovery Analysis	429
	23.8.3 Class Differentiation Analysis	429
	23.9 Annotation	430
	23.10 Pathway, GO, and Class-Level Analysis Tools	430
	23.11 Validation of Microarray Experiments	431
	23.12 Conclusions	431
	References	432
	24 Multi-Armed Bandits, Gittins Index, and Its Calculation	442
	24.1 Introduction	442
	24.2 Mathematical Formulation of Multi-Armed Bandits	442
	24.2.1 An Example	443
	24.2.2 Solution Concept and the Gittins Index	444
	24.2.3 Salient Features of the Model	444
	24.3 Off-Line Algorithms for Computing Gittins Index	445
	24.3.1 Largest-Remaining- Index Algorithm (Varaiya, Walrand, and Buyukkoc)	446
	24.3.2 State-Elimination Algorithm (Sonin)	447
	24.3.3 Triangularization Algorithm (Denardo, Park, and Rothblum)	449
	24.3.4 Fast-Pivoting Algorithm (Niño-Mora)	451
	24.3.5 An Efficient Method to Compute Gittins Index of Multiple Processes	453
	24.4 On-Line Algorithms for Computing Gittins Index	454
	24.4.1 Restart Formulation (Katehakis and Veinott)	455
	24.4.2 Linear Programming Formulation (Chen and Katehakis)	455
	24.5 Computing Gittins Index for the Bernoulli Sampling Process	456
	24.5.1 Dynamic Programming Formulation (Gittins)	457
	24.5.2 Restart Formulation (Katehakis and Derman)	457
	24.5.3 Closed-Form Approximations	458
	24.5.4 Qualitative Properties of Gittins Index	458
	24.6 Conclusion	459
	References	459
	25 Multiple Comparisons	462
	25.1 Introduction	462
	25.2 Strong and Weak Control of the FWE	462
	25.3 Criteria for Deciding Whether Adjustment is Necessary	463
	25.4 Implicit Multiplicity: Two-Tailed Testing	464
	25.5 Specific Multiple Comparison Procedures	465
	25.5.1 Multiple Arms	465
	25.5.2 Multiple End Points	466
	25.5.3 Subgroup Analyses	468
	25.5.4 Interim Monitoring	469
	References	470
	26 Multiple Evaluators	472
	26.1 Introduction	472
	26.2 Agreement for Continuous Data	473
	26.2.1 Hypothesis Testing Approach	473
	26.2.2 An Index Approach	473
	26.2.3 An Interval Approach	475
	26.3 Agreement for Categorical Data	475
	26.3.1 Measuring Agreement between Two Evaluators	476
	26.3.2 Extensions to Kappa and Other Approaches for Modeling Patterns of Agreement	477
	26.3.3 Issues with Kappa	478
	26.4 Summary and Discussion	479
	Acknowledgments	479
	References	479
	27 Noncompartmental Analysis	483
	27.1 Introduction	483
	27.2 Terminology	484
	27.2.1 Compartment	484
	27.2.2 Parameter	484
	27.2.3 Fixed Constant	484
	27.2.4 Statistic	484
	27.2.5 Comment	484
	27.3 Objectives and Features of Noncompartmental Analysis	485
	27.3.1 Advanced Noncompartmental Techniques	485
	27.4 Comparison of Noncompartmental and Compartmental Models	486
	27.5 Assumptions of NCA and Its Reported Descriptive Statistics	486
	27.5.1 Assumption 1: Routes and Kinetics of Drug Absorption	487
	27.5.2 Assumptions 2 to 4: Drug Distribution	487
	27.5.3 Assumption 5: Routes of Drug Elimination	489
	27.5.4 Assumption 6: Kinetics of Drug Elimination	489
	27.5.5 Assumptions 7 and 8: Sampling Times and Monoexponential Decline	489
	27.5.6 Assumption 9: Time Invariance of Disposition Parameters	490
	27.5.7 Assumptions of Subsequent Descriptive Statistics	490
	27.6 Calculation Formulas for NCA	490
	27.6.1 NCA of Plasma or Serum Concentrations by Numerical Integration	491
	27.6.2 NCA of Plasma or Serum Concentrations by Curve Fitting	496
	27.6.3 NCA with Plasma or Serum Concentrations and Amounts in Urine	497
	27.6.4 Superposition Methods and Deconvolution	497
	27.7 Guidelines for Performance of NCA Based on Numerical Integration	498
	27.7.1 How to Select Concentration Data Points for Estimation of Terminal Half-Life	498
	27.7.2 How to Handle Samples Below the Quantification Limit	500
	27.7.3 NCA for Sparse Concentration-Time Data	501
	27.7.4 Reporting the Results of an NCA	501
	27.7.5 How to Design a Clinical Trial That Is to Be Analyzed by NCA	502
	27.8 Conclusions and Perspectives	503
	Acknowledgments	503
	References	503
	Further Reading	508
	28 Nonparametric ROC Analysis for Diagnostic Trials	509
	28.1 Introduction	509
	28.2 Different Aspects of Study Design	510
	28.3 Nonparametric Models and Hypotheses	512
	28.4 Point Estimator	513
	28.5 Asymptotic Distribution and Variance Estimator	514
	28.6 Derivation of the Confidence Interval	516
	28.7 Statistical Tests	516
	28.8 Adaptations for Cluster Data	516
	28.9 Results of a Diagnostic Study	517
	28.10 Summary and Final Remarks	520
	Acknowledgments.	520
	References	520
	29 Optimal Biological Dose for Molecularly Targeted Therapies	522
	29.1 Introduction	522
	29.2 Phase I Dose-Finding Designs for Cytotoxic Agents	523
	29.3 Phase I Dose-Finding Designs for Molecularly Targeted Agents	523
	29.3.1 Dynamic De-escalating Designs	524
	29.3.2 Dose Determination through Simultaneous Investigation of Efficacy and Toxicity	525
	29.3.3 Individualized Maximum Repeatable Dose	526
	29.3.4 Proportion Designs and Slope Designs	527
	29.3.5 Generalized Proportion Designs	527
	29.4 Discussion	528
	References	529
	Further Reading	531
	30 Over- and Underdispersion Models	532
	30.1 Introduction	532
	30.2 Count Dispersion Models	534
	30.2.1 Mixed Poisson	534
	30.2.2 Compound Poisson	535
	30.2.3 Weighted Poisson	536
	30.2.4 Lagrangian Poisson	538
	30.2.5 Semiparametric Poisson	539
	30.3 Count Explanatory Models	540
	30.3.1 Generalized Linear Models	541
	30.3.2 Count Time Series Models	542
	30.3.3 Nonparametric Models	544
	30.4 Summary and Final Remarks	545
	References	546
	31 Permutation Tests in Clinical Trials	553
	31.1 Randomization Inference—Introduction	553
	31.2 Permutation Tests—How They Work	554
	31.3 Normal Approximation to Permutation Tests	557
	31.4 Analyze as You Randomize	558
	31.5 Interpretation of Permutation Analysis Results	559
	31.6 Summary	560
	References	560
	32 Pharmacoepidemiology, Overview	562
	32.1 Introduction	562
	32.2 The Case–Crossover Design	563
	32.3 Confounding Bias	565
	32.3.1 Missing Confounder Data	566
	32.3.2 The Case–Time– Control Design	567
	32.4 Risk Functions Over Time	569
	32.5 Probabilistic Approach for Causality Assessment	571
	32.6 Methods Based on Prescription Data	572
	Acknowledgments	573
	References	574
	33 Population Pharmacokinetic and Pharmacodynamic Methods	577
	33.1 Introduction	577
	33.2 Terminology	578
	33.2.1 Variable	578
	33.2.2 Parameter	578
	33.2.3 Variability	578
	33.2.4 Uncertainty	578
	33.2.5 Covariate (also Called Covariable)	579
	33.2.6 Covariance	579
	33.2.7 Mixed Effects Model	579
	33.2.8 Subject Variability	579
	33.3 Fixed Effects Models	579
	33.3.1 Input-Output Models	579
	33.3.2 Group Models	580
	33.4 Random Effects Models	581
	33.4.1 Parameter Variability Model	581
	33.4.2 Residual Error Model	582
	33.5 Model Building and Parameter Estimation	582
	33.5.1 Hypothesis Testing	583
	33.5.2 Objective Function	584
	33.5.3 Bootstrap	584
	33.5.4 Bayesian Estimation	585
	33.5.5 Mixture Models	587
	33.6 Software	587
	33.6.1 NONMEM	588
	33.6.2 Other Programs	588
	33.7 Model Evaluation	588
	33.7.1 Residuals	589
	33.7.2 Predictive Checks	589
	33.7.3 Prediction Discrepancy	589
	33.7.4 Cross-Validation and External Validation	591
	33.8 Stochastic Simulation	591
	33.9 Experimental Design	591
	33.10 Applications	592
	33.10.1 Drug Development	592
	33.10.2 Regulatory Science	592
	33.10.3 Human and Disease Biology	592
	Acknowledgment	593
	References	593
	Further Reading	594
	34 Proportions: Inferences and Comparisons	596
	34.1 Introduction	596
	34.2 One-Sample Case	597
	34.2.1 Point Estimation	597
	34.2.2 Interval Estimation	597
	34.2.3 Hypothesis Testing	601
	34.2.4 Power and Sample Size Determination	603
	34.2.5 One-Sample Summary	604
	34.3 Two Independent Samples	604
	34.3.1 Asymptotic Methods	605
	34.3.2 Exact Methods	607
	34.3.3 Bayesian Methods	610
	34.3.4 Study Design and Power	611
	34.3.5 Noninferiority and Equivalence Tests	612
	34.3.6 Two-Sample Summary	613
	34.4 Note on Software	614
	References	615
	35 Publication Bias	621
	35.1 Publication Bias and the Validity of Research Reviews	621
	35.2 Research on Publication Bias	622
	35.2.1 Direct Evidence of Publication Bias	622
	35.2.2 Indirect Evidence of Publication Bias	623
	35.2.3 Clinical Significance of the Evidence	623
	35.3 Data Suppression Mechanisms Related to Publication Bias	623
	35.4 Prevention of Publication Bias	624
	35.4.1 Trials Registries	625
	35.4.2 Prospective Meta-Analysis	625
	35.4.3 Thorough Literature Search	625
	35.5 Assessment of Publication Bias	625
	35.5.1 File Drawer Analysis (Failsafe N Approach)	626
	35.5.2 Funnel Plots	626
	35.5.3 Statistical Tests	627
	35.5.4 Selection Models	629
	35.5.5 Comparing the Results of the Different Methods	630
	35.6 Impact of Publication Bias	631
	References	631
	Further Reading	633
	36 Quality of Life	634
	36.1 Background	634
	36.1.1 Health-Related Quality of Life	634
	36.2 Measuring Health-Related Quality of Life	635
	36.2.1 Health Status Versus Patient Preferences	635
	36.2.2 Objective Versus Subjective	637
	36.2.3 Generic Versus Disease-Specific Instruments	637
	36.2.4 Global Index Versus Profile of Domain- Specific Measures	637
	36.2.5 Response Format	638
	36.2.6 Period of Recall	638
	36.2.7 Scoring	639
	36.3 Development and Validation of HRQoL Measures	639
	36.3.1 Development	639
	36.3.2 Validation	639
	36.3.3 Translation / Cross- Cultural Validation	641
	36.3.4 Item Banking and Computer-Adaptive Testing	641
	36.4 Use in Research Studies	641
	36.4.1 Instrument Selection	641
	36.4.2 Multiple End Points	642
	36.4.3 Missing Data	642
	36.5 Interpretation/Clinical Significance	643
	36.5.1 Distributional Methods	643
	36.5.2 Anchor-Based Methods	643
	36.6 Conclusions	644
	References	645
	37 Relative Risk Modeling	648
	37.1 Introduction	648
	37.2 Why Model Relative Risks?	648
	37.3 Data Structures and Likelihoods	649
	37.4 Approaches to Model Specification	650
	37.4.1 Empiric Models	650
	37.4.2 Models for Extended Exposure Histories	653
	37.4.3 Nonparametric Models	654
	37.5 Mechanistic Models	655
	References	656
	38 Sample Size Considerations for Morbidity/Mortality Trials	659
	38.1 Introduction	659
	38.2 General Framework for Sample Size Calculation	659
	38.3 Choice of Test Statistics	660
	38.3.1 Parametric Tests	660
	38.3.2 Nonparametric Tests	661
	38.4 Adjustment of Treatment Effect	662
	38.4.1 The Discrete Nonstationary Markov Process Model	663
	38.4.2 Implementation	664
	38.4.3 Illustration	665
	38.5 Informative Noncompliance	665
	References	666
	39 Sample Size for Comparing Means	668
	39.1 Introduction	668
	39.2 One-Sample Design	669
	39.2.1 Test for Equality	669
	39.2.2 Test for Noninferiority / Superiority	669
	39.2.3 Test for Equivalence	670
	39.2.4 An Example	670
	39.2.4 An Example	670
	39.3 Two-Sample Parallel Design	671
	39.3.1 Test for Equality	671
	39.3.2 Test for Noninferiority / Superiority	671
	39.3.3 Test for Equivalence	672
	39.3.4 An Example	672
	39.4 Two-Sample Crossover Design	672
	39.4.1 Test for Equality	673
	39.4.2 Test for Noninferiority / Superiority	673
	39.4.3 Test for Equivalence	674
	39.4.4 An Example	674
	39.5 Multiple-Sample One-Way ANOVA	674
	39.5.1 Pairwise Comparison	674
	39.5.2 Simultaneous Comparison	675
	39.5.3 An Example	675
	39.6 Multiple-Sample Williams Design	676
	39.6.1 Test for Equality	676
	39.6.2 Test for Noninferiority / Superiority	677
	39.6.3 Test for Equivalence	677
	39.6.4 An Example	677
	39.7 Discussion	677
	References	678
	40 Sample Size for Comparing Proportions	679
	40.1 Introduction	679
	40.2 One-Sample Design	680
	40.2.1 Test for Equality	680
	40.2.2 Test for Non-Inferiority / Superiority	680
	40,2.3 Test for Equivalence	681
	40.2.4 An Example	681
	40.3 Two-Sample Parallel Design	681
	40.3.1 Test for Equality	682
	40.3.2 Test for Non-Inferiority / Superiority	682
	40.3.3 Test for Equivalence	682
	40.3.4 An Example	683
	40.4 Two-Sample Crossover Design	683
	40.4.1 Test for Equality	684
	40.4.2 Test for Non-Inferiority / Superiority	684
	40.4.3 Test for Equivalence	685
	40.4.4 An Example	685
	40.5 Relative Risk—Parallel Design	685
	40.5.1 Test for Equality	686
	40.5.2 Test for Non-Inferiority / Superiority	686
	40.5.3 Test for Equivalence	686
	40.5.4 An Example	687
	40.6 Relative Risk—Crossover Design	687
	40.6.1 Test for Equality	688
	40.6.2 Test for Non-Inferiority / Superiority	688
	40.6.3 Test for Equivalence	688
	40.6.4 An Example	689
	40.7 Discussion	689
	References	689
	41 Sample Size for Comparing Time-to-Event Data	690
	41.1 Introduction	690
	41.2 Exponential Model	690
	41.2.1 Test for Equality	691
	41.2.2 Test for Noninferiority / Superiority	692
	41.2.3 Test for Equivalence	692
	41.2.4 An Example	693
	41.3 Cox's Proportional Hazards Model	693
	41.3.1 Test for Equality	693
	41.3.2 Test for Noninferiority / Superiority	694
	41.3.3 Test for Equivalence	694
	41.3.4 An Example	695
	41.4 Log-Rank Test	695
	41.4.1 An Example	696
	41.5 Discussion	696
	References	696
	42 Sample Size for Comparing Variabilities	698
	42.1 Introduction	698
	42.2 Comparing Intrasubject Variabilities	698
	42.2.1 Parallel Design with Replicates	699
	42.2.2 Replicated Crossover Design	700
	42.3 Comparing Intersubject Variabilities	702
	42.3.1 Parallel Design with Replicates	703
	42.3.2 Replicated Crossover Design	704
	42.4 Comparing Total Variabilities	706
	42.4.1 Parallel Designs Without Replicates	706
	42.4.2 Parallel Design with Replicates	708
	42.4.3 The Standard 2 x 2 Crossover Design	710
	42.4.4 Replicated 2 x 2m Crossover Design	711
	42.5 Discussion	713
	References	713
	43 Screening, Models of	715
	43.1 Introduction	715
	43.2 What is Screening?	715
	43.3 Why Use Modeling?	717
	43.4 Characteristics of Screening Models	718
	43.4.1 Types of Model	718
	43.4.2 Markov Framework for Modeling	718
	43.5 A Simple Disease and Screening Model	719
	43.6 Analytic Models for Cancer	721
	43.7 Simulation Models for Cancer	730
	43.8 Model Fitting and Validation	734
	43.8.1 An Example of Model Fitting	737
	43.8.2 An Application of the Model to Breast Cancer Screening	739
	43.8.3 A Comparison of Two Models for Breast Cancer	739
	43.9 Models for Other Diseases	741
	43.10 Current State and Future Directions	742
	References	743
	44 Screening Trials	747
	44.1 Introduction	747
	44.2 Design Issues	747
	44.3 Sample Size	748
	44.4 Study Designs	749
	44.4.1 Classic Two-Arm Trial That Addresses a Single Question	749
	44.4.2 Designs for Investigating More than One Question in the Same Study	749
	44.5 Analysis	751
	44.5.1 Primary Analysis	751
	44.5.2 Overall Mortality Analysis	751
	44.5.3 Limited Mortality Analysis	752
	44.5.4 Comparability	752
	44.5.5 Secondary Analyses	753
	44.6 Trial Monitoring	754
	References	754
	45 Secondary Efficacy End Points	757
	45.1 Introduction	757
	45.2 Literature Review	760
	45.3 Review of Methodology for Multiplicity Adjustment and Gatekeeping Strategies for Secondary End Points	762
	45.4 Summary	764
	References	764
	Further Reading	765
	46 Sensitivity, Specificity, and Receiver Operator Characteristic (ROC) Methods	766
	46.1 Evaluating a Single Binary Test Against a Binary Criterion	766
	46.2 Evaluation of a Single Binary Test: ROC Methods	769
	46.3 Evaluation of a Test Response Measured on an Ordinal Scale: ROC Methods	771
	46.4 Evaluation of Multiple Different Tests	773
	46.4.1 A Family of Tests	773
	46.5 The Optimal Sequence of Tests	773
	46.6 Sampling and Measurement Issues	775
	46.6.1 Naturalistic Sampling	775
	46.6.2 Prospective or Two- Stage Sampling	775
	46,6.3 Retrospective (Case- Control) Sampling	776
	46.7 Summary	776
	References	777
	47 Software for Genetics/Genomics	778
	47.1 Introduction	778
	47.2 Data Management	778
	47.2.1 Data Storage and Retrieval	780
	47.2.2 Data Visualization	780
	47.2.3 Data Processors	780
	47.2.4 Error Detection for Family and Genotype Data	787
	47.2.5 Error Detection for Genetic Maps	788
	47.3 Genetic Analysis	788
	47.3.1 Summary Statistics	788
	47.3.2 Familial Aggregation, Commingling, and Segregation Analysis	789
	47.3.3 Linkage Analysis	789
	47.3.4 Association Analysis	790
	47.3.5 Linkage Disequilibrium and Transmission Disequilibrium Tests	791
	47.3.6 Admixture Mapping	792
	47.3.7 Haplotype Analysis	793
	47.3.8 Software Suites	794
	47.4 Genomic Analysis	794
	47.5 Other	796
	47.5.1 Power Calculations/ Simulation	796
	47.5.2 Meta-analysis	796
	References	796
	Further Reading	802
	48 Stability Study Designs	804
	48.1 Introduction	804
	48.2 Stability Study Designs	805
	48.2.1 Full Design	805
	48.2.2 Reduced Design	806
	48.2.3 Other Fractional Factorial Designs	808
	48.3 Criteria for Design Comparison	808
	48.4 Stability Protocol	814
	48.5 Basic Design Considerations	814
	48.5.1 Impact of Design Factors on Shelf Life Estimation	814
	48.5.2 Sample Size and Sampling Considerations	815
	48.5.3 Other Issues	815
	48.6 Conclusions	816
	Acknowledgments	816
	References	816
	49 Subgroup Analysis	819
	49.1 Introduction	819
	49.2 The Dilemma of Subgroup Analysis	819
	49.3 Planned Versus Unplanned Subgroup Analysis	820
	49.4 Frequentist Methods	821
	49.4.1 Significance Testing Within Subgroups	821
	49.5 Testing Treatment by Subgroup Interactions	822
	49.6 Subgroup Analyses in Positive Clinical Trials	823
	49.7 Confidence Intervals for Treatment Effects within Subgroups	824
	49.8 Bayesian Methods	825
	References	826
	50 Survival Analysis, Overview	828
	50.1 Introduction	828
	50.2 History	828
	50.2.1 The Prehistory of Survival Analysis in Demography and Actuarial Science	828
	50.2.2 The "Actuarial" Life Table and the Kaplan– Meier Estimator	829
	50.2.3 Parametric Survival Models	830
	50.2.4 Multistate Models	830
	50.3 Survival Analysis Concepts	830
	50.4 Nonparametric Estimation and Testing	831
	50.5 Parametric Inference	833
	50.6 Comparison with Expected Survival	833
	50.7 The Cox Regression Model	833
	50.8 Other Regression Models for Survival Data	835
	50.9 Multistate Models	835
	50.10 Other Kinds of Incomplete Observation	837
	50.11 Multivariate Survival Analysis	837
	50.12 Concluding Remarks	837
	References	838
	51 The FDA and Regulatory Issues	841
	51.1 Caveat	841
	51.2 Introduction	841
	51.3 Chronology of Drug Regulation in the United States	842
	51.4 FDA Basic Structure	846
	51.5 IND Application Process	846
	51.5.1 Types of IND	847
	51.5.2 Parallel Track	848
	51.5.3 Resources for Preparation of IND Applications	848
	51.5.4 The First Step, the Phase I IND Application	849
	51.5.5 Meetings with the FDA (http://www. fda. gov / cder / guidance / 2125fnl.pdf)	854
	51.6 Drug Development and Approval Time Frame	855
	51.6.1 Accelerated Development /Review	855
	51.6.2 Fast Track Programs	856
	51.6.3 Safety of Clinical Trials	856
	51.7 NDA Process	857
	51.7.1 Review Priority Classification	859
	51.7.2 P—Priority Review	859
	51.7.3 S—Standard Review	859
	51.7.4 Accelerated Approval (21 CFR Subpart H Sec. 314.510)	860
	51.8 U.S. Pharmacopeia and FDA	860
	51.9 CDER Freedom of Information Electronic Reading Room	861
	51.10 Conclusion	861
	52 The Kappa Index	862
	52.1 Introduction	862
	52.2 The Kappa Index	862
	52.2.1 Reliability in Two Applications of a Dichotomous Test	863
	52.2.2 Reliability for Tests with Multiple Categorical Outcomes	864
	52.3 Inference for Kappa via Generalized Estimating Equations	866
	52.4 The Dependence of Kappa on Marginal Rates	868
	52.5 General Remarks	869
	References	869
	53 Treatment Interruption	872
	53.1 Introduction	872
	53.2 Therapeutic TI Studies in HIV/AIDS	872
	53.2.1 Overview	872
	53.2.2 Design Issues in Therapeutic TI studies	876
	53.3 Management of Chronic Disease	879
	53.4 Analytic Treatment Interruption in Therapeutic Vaccine Trials	880
	53.5 Randomized Discontinuation Designs	881
	53.6 Final Comments	882
	References	882
	54 Trial Reports: Improving Reporting, Minimizing Bias, and Producing Better Evidence-Based Practice	886
	54.1 Introduction	886
	54.2 Reporting Issues in Clinical Trials	886
	54.2.1 Incomplete Reporting of Trial Methods	886
	54.2.2 Selective Reporting of Outcomes	887
	54.2.3 Failure to Report Trials	887
	54.2.4 Spin in the Interpretation of Trials	888
	54.3 Moral Obligation to Improve the Reporting of Trials	889
	54.4 Consequences of Poor Reporting of Trials	889
	54.4.1 Impact on Knowledge Syntheses	889
	54.5 Distinguishing Between Methodological and Reporting Issues	890
	54.5.1 Cochrane Risk of Bias Tool	891
	54.6 One Solution to Poor Reporting: CONSORT 2010 and CONSORT Extensions	892
	54.7 Impact of CONSORT	892
	54.8 Guidance for Reporting Randomized Trial Protocols: SPIRIT	896
	54.9 Trial Registration	896
	54.10 Final Thoughts	897
	References	898
	55 U.S. Department of Veterans Affairs Cooperative Studies Program	902
	55.1 Introduction	902
	55.2 History of the Cooperative Studies Program (CSP)	902
	55.3 Organization and Functioning of the CSP	904
	55.3.1 Planning Request	904
	55.3.2 Planning Phase	906
	55.3.3 Evaluation Phase	906
	55.3.4 Implementation of the Trial	907
	55.3.5 Final Analysis and Publication Phase	910
	55.4 Roles of the Biostatistician and Pharmacist in the CSP	911
	55.5 Ongoing and Completed Cooperative Studies (1972–2000)	913
	55.6 Current Challenges and Opportunities	913
	55.6.1 Changes in the VA Health Care System	913
	55.6.2 Concerns about Patients' Rights in Research	917
	55.6.3 Efficiency and Interdependence of the CSPCCs	918
	55.6.4 Ensuring the Adequacy of Flow of Ideas and Training of Investigators	918
	55.6.5 Partnering with Outside Organizations	919
	55.7 Concluding Remarks	921
	Acknowledgments	921
	References	923
	56 Women's Health Initiative: Statistical Aspects and Selected Early Results	927
	56.1 Introduction	927
	56.2 WHI Clinical Trial and Observational Study	927
	56.3 Study Organization	929
	56.4 Principal Clinical Trial Comparisons, Power Calculations, and Safety and Data Monitoring	929
	56.5 Biomarkers and Intermediate Outcomes	934
	56.6 Data Management and Computing Infrastructure	934
	56.7 Quality Assurance Program Overview	936
	56.8 Early Results from the WHI Clinical Trial	937
	56.9 Summary and Discussion	938
	Acknowledgments.	938
	References	938
	57 World Health Organization (WHO): Global Health Situation	940
	57.1 Introduction	940
	57.2 Program Activities to the End of the Twentieth Century	941
	57.2.1 African Region	942
	57.2.2 Region of the Americas	942
	57.2.3 Eastern Mediterranean Region	943
	57.2.4 European Region	943
	57.2.5 Southeast Asia Region	943
	57.2.6 Western Pacific Region	943
	57.2.7 Main Activities at Global and Regional Levels	944
	57.3 Vision for the Use and Generation of Data in the First Quarter of the Twenty-First Century	945
	Reference	949
	Further Reading	949
	Index	951
	EULA	963