- ISBN: 9780470670156 | 0470670150
- Cover: Hardcover
- Copyright: 10/1/2012
This book provides a comprehensive, in-depth treatment of statistical methods in healthcare. The book uses an integrated, case study-based approach, with focus on applications to make a go-to reference source for practitioners and specialists in health care and drug development. Offering a broad coverage of standards and established methods through leading-edge techniques, this book looks at the statistical methods used in planning, delivering, and monitoring healthcare. Also covered are the statistical aspects of the development and production of pharmaceuticals and medical devices.
Frederick Faltin, Founder and Managing Director of The Faltin Group, USA.
Ron Kenett, Chairman and CEO of the KPA Group, Israel.
Fabrizio Ruggeri, CNR IMATI, Italy.
Foreword xix
Preface xxi
Editors xxiii
Contributors xxv
Part One STATISTICS IN THE DEVELOPMENT OF PHARMACEUTICAL PRODUCTS
1 Statistical Aspects in ICH, FDA and EMA Guidelines 3
Allan Sampson and Ron S. Kenett
Synopsis 3
1.1 Introduction 3
1.2 ICH Guidelines Overview 5
1.3 ICH Guidelines for Determining Efficacy 7
1.4 ICH Quality Guidelines 11
1.5 Other Guidelines 14
1.6 Statistical Challenges in Drug Products Development and Manufacturing 17
1.7 Summary 18
References 19
2 Statistical Methods in Clinical Trials 22
Telba Irony, Caiyan Li and Phyllis Silverman
Synopsis 22
2.1 Introduction 22
2.1.1 Claims 23
2.1.2 Endpoints 23
2.1.3 Types of Study Designs and Controls 24
2.2 Hypothesis Testing, Significance Levels, p-values, Power and Sample Size 25
2.2.1 Hypothesis Testing 25
2.2.2 Statistical Errors, Significance Levels and p-values 25
2.2.3 Confidence Intervals 26
2.2.4 Statistical Power and Sample Size 27
2.3 Bias, Randomization and Blinding/Masking 29
2.3.1 Bias 29
2.3.2 Randomization 30
2.3.3 Blinding or Masking 31
2.4 Covariate Adjustment and Simpson’s Paradox 32
2.4.1 Simpson’s Paradox 32
2.4.2 Statistical Methods for Covariate Adjustment 34
2.5 Meta-analysis, Pooling and Interaction 35
2.5.1 Meta-analysis 35
2.5.2 Pooling and Interaction 37
2.6 Missing Data, Intent-to-treat and Other Analyses Cohorts 38
2.6.1 Missing Data 38
2.6.2 Intent-to-treat (ITT) and Other Analysis Cohorts 39
2.7 Multiplicity, Subgroup and Interim Analyses 40
2.7.1 Multiplicity 40
2.7.2 Subgroup Analyses 41
2.7.3 Interim Analyses 42
2.8 Survival Analyses 43
2.8.1 Estimating Survival Functions 44
2.8.2 Comparison of Survival Functions 45
2.9 Propensity Score 46
2.10 Bayesian Versus Frequentist Approaches to Clinical Trials 48
2.11 Adaptive Designs 50
2.11.1 Sequential Designs 51
2.12 Drugs Versus Devices 53
References 54
Further Reading 54
3 Pharmacometrics in Drug Development 56
Serge Guzy and Robert Bauer
Synopsis 56
3.1 Introduction 56
3.1.1 Pharmacometrics Definition 56
3.1.2 Dose-response Relationship 57
3.1.3 FDA Perspective of Pharmacometrics 57
3.1.4 When Should We Perform Pharmacometric Analysis? 58
3.1.5 Pharmacometric Software Tools 58
3.1.6 Organization of the Chapter 58
3.2 Pharmacometric Components 59
3.2.1 Pharmacokinetics (PK) 59
3.2.2 Pharmacodynamics (PD) 59
3.2.3 Disease Progression 59
3.2.4 Simulation of Clinical Trials 59
3.3 Pharmacokinetic/Pharmacodynamic Analysis 60
3.3.1 Compartmental Methods 60
3.4 Translating Dynamic Processes into a Mathematical Framework 61
3.5 Nonlinear Mixed-effect Modeling 63
3.6 Model Formulation and Derivation of the Log-likelihood 63
3.7 Review of the Most Important Pharmacometric Software Characteristics 65
3.7.1 NONMEM 65
3.7.2 PDx-MC-PEM 65
3.7.3 MONOLIX 66
3.7.4 WinBUGS 66
3.7.5 S-ADAPT 66
3.8 Maximum Likelihood Method of Population Analysis 67
3.9 Case Study: Population PK/PD Analysis in Multiple Sclerosis Patients 68
3.9.1 Study Design 68
3.9.2 Model Building 69
3.9.3 The PK Model 69
3.9.4 Platelet Modeling 69
3.9.5 T1 Lesions Model 69
3.10 Mathematical Description of the Dynamic Processes Characterizing the PK/Safety/Efficacy System 70
3.10.1 Optimization Procedure and Phase 2b Simulation Procedures 72
3.10.2 Clinical Simulation Results and Discussion 72
3.10.3 Calculation of the Cumulative Number of T1 Lesions and the Percentage MRI Improvement 73
3.10.4 Estimation of the Percentage of Patients to Reach Platelet Counts Below a Certain Threshold Value 73
3.10.5 Tentative Proposal for the Phase 2b Trial Design 74
3.11 Summary 75
3.11 References 76
4 Interactive Clinical Trial Design 78
Zvia Agur
Synopsis 78
4.1 Introduction 79
4.2 Development of the Virtual Patient Concept 80
4.2.1 The Basic Virtual Patient Model 80
4.3 Use of the Virtual Patient Concept to Predict Improved Drug Schedules 86
4.3.1 Modeling Vascular Tumor Growth 86
4.3.2 Synthetic Human Population (SHP) 91
4.4 The Interactive Clinical Trial Design (ICTD) Algorithm 94
4.4.1 Preclinical Phase: Constructing the PK/PD Module 94
4.4.2 Phase I: Finalizing and Validating the PK/PD Module 95
4.4.3 Interim Stage Between Phase I and Phase II: Intensive Simulations of Short-term Treatments 96
4.4.4 Phase II and Phase III: Focusing the Clinical Trials 96
4.4.5 Interactive Clinical Trial Design Method as Compared to
Adaptive Clinical Trial Design Methods 99
4.5 Summary 100
Acknowledgements 100
References 100
5 Stage-wise Clinical Trial Experiments in Phases I, II and III 103
Shelemyahu Zacks
Synopsis 103
5.1 Introduction 103
5.2 Phase I Clinical Trials 104
5.2.1 Up-and-down Adaptive Designs in Search of the MTD 105
5.2.2 The Continuous Reassessment Method 107
5.2.3 Efficient Dose Escalation Scheme With Overdose Control (EWOC) 109
5.3 Adaptive Methods for Phase II Trials 110
5.3.1 Individual Dosing 110
5.3.2 Termination of Phase II 111
5.4 Adaptive Methods for Phase III 112
5.4.1 Randomization in Clinical Trials 112
5.4.2 Adaptive Randomization Procedures 113
5.4.3 Group Sequential Methods: Testing Hypotheses 119
5.5 Summary 119
References 120
6 Risk Management in Drug Manufacturing and Healthcare 122
Ron S. Kenett
Synopsis 122
6.1 Introduction to Risks in Healthcare and Trends in Reporting Systems 122
6.2 Reporting Adverse Events 124
6.3 Risk Management and Optimizing Decisions With Data 126
6.3.1 Introduction to Risk Management 126
6.3.2 Bayesian Methods in Risk Management 128
6.3.3 Basics of Financial Engineering and Risk Management 129
6.3.4 Black Swans and the Taleb Quadrants 130
6.4 Decision Support Systems for Managing Patient Healthcare Risks 131
6.5 The Hemodialysis Case Study 137
6.6 Risk-based Quality Audits of Drug Manufacturing Facilities 142
6.6.1 Background on Facility Quality Audits 142
6.6.2 Risk Dimensions of Facilities Manufacturing Drug Products 143
6.6.3 The Site Risk Assessment Structure 144
6.7 Summary 152
References 152
7 The Twenty-first Century Challenges in Drug Development 155
Yafit Stark
Synopsis 155
7.1 The FDA’s Critical Path Initiative 155
7.2 Lessons From 60 Years of Pharmaceutical Innovation 156
7.2.1 New-drug Performance Statistics 156
7.2.2 Currently There are Many Players, but Few Winners 156
7.2.3 Time to Approval – Standard New Molecular Entities 157
7.3 The Challenges of Drug Development 158
7.3.1 Clinical Trials 158
7.3.2 The Critical-path Goals 159
7.3.3 Three Dimensions of the Critical Path 159
7.3.4 A New-product Development Toolkit 160
7.3.5 Towards a Better Safety Toolkit 160
7.3.6 Tools for Demonstrating Medical Utility 160
7.4 A New Era in Clinical Development 160
7.4.1 Advancing New Technologies in Clinical Development 161
7.4.2 Advancing New Clinical Trial Designs 161
7.4.3 Advancing Innovative Trial Designs 162
7.4.4 Implementing Pharmacogenomics (PGx) During All Stages of Clinical Development 162
7.5 The QbD and Clinical Aspects 163
7.5.1 Possible QbD Clinical Approach 164
7.5.2 Defining Clinical Design Space 164
7.5.3 Clinical Deliverables to QbD 164
7.5.4 Quality by Design in Clinical Development 165
References 166
Part Two STATISTICS IN OUTCOMES ANALYSIS
8 The Issue of Bias in Combined Modelling and Monitoring of Health Outcomes 169
Olivia A. J. Grigg
Synopsis 169
8.1 Introduction 170
8.1.1 From the Industrial Setting to the Health Setting: Forms of Bias and the Flexibility of Control Charts 170
8.1.2 Specific Types of Control Chart 171
8.2 Example I: Re-estimating an Infection Rate Following a Signal 172
8.2.1 Results From a Shewhart and an EWMA Chart 172
8.2.2 Results From a CUSUM, and General Concerns About Bias 173
8.2.3 More About the EWMA as Both a Chart and an Estimator 174
8.3 Example II: Correcting Estimates of Length-of-stay Measures to Protect against Bias Caused by Data Entry Errors 175
8.3.1 The Multivariate EWMA Chart 175
8.3.2 A Risk Model for Length of Stay Given Patient Age and Weight 176
8.3.3 Risk Adjustment 176
8.3.4 Results From a Risk-adjusted Multivariate EWMA Chart 177
8.3.5 Correcting for Bias in Estimation Through Regression 178
8.4 Discussion 182
References 182
9 Disease Mapping 185
Annibale Biggeri and Dolores Catelan
Synopsis 185
9.1 Introduction 186
9.2 Epidemiological Design Issues 186
9.3 Disease Tracking 187
9.4 Spatial Data 188
9.5 Maps 188
9.6 Statistical Models 191
9.7 Hierarchical Models for Disease Mapping 192
9.7.1 How to Choose Priors in Disease Mapping? 194
9.7.2 More on the BYM Model and the Clustering Term 195
9.7.3 Model Checking 200
9.8 Multivariate Disease Mapping 200
9.9 Special Issues 202
9.9.1 Gravitational Models 202
9.9.2 Wombling 202
9.9.3 Some Specific Statistical Modeling Examples 203
9.9.4 Ecological Bias 205
9.9.5 Area Profiling 207
9.10 Summary 210
References 210
10 Process Indicators and Outcome Measures in the Treatment of Acute Myocardial Infarction Patients 219
Alessandra Guglielmi, Francesca Ieva, Anna Maria Paganoni and Fabrizio Ruggeri
Synopsis 219
10.1 Introduction 220
10.2 A Semiparametric Bayesian Generalized Linear Mixed Model 222
10.3 Hospitals’ Clustering 223
10.4 Applications to AMI Patients 224
10.5 Summary 227
References 228
11 Meta-analysis 230
Eva Negri
Synopsis 230
11.1 Introduction 231
11.2 Formulation of the Research Question and Definition of Inclusion/Exclusion Criteria 232
11.3 Identification of Relevant Studies 233
11.4 Statistical Analysis 234
11.5 Extraction of Study-specific Information 234
11.6 Outcome Measures 235
11.6.1 Binary Outcome Measures 235
11.6.2 Continuous Outcome Measures 236
11.7 Estimation of the Pooled Effect 237
11.7.1 Fixed-effect Models 237
11.7.2 Random-effects Models 240
11.7.3 Random-effects vs. Fixed-effects Models 241
11.8 Exploring Heterogeneity 242
11.9 Other Statistical Issues 243
11.10 Forest Plots 243
11.11 Publication and Other Biases 245
11.12 Interpretation of Results and Report Writing 246
11.13 Summary 247
References 247
Part Three STATISTICAL PROCESS CONTROL IN HEALTHCARE
12 The Use of Control Charts in Healthcare 253
William H. Woodall, Benjamin M. Adams and James C. Benneyan
Synopsis 253
12.1 Introduction 253
12.2 Selection of a Control Chart 255
12.2.1 Basic Shewhart-type Charts 255
12.2.2 Use of CUSUM and EWMA Charts 257
12.2.3 Risk-adjusted Monitoring 259
12.3 Implementation Issues 261
12.3.1 Overall Process Improvement System 261
12.3.2 Sampling Issues 262
12.3.3 Violations of Assumptions 262
12.3.4 Measures of Control Chart Performance 263
12.4 Certification and Governmental Oversight Applications 263
12.5 Comparing the Performance of Healthcare Providers 264
12.6 Summary 265
Acknowledgements 265
References 265
13 Common Challenges and Pitfalls Using SPC in Healthcare 268
Victoria Jordan and James C. Benneyan
Synopsis 268
13.1 Introduction 268
13.2 Assuring Control Chart Performance 269
13.3 Cultural Challenges 270
13.3.1 Philosophical and Statistical Literacy 270
13.3.2 Acceptable Quality Levels 271
13.4 Implementation Challenges 272
13.4.1 Data Availability and Accuracy 272
13.4.2 Rational Subgroups 273
13.4.3 Specification Threshold Approaches 273
13.4.4 Establishing Versus Maintaining Stability 275
13.5 Technical Challenges 276
13.5.1 Common Errors 276
13.5.2 Subgroup Size Selection 278
13.5.3 Over-use of Supplementary Rules 279
13.5.4 g Charts 280
13.5.5 Misuse of Individuals Charts 281
13.5.6 Distributional Assumptions 282
13.6 Summary 284
References 285
14 Six Sigma in Healthcare 286
Shirley Y. Coleman
Synopsis 286
14.1 Introduction 287
14.2 Six Sigma Background 288
14.3 Development of Six Sigma in Healthcare 289
14.4 The Phases and Tools of Six Sigma 292
14.5 DMAIC Overview 292
14.5.1 Define 292
14.5.2 Measure 293
14.5.3 Analyse 295
14.5.4 Improve 296
14.5.5 Control 297
14.5.6 Transfer 298
14.6 Operational Issues of Six Sigma 298
14.6.1 Personnel 298
14.6.2 Project Selection 300
14.6.3 Training 301
14.6.4 Kaizen Workshops 301
14.6.5 Organisation of Training 302
14.7 The Way Forward for Six Sigma in Healthcare 303
14.7.1 Variations 303
14.7.2 Six Sigma and the Complementary Methodology of Lean Six Sigma 304
14.7.3 Implementation Issues 305
14.7.4 Implications of Six Sigma for Statisticians 306
14.8 Summary 307
References 307
15 Statistical Process Control in Clinical Medicine 309
Per Winkel and Nien Fan Zhang
Synopsis 309
15.1 Introduction 310
15.2 Methods 310
15.2.1 Control Charts 310
15.2.2 Measuring the Quality of a Process 311
15.2.3 Logistic Regression 311
15.2.4 Autocorrelation of Process Measurements 312
15.2.5 Simulation 312
15.3 Clinical Applications 313
15.3.1 Measures and Indicators of Quality of Healthcare 313
15.3.2 Applications of Control Charts 314
15.4 A Cautionary Note on the Risk-adjustment of Observational Data 324
15.5 Summary 328
Appendix A 328
15.A.1 The EWMA Chart 328
15.A.2 Logistic Regression 329
15.A.3 Autocovariance and Autocorrelation 330
Acknowledgements 330
References 330
Part Four APPLICATIONS TO HEALTHCARE POLICY AND IMPLEMENTATION
16 Modeling Kidney Allocation: A Data-driven Optimization Approach 335
Inbal Yahav
Synopsis 335
16.1 Introduction 335
16.1.1 Literature Review 338
16.2 Problem Description 340
16.2.1 Notation 340
16.2.2 Choosing Objectives 341
16.3 Proposed Real-time Dynamic Allocation Policy 342
16.3.1 Stochastic Optimization Formulation 342
16.3.2 Knowledge-based Real-time Allocation Policy 343
16.4 Analytical Framework 344
16.4.1 Data 344
16.4.2 Model Estimation 344
16.5 Model Deployment 345
16.5.1 Stochastic Optimization Analysis 346
16.5.2 Knowledge-based Real-time Policy 347
16.6 Summary 350
Acknowledgement 352
References 352
17 Statistical Issues in Vaccine Safety Evaluation 353
Patrick Musonda
Synopsis 353
17.1 Background 353
17.2 Motivation 354
17.3 The Self-controlled Case Series Model 354
17.4 Advantages and Limitations 357
17.5 Why Use the Self-controlled Case Series Method 358
17.6 Other Case-only Methods 358
17.7 Where the Self-controlled Case Series Method Has Been Used 359
17.8 Other Issues That were Explored in Improving the SCCM 360
17.9 Summary of the Chapter 362
References 362
18 Statistical Methods for Healthcare Economic Evaluation 365
Caterina Conigliani, Andrea Manca and Andrea Tancredi
Synopsis 365
18.1 Introduction 365
18.2 Statistical Analysis of Cost-effectiveness 366
18.2.1 Incremental Cost-effectiveness Plane, Incremental Cost-effectiveness Ratio and Incremental Net Benefit 366
18.2.2 The Cost-effectiveness Acceptability Curve 368
18.3 Inference for Cost-effectiveness Data From Clinical Trials 369
18.3.1 Bayesian Parametric Modelling 370
18.3.2 Semiparametric Modelling and Nonparametric Statistical Methods 373
18.3.3 Transformation of the Data 374
18.4 Complex Decision Analysis Models 375
18.4.1 Markov Models 376
18.5 Further Extensions 378
18.5.1 Probabilistic Sensitivity Analysis and Value of Information Analysis 379
18.5.2 The Role of Bayesian Evidence Synthesis 380
18.6 Summary 383
References 383
19 Costing and Performance in Healthcare Management 386
Rosanna Tarricone and Aleksandra Torbica
Synopsis 386
19.1 Introduction 387
19.2 Theoretical Approaches to Costing Healthcare Services: Opportunity Cost and Shadow Price 387
19.3 Costing Healthcare Services 388
19.3.1 Measuring Full Costs of Healthcare Services 389
19.3.2 Definition of the Cost Object (Output) 389
19.3.3 Classification of Cost Components (Direct vs. Non-direct Costs) 390
19.3.4 Selection of Allocation Methods 390
19.3.5 Calculation of Full Costs 392
19.4 Costing for Decision Making: Tariff Setting in Healthcare 392
19.4.1 General Features of Cost-based Pricing and Tariff Setting 393
19.4.2 Cost-based Tariff Setting in Practice: Prospective Payments System for Hospital Services Reimbursement 394
19.5 Costing, Tariffs and Performance Evaluation 395
19.5.1 Definition of Final Cost Object 396
19.5.2 Classification and Evaluation of Cost Components 396
19.5.3 Selection of Allocative Methods and Allocative Basis 397
19.5.4 Calculation of the Full Costs 397
19.5.5 Results 398
19.6 Discussion 400
19.7 Summary 402
References 403
Part Five APPLICATIONS TO HEALTHCARE MANAGEMENT
20 Statistical Issues in Healthcare Facilities Management 407
Daniel P. O’Neill and Anja Drescher
Synopsis 407
20.1 Introduction 407
20.2 Healthcare Facilities Management 409
20.2.1 Description 409
20.2.2 Relevant Data 410
20.3 Operating Expenses and the Cost Savings Opportunities Dilemma 412
20.4 The Case for Baselining 413
20.5 Facilities Capital . . . is it Really Necessary? 414
20.5.1 Facilities Capital Management 414
20.5.2 A Census of Opportunities 415
20.5.3 Prioritization and Efficiency Factors 416
20.5.4 Project Management 417
20.6 Defining Clean, Orderly and in Good Repair 418
20.6.1 Customer Focus 418
20.6.2 Metrics and Methods 419
20.7 A Potential Objective Solution 420
20.8 Summary 424
References 425
21 Simulation for Improving Healthcare Service Management 426
Anne Shade
Synopsis 426
21.1 Introduction 426
21.2 Talk-through and Walk-through Simulations 427
21.3 Spreadsheet Modelling 428
21.4 System Dynamics 429
21.5 Discrete Event Simulation 429
21.6 Creating a Discrete Event Simulation 431
21.7 Data Difficulties 432
21.8 Complex or Simple? 434
21.9 Design of Experiments for Validation, and for Testing Robustness 436
21.10 Other Issues 438
21.11 Case Study No. 1: Simulation for Capacity Planning 439
21.12 Case Study No. 2: Screening for Vascular Disease 440
21.13 Case Study No. 3: Meeting Waiting Time Targets in Orthopaedic Care 441
21.14 Case Study No. 4: Bed Capacity Implications Model (BECIM) 442
21.15 Summary 443
References 444
22 Statistical Issues in Insurance/payor Processes 445
Melissa Popkoski
Synopsis 445
22.1 Introduction 445
22.2 Prescription Drug Claim Processing and Payment 446
22.2.1 General Process: High-level Outline 446
22.2.2 Prescription Drug Plan Part D Claims Payment Process 447
22.3 Case Study: Maximizing Part D Prescription Drug Claim Reimbursement 450
22.4 Looking Ahead 453
22.5 Summary 454
Reference 455
23 Quality of Electronic Medical Records 456
Dario Gregori and Paola Berchialla
Synopsis 456
23.1 Introduction 456
23.2 Quality of Electronic Data Collections 459
23.2.1 Administrative Databases 461
23.2.2 Health Surveys 461
23.2.3 Patient Medical Records 462
23.2.4 Clinical Trials 462
23.2.5 Clinical Epidemiology Studies 462
23.3 Data Quality Issues in Electronic Medical Records 462
23.4 Procedure to Enhance Data Quality 464
23.4.1 Clinical Vocabularies 466
23.4.2 Ontologies 466
23.4.3 Potential Technical Challenges for EMR Data Quality 467
23.4.4 Data Warehousing 469
23.5 Form Design and On-entry Procedures 469
23.5.1 Data Capture 470
23.5.2 Data Input 470
23.5.3 Error Prevention 471
23.5.4 Physician-entered Data 471
23.6 Quality of Data Evaluation 472
23.7 Summary 475
References 475
Index 481
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