Statistical Methods in Healthcare

ISBN-13: 9780470670156 / Angielski / Twarda / 2012 / 520 str.

Frederick Faltin

Opis

This title provides a comprehensive, in-depth treatment of statistical methods in healthcare and offers a broad coverage of standards and established methods through leading edge techniques.

Spis treści

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

Blurb

In recent years the number of innovative medicinal products and devices submitted and approved by regulatory bodies has declined dramatically.  The medical product development process is no longer able to keep pace with increasing technologies, science and innovations and the goal is to develop new scientific and technical tools and to make product development processes more efficient and effective. Statistical Methods in Healthcare focuses on the application of statistical methodologies to evaluate promising alternatives and to optimize the performance and demonstrate the effectiveness of those that warrant pursuit is critical to success. Statistical methods used in planning, delivering and monitoring health care, as well as selected statistical aspects of the development and/or production of pharmaceuticals and medical devices are also addressed.

With a focus on finding solutions to these challenges, this book:

• Provides a comprehensive, in–depth treatment of statistical methods in healthcare, along with a reference source for practitioners and specialists in health care and drug development.
• Offers a broad coverage of standards and established methods through leading edge techniques.
• Uses an integrated, case–study based approach, with focus on applications.
• Looks at the use of analytical and monitoring schemes to evaluate therapeutic performance.
• Features the application of modern quality management systems to clinical practice, and to pharmaceutical development and production processes.
• Addresses the use of modern Statistical methods such as Adaptive Design, Seamless Design, Data Mining, Bayesian networks and Bootstrapping that can be applied to support the challenging new vision.

Practitioners in healthcare–related professions, ranging from clinical trials to care delivery to medical device design, as well as statistical researchers in the field, will benefit from this book.